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Joseph Huang is a Partner at the global venture capital firm Headline based in the Asia office. He focuses on Consumer Internet, Fintech, and SaaS.

Joseph Huang | Headline

Our Guiding Principles

Joseph Huang is a Partner at the global venture capital firm Headline based in the Asia office. He focuses on Consumer Internet, Fintech, and SaaS. He believes the most important trait in an investor is the ability to continually looking for new angles.

Joseph Huang

Observability has always been about answering a simple question: what’s happening inside my system? But in today’s AI-native world — where applications multiply daily, services are deeply distributed, and AI agents are writing and shipping code alongside humans — that question isn’t enough. Systems don’t just need to be monitored. They need to explain themselves.

This week, Honeycomb launched Honeycomb Intelligence, an AI-native suite designed for exactly this future. With MCP Server, Canvas, and Anomaly Detection, Honeycomb is reimagining observability as something that doesn’t just live in dashboards and alerts, but directly inside the developer workflow with fast and interactive feedback loops.

What it is: Honeycomb’s MCP Server connects AI coding assistants directly to telemetry data in Honeycomb. It’s now generally available with support for BubbleUp, heatmaps, and histograms.

How it works: At a basic level, MCP (Model Context Protocol) servers let AI assistants translate natural-language questions into data-backed queries. That’s table stakes. But Honeycomb has gone further: they’ve engineered an MCP that actually understands how to deliver the right context without overwhelming the model. Instead of feeding an AI agent an ocean of noisy telemetry, the Honeycomb MCP filters and structures the data so the agent sees exactly what it needs to reason effectively. This minimizes hallucinations, speeds up investigations, and keeps the AI focused on what matters.

Why it matters: Many vendors could ship a standard MCP server and call it a day. Honeycomb’s version is more compelling because it’s tuned for observability’s hardest problems: scale, cardinality, and context. It doesn’t just let an AI query your system — it ensures those queries are meaningful, performant, and actionable. That makes AI assistants not just functional, but trustworthy. It positions Honeycomb’s MCP as the connective tissue between development environments and production insight, where agents and humans can reliably debug issues without drowning in irrelevant data.


Canvas: A New Interface for Understanding Systems

What it is: Canvas is an AI-guided workspace that turns natural-language questions into multi-step investigations.

How it works: Engineers describe an issue — “Why did checkout latency spike yesterday?” — and Canvas automatically sequences relevant queries, surfaces charts, and highlights anomalies. Each step is shown visually (heatmaps, histograms, traces), and users can guide or redirect the AI. Canvas sessions can be shared with teammates or exported to Slack, where others can interact with the results directly.

Why it matters: The real power of Honeycomb’s approach to observability — unlike the traditional three-pillars model of logs, metrics, and traces — is its ability to give humans and AI access to rich, high-cardinality event data to uncover unanticipated connections and correlations. AI thrives on speed and context, both of which are enabled by Canvas. Instead of requiring query expertise or intimate system knowledge, engineers can simply ask questions in natural language and let the AI surface relationships that would otherwise stay hidden.

This transforms Canvas from a convenience into a force multiplier. It lowers the barrier for anyone on the team to uncover deep insights, it turns debugging into a collaborative process, and it unlocks the full potential of Honeycomb’s event-driven architecture by making complex system behaviors explainable in real time. In short, Canvas makes the strengths of Honeycomb’s data model broadly usable.

What it is: An ML-driven engine that learns normal patterns across your services and surfaces deviations before they become incidents.

How it works: Honeycomb continuously monitors telemetry such as request rates, error counts, and latency, building baselines for each service. When actual behavior deviates significantly, the system raises an anomaly alert. Each alert includes context and integrates directly with BubbleUp and Canvas, so engineers can immediately see what’s different and why.

Why it matters: With hundreds of microservices and an explosion of telemetry, no team can hand-craft alerts for everything. Static thresholds can’t keep up. Anomaly Detection shifts observability from reactive firefighting to proactive protection. It reduces noise, spots regressions earlier, and ensures engineers aren’t blindsided by unknown unknowns.


The Bigger Picture: Why These Fit Together

Each of these releases is powerful on its own. But the real significance lies in how they work together as a unified vision:

MCP Server ensures observability isn’t separate from development. It brings system insight into the IDE, and crucially, into the AI agents that are increasingly part of how code gets written. And unlike a basic MCP, Honeycomb’s implementation solves the hardest part — delivering the right amount of context so investigations are accurate, fast, and free from noise. Observability starts at the moment of creation, not after an incident.

Canvas ensures those insights are usable and collaborative. It leverages Honeycomb’s unique strength — rich event-based telemetry capable of surfacing unanticipated connections — and makes it accessible through natural language and AI-guided investigations. Instead of requiring specialized expertise, Canvas turns the system into something that can explain itself, and makes those explanations easy to share.

Anomaly Detection ensures no critical signals are missed. It provides the early warnings that feed into both MCP and Canvas, so engineers aren’t just asking questions after a failure. They’re investigating patterns proactively, guided by AI surfacing what’s abnormal.

Just as important is the foundation Honeycomb already built: a high-performance event-based observability platform capable of collecting massive volumes of high-cardinality data, correlating across traces, metrics, and logs, and delivering sub-second queries at scale. This rich telemetry and contextual depth is what makes Intelligence possible. Without clean, detailed event data and Honeycomb’s unique architecture, these AI-powered features would lack the signal to deliver useful insights.

Together, Honeycomb’s core observability engine plus Intelligence layer create a connected feedback loop: anomalies detected automatically, surfaced into developer workflows, investigated collaboratively, and continuously learned from. Code, telemetry, AI, and engineers form a closed loop, shortening the path from issue to insight and from insight to resolution.

The real potential of Honeycomb Intelligence isn’t in the individual features, it’s in what they unlock together. By tying real-time data, AI-guided investigation, and proactive detection into a single loop, Honeycomb is building toward something bigger: a world where systems explain themselves.

Imagine a future where every feature shipped comes with an automatic narrative of how it affected performance, where anomalies aren’t just flagged but contextualized in plain language, and where institutional knowledge compounds because every investigation becomes part of a living memory. Honeycomb is creating the conditions for engineering teams to move faster with more confidence, while learning from their systems continuously.

That’s the trajectory these releases point toward: observability as a compounding advantage, not just a safety net. Honeycomb isn’t just keeping pace with complexity — it’s redefining what it means to understand and trust the software you build.

Honeycomb Intelligence: The Future of Observability in an AI-Native World

Granola. Spotify. ChatGPT. The Row. Waymo. Erewhon. Skims. 

Some companies aren’t just better; they’re unrepeatable. They don’t fit a mold; they break the  mold. These companies are “N of 1s”: rule-defying outliers that succeed not in spite of their singularity, but because of it.

N of 1 companies emerge at the intersection of the right product and the right cultural moment, but what makes them endure runs deeper. It's a structural, emotional, or narrative edge that resists commoditization: something competitors can see but can't replicate.

These consumer winners don’t live by formulas, frameworks, or spreadsheets. While SaaS companies might thrive by optimizing conversion rates and standardizing customer journeys, consumer category-creators are always rule-breakers, outliers in a market full of safe bets and copycats. Brilliance here isn’t found in a backtested model; it’s found in the exception to the rule.


Think: a magic notetaking tool that makes effortless a laborious-yet-essential task. A cult-like grocer daring to upend margin logic. A new vehicle that feels straight out of a sci-fi movie. A sleek ring that turns invisible health data into obsessive self-optimization.


To invest in consumer companies, you must hunt for rare magic: for companies that redefine entire categories through sheer force of vision. These aren't just "better" products or "prettier" packaging. These are companies whose rule-breaking charm and unreplicable user experience truly separate them from seas of competitors building the same thing.

The market talks about "taste" as some vague superpower. At Headline, we focus on pattern-matching what makes N of 1 companies that consumers love, so we can recognize the next one before it becomes obvious.

The challenge of consumer investing is that by the time everyone can see the magic, it's too late. The best N of 1 companies often look inexplicable in their early days: weird, niche, or serving needs that don't yet feel universal. While there may never be a single formula, we wanted to unpack the elements we've noticed across the best ones that consistently separate the truly singular companies from the rest..

There are four clear categories of “n of 1” consumer companies, which are designated for their unique superpowers. 

Some N of 1 companies win because the product itself feels like a breakthrough. They introduce a form factor or functionality that permanently shifts consumer expectations. Think of OŪRA, which turned a simple ring into a biometric tracker with cult adoption, or Granola, which eliminated the burden of note-taking entirely, delivering summaries so relevant it's as if the AI knew exactly what mattered from your conversation. Once you’ve experienced that step-change, everything else feels like a step backwards.


Others build lasting advantage through mythology and identity. A24 created more than a film studio; it shaped a creative new worldview that fans proudly signal. Strava has become less of a run tracker and more of a social network, with athletes obsessively checking each other’s routes and performances. On TikTok, The RealReal fanatics trade tips for spotting mispriced items and post “graveyards” of pieces they regret not buying, turning secondhand luxury into a shared cultural game. Even Duolingo’s green owl has turned into a beloved character online, its memes extending the brand far beyond language learning. When a company becomes shorthand for belonging, its community naturally takes on the work of distribution.


Every consumer company needs strong distribution to survive. The difference for N of 1s is that distribution is not just a necessity but the unfair edge that defines them. Skims mastered this by combining celebrity influence with algorithm-driven personalization, allowing the brand to live everywhere its customers already spend their attention. Netflix achieved the same by embedding itself into daily routines, using recommendations to make discovery feel effortless. For N of 1s, distribution evolves from survival mechanism to the very engine of dominance.

Finally, certain N of 1s win by reshaping the economics of their category. Eight Sleep introduced subscription services to the category of sleep itself, turning what was once a one-time mattress purchase into ongoing sleep optimization. The Row has done the same in fashion, where $700 flip-flops can sell out because the brand’s stark minimalism makes dupes feel hollow by comparison. Their pricing power and consumers’ willingness to opt in reflect an aura that competitors cannot replicate. Whether through the invention of new revenue streams or the cultivation of brand-driven pricing power, these companies prove that price itself can be a moat.

N of 1 Companies and the Future of Consumer Culture

As we look ahead, we think the next wave of N of 1 companies will create cultural shifts rather than follow them. These founders will invent entirely new categories that feel inevitable only in hindsight.

In a world of endless competition and fleeting attention, “N of 1” consumer companies stand apart by building structural advantages, cultivating cult-like followings, and delivering products so compelling they become indispensable. These companies do more than win categories. They introduce entirely new ways of living in and seeing the world.

The landscape of consumer venture capital has evolved significantly over the past two years, with some investors pulling back while others have doubled down on their commitment to the space. As the market shifts, it has become increasingly difficult to identify which VCs remain bullish about investing in consumer-focused brands and startups. The uncertainty has led many founders to question where to turn for funding and which investors are still actively pursuing opportunities in consumer markets.

To address this, we’ve compiled a comprehensive list of early-stage consumer VCs who are still investing in areas like consumer technology, consumer marketplaces, prosumer software, consumer subscription, and CPG / eComm. This curated resource is designed to help founders navigate the current venture landscape by connecting them with investors who want to fuel the next wave of consumer innovation. Headline maintains its strong conviction and enthusiasm for the opportunities in consumer. Whether you're launching a new app or building a consumer brand, this list can serve as a guide to finding the right backers.

Did we miss someone? Nominate a venture firm that actively invests in pre-seed or seed consumer startups here and we’ll update our list accordingly.

Want access to our full list for easy use? Happy to help. Just click here.

Zoe Bhargava (Harvard MBA ‘25) was also a collaborator on this report.

The Pre-Seed and Seed Consumer VC Market Map

AI has changed how companies are built and evaluated. The old SaaS fundraising frameworks don’t cut it anymore: AI has brought that era of software to a close. Here’s a new playbook, with slides to copy and make your own.

Headline Series A Pitch Deck Template for AI Apps

Never in economic history have we witnessed a technology adoption curve as steep as AI’s. Smartphones took a decade to reshape consumer behavior. Social networks took years to reach critical mass. Generative AI, by contrast, has exploded into daily workflows in less than 24 months.

This is not just a matter of hype. It is measurable. Google’s own numbers tell the story: between April 2024 and April 2025, its token consumption increased 50x, reaching 480 trillion tokens per month. That is a proxy for real economic activity – every prompt, every query, every agent is another token consumed.

The leaders of this new economy are growing at a pace unseen in enterprise software. In the past six months alone, Anthropic has quadrupled its ARR to reach $4B. In the meantime, OpenAI, already larger, still managed to triple ARR to $12B.

What makes this wave unique is not only the size of the frontrunners but also the breadth of the ecosystem. Companies that did not exist five years ago now generate over $100M ARR: Mistral, Harvey, Abridge, Mercor, Lovable, Synthesia, Helsing, and more. In SaaS history, the journey to $100M ARR used to take a decade. AI-native startups are hitting the milestone in less than half the time.

The aggregate impact is already visible: AI generated $280B in revenue in 2024, a figure projected to reach nearly $2T by 2030.

Why has adoption been so fast? We see two reinforcing dynamics:

Technology meets ROI – As soon as technical advances become reliable enough for practical use cases, adoption spikes.

Talent shortages – In industries where skilled workers are scarce, AI tools are not optional; they are competitive advantages.

The result: penetration rates climb quickly, market leaders emerge faster, and growth compounds.

This dynamic has unfolded in identifiable “batches” of adoption:

Batch 1: LLMs dealing with text and voice knowledge tasks have reshaped day-to-day jobs for many knowledge workers in different categories : Legal, Coding, Healthcare, Customer Support, Cyber. 

Batch 2: Image processing by AI through VLMs have created a new set of use cases impacting other categories such as Security, Marketing or Defense.

Batch 3: With the agentic era, AI is starting to tackle increasingly complex workflows which enables enterprise integration in sectors like Finance, HR or Productivity.



Next batch (our forward bet): Live decision-making and real-world support will have a deep impact at a broader scale. Think about AI-embedded robots already being built for Logistics use cases for instance. Or real-time decision making to trade on financial markets or balance the electric grid.

Each batch has produced category-defining companies. Each has turned AI into a tool that is augmenting humans, especially those in under-staffed sectors, rather than stealing people’s jobs.

The US currently leads the AI race. They have nearly 70% of global compute power and the first AI behemoths originally came from the US. But Europe does have unique strengths that position it to produce the next global winners:

Market Depth: 742 million people, nearly double the U.S. population.

Talent Density: Home to world-class universities, research centers, and technical talent.

Buyer Preference: European enterprises often favor local vendors for reasons of sovereignty, data control, and trust.

Europe is already proving itself. Companies like Mistral (France), ElevenLabs (Poland/UK), Helsing (Germany), Lovable (SWE), Synthesia (UK), and others have broken through.

At Headline, we believe the next generation of global leaders will also emerge here. That is why we have curated the AI Europe 100 – the most promising AI-native startups across all the categories above.

The story of AI adoption is not a story of substitution. It is a story of amplification. Where human capacity is limited, AI multiplies it. That is why industries constrained by talent shortages are adopting AI fastest.

What is remarkable is that this amplification is happening at scale, across geographies and industries, and at a speed never seen before. From $280B in revenue today to nearly $2T within five years, AI is reshaping the world economy in real time.

The question is no longer if. It is where the winners will be built.

Here is the full presentation from Headline's AI event in Paris, presenting the AI Europe 100.

Discover AI Europe 100, Headline’s curated list of the 100 most promising AI startups shaping Europe's future, unveiled at the Musée de Montmartre in Paris on September 4, 2025, spotlighting the continent’s fastest-growing innovators in a technology era defined by explosive change.

AI Europe 100 | Headline

When you look at the healthcare landscape—especially for rare diseases—it’s easy to feel a sense of urgency and frustration. Over 30 million Americans live with a rare disease, navigating a maze of specialists, endless paperwork, and a constant search for answers. Shockingly and sadly, around 95% of these conditions have no approved treatments. 

That’s the world Citizen Health set out to change. 

Today, Headline joins a $30 million Series A round alongside 8VC and prior investors Transformation Capital, Wavemaker 360, and the Chan Zuckerberg Initiative (CZI)—all of whom bring deep expertise and networks in healthcare and technology.

At its core, Citizen Health is building a beautiful consumer platform that brings together every piece of a patient’s health journey—clinical data, genetic information, imaging, patient-reported outcomes, and claims. They’re then using AI to make sense of all this data, turning a pile of disconnected records into a clear, actionable story for each patient. For families, this means having a digital interface where they can actually find and use their medical information, get matched to clinical trials, make sense of medications and treatments, and connect with others who understand what they’re going through. 

For healthy consumers, this interface may not sound very critical but for rare disease patients and their families, these records are typically inaccessible or scattered across numerous portals, screenshots, and printouts and can be the difference of understanding whether they qualify for a new treatment or will have an adverse reaction to a drug. On Citizen, all records can be transferred to a healthcare provider with the click of a button.

But the impact goes way beyond individual patients. By partnering with patient advocacy groups (PAGs), Citizen Health is also solving a massive data fragmentation problem. There are about 1,200 PAGs in the U.S., each with its own registry and approach, which unintentionally keeps valuable data siloed. Citizen Health’s platform acts as a universal connector, aggregating data across all these groups, making it far more useful—not just for patients, but for the researchers and companies racing to develop new therapies.

Citizen Health was spun out of Invitae, bringing a powerhouse team with deep roots in both technology and patient advocacy. CEO Farid Vij, who was previously COO at Ciitizen (acquired by Invitae for $325M), and Chief Business Officer Nasha Fitter, a widely respected rare disease advocate, helped Citizen Health to build early trust with patient advocacy groups and families. From a business perspective, Citizen’s approach is already paying off. Since its seed round, Citizen Health has brought on dozens of PAGs and thousands of patients. Nearly half of their customers have expanded their business with Citizen Health, either by adding new disease cohorts or new use cases. Big names like AstraZeneca, UCB, and Ionis are early customers.

In addition to the impact on patient lives, we are very excited about the flywheel that Citizen Health is starting to see. Historically, biotech and pharma companies have struggled to develop treatments for rare diseases because of the lack of available patient data. As more PAGs and patients join the platform, their data becomes richer and more valuable.  This attracts more enterprise customers and drives more revenue, enabling Citizen Health to invest further in their technology and outreach. This, in turn, draws in even more PAGs and patients, creating a virtuous cycle of growth and impact. 

Citizen Health’s growth has been organic, driven predominantly through word-of-mouth due to Nasha’s advocacy for FOXG1 and credibility in the rare disease space. As AI continues to drive down the data structuring costs, their B2B2C model keeps customer acquisition costs low. 

Unique in healthcare, the consensus among patients, advocacy leaders, and pharma clients was glowing and consistent during our diligence process. PAG leaders raved about how Citizen finally gave their communities a way to organize and use their health data. Pharma clients highlighted the unique value of Citizen’s data across their efforts in new drug development, trials, and even patient recruitment. And everyone—from customers to competitors—spoke highly of Farid, Nasha, and the entire Citizen Health team.

Through their own experiences, Citizen deeply understood that rare disease patients never have beautiful products built for them – constantly told that the market opportunity isn’t great enough for great innovation. We admire that the team sees patients as consumers, who deserve intuitive and modern interfaces to navigate a notoriously complex system. We hope that in the future, when anyone is sick, the first question their doctor asks is, “Are you on Citizen?”

Citizen Health is the perfect combination of the right team + right technology + right approach + right time, colliding to solve one of healthcare’s most challenging problems. Citizen Health isn’t just building a business, but creating a platform that could redefine how we think about managing rare diseases–and beyond.

For more information, visit citizen.health.

A Consumer Platform to Drive Better Rare Disease Prognoses for One and All; Our Investment in Citizen Health’s Series A

Discover why Headline backs Citizen Health, an AI-powered consumer platform that unifies clinical, genetic, imaging, and patient-reported data to help rare disease patients access treatments, clinical trials, and broader care—while solving fragmented data challenges across patient advocacy groups.

A Consumer Platform to Improve Rare Disease Outcomes | Headline Investment in Citizen Health Series A

In a new world of efficient execution fueled by AI, the push to operate leanly applies not only to headcount and applications but also to infrastructure software. The effort to rationalize spend has emerged as one of the most defining trends that we see across the infra stack today—second only to AI adoption and integration. Such a mindset reflects a clear shift from the “growth at all costs” philosophy of recent years. With budgets tightening and AI initiatives taking priority, buyers are increasingly focused on simplicity and cost control.

Part of this movement seems to stem from growing frustration with pre-COVID breakout vendors like Fivetran, Snowflake, Splunk, Auth0, Datadog, Confluent, and Snyk—many of which have since gone public, been acquired, or raised at premium late-stage valuations. As these companies shift focus toward their largest enterprise customers and profitability, many have raised prices while simultaneously slowing product velocity. Buyers, in turn, are pushing back against their high spend on these platforms, which is becoming increasingly hard to justify.

There is now a real opening for challengers offering simpler, leaner, and more cost-aligned alternatives. The 10 trends below explore where we see the infrastructure software market headed.

1. Market Preference Is Shifting Back to Best-of-Suite Platforms

Buyer sentiment is shifting back toward best-of-suite solutions, as teams grapple with tool sprawl and vendor fatigue following the era of best-of-breed adoption. The trend is especially evident in security, where Gartner found that 75% of organizations are actively pursuing vendor consolidation. Major M&A activity reflects this shift—two of the largest deals of 2025 are security consolidation plays: Google’s $32B acquisition of Wiz and Palo Alto’s $25B acquisition of CyberArk.

The same dynamic is playing out among startups, where several challengers are seeing early success by centering their value proposition around a best-of-suite approach.

Networking: NetBox Labs — Composable platform for managing network infrastructure, including operations, observability, automation, and security

Cybersecurity: Aikido Security — Comprehensive security platform spanning AppSec and cloud security

DevOps/SRE: Incident — All-in-one incident management platform covering alerting and incident response

2. Transparent Pricing Is Becoming a Differentiator

As incumbent pricing grows increasingly opaque, buyers are migrating to challengers with clearer, more predictable pricing models. In many cases, renewal shock—driven by quietly compounding usage or gated features—pushes teams to reevaluate long-standing vendors.

Observability: Datadog → Honeycomb — Honeycomb prices based on data ingest—but in atomic units which can contain an effectively unlimited number of key/value pairs (sometimes called "custom metrics") tied to a given timestamp. This allows Honeycomb pricing to be more predictable, as costs are simply based on request volumes rather than a mix (or layering) of per-host, per-product, or per-attribute costs.  

ETL/ELT: Fivetran → Estuary — Fivetran charges based on Monthly Active Rows (MAR)—the number of rows inserted, updated, or deleted each month—which can spike unpredictably in high-churn datasets. Estuary avoids this with transparent pricing based on data volume and connector count.

AppSec: Snyk → Aikido Security — Aikido charges a flat per-user rate that covers its full suite of modules across AppSec and cloud security—avoiding the per-seat plus per-feature escalation common with Snyk's licensing model.

Auth: Auth0 → Clerk — Clerk’s pay-as-you-grow model avoids Auth0’s MAU caps and tiered gating, keeping pricing predictable while advanced features remain add-ons rather than Enterprise-only.

Business Intelligence: Looker → Omni — Omni offers a single, predictable monthly rate with no hidden fees or per-role pricing. Looker, in contrast, frequently charges separately for dev, standard, and viewer licenses and ties costs to compute usage and feature access—resulting in higher and less predictable bills.

3. The Modern Data Stack Is Converging on a Leaner, Modular Core

As complexity and cost have accumulated in the modern data stack, many teams are converging on simpler, more modular foundations. Rather than over-centralizing around warehouses or over-engineering pipelines, teams are relying on relational databases and object storage to do more—each extended just enough to support evolving needs without bloating the stack.

Relational databases are becoming increasingly versatile. New vendors and extensions are enabling teams to extend their existing databases to support functionality that has historically required multiple systems. For example, PuppyGraph, pgvector, and ParadeDB minimize the need to run separate graph, vector, and search databases, respectively. Reducing the number of systems that teams need to purchase, operate, sync, monitor, and secure meaningfully lowers administrative overhead, cost, operational burden, potential downtime, and the surface area for security breaches. Postgres’s rich extension ecosystem has made it a prime beneficiary of this shift, fueling its continued rise in popularity.

Object storage is having a similar moment, becoming the foundation for a growing range of workloads in analytics, search, and data processing, where cost, scalability, simplicity, and durability take precedence over fully optimized performance. In analytics, engines like Trino and even ClickHouse can query object stores directly using open table formats such as Apache Iceberg and Delta Lake. In search, AWS recently introduced S3 Vectors as the first cloud object store with native vector storage and querying. turbopuffer extends this approach by enabling both vector and full-text search on object storage, supported by a caching layer to boost performance. Further upstream, Bauplan aligns with the trend by offering serverless abstractions for building data pipelines, often for AI/ML use cases, directly on top of object storage.

Together, these trends reflect a broader move toward leaner infrastructure: fewer systems to manage, fewer pipelines to maintain, and more leverage from modular, general-purpose, and cost-effective primitives that can flex to support modern operational, analytical, and AI workloads.

4. Cost Optimization Platforms Are Emerging Across the Stack

As infrastructure costs become a board-level concern, a new class of tooling is emerging to reduce spend across compute, storage, and software. While Kubernetes and cloud billing were early focus areas, nearly every major cost center now has dedicated solutions, including spaces like data warehousing and business intelligence. Some tools, such as Pump, take a fintech-style approach, leveraging group buying across customers to obtain volume discounts and unlock the benefits of reserved instances. Others, such as Espresso AI, focus on optimizing utilization and footprint—identifying overprovisioned workloads, inefficient queries, or unused assets to reduce waste at the source.

Cloud Billing: Pump — Aggregates usage, unlocks preferred pricing on reserved instances, and simplifies cloud cost visibility

Data Warehouses: Espresso AI — Surfaces inefficient queries, redundant schedules, and unused tables to shrink compute/storage spend

BI Tools: Datalogz — Detects stale dashboards, orphaned models, and unused reports to reduce unnecessary BI layer costs

5. A New Class of Vendors Is Winning on Cost-Aware Design

A new class of infrastructure vendors is emerging—distinct from traditional cost optimization platforms—that targets high-cost line items like CI compute and SIEM. Rather than positioning solely as cost cutters, these players offer optimized, drop-in components that improve performance and efficiency while reducing compute and storage overhead as a natural byproduct. In many cases, they are replacing brute-force, overprovisioned systems with leaner, AI-native primitives that become embedded parts of the modern stack. Cribl pioneered this positioning, and a new wave of entrants is now applying the same playbook across the infrastructure stack, often with AI-native positioning.

CI Compute: Namespace — Provides faster, lower-cost CI runners—combining proprietary hardware with intelligent caching to serve as a drop-in replacement for managed runners across platforms like GitHub Actions

Security Data: Realm Security — Lowers SIEM costs (e.g., from Splunk, Sumo Logic) by acting as an upstream telemetry broker—intelligently filtering and routing high-volume, low-signal data to cheaper destinations

6. Challengers Are Capturing the Underserved Mid-Market

While much of the narrative discussed in this piece thus far centers on migration from legacy platforms, a parallel trend is unfolding: emerging vendors are capturing greenfield adoption by targeting the historically underserved mid-market. Incumbent solutions are often prohibitively expensive, require significant customization, and demand heavy implementation and maintenance resources—creating friction for leaner teams with constrained budgets. In contrast, new entrants are prioritizing simplicity, accessibility, and rapid time-to-value, delivering solutions that "just work" for mid-market buyers without the overhead of enterprise tooling. As part of this shift, cloud-native vendors are also introducing low-friction, modern alternatives that replace the complexity of clunky on-prem incumbents.

Data Modeling: SqlDBM — A cloud-native alternative to Erwin

Cybersecurity: Aikido — For teams where managing the Snyk + Semgrep + Wiz stack is excessive

Artifact Repositories: Cloudsmith – A cloud-native alternative to JFrog Artifactory

7. AI-Native Service Delivery Is Transforming Outsourcing Models

Most companies—especially outside the Global 2000—lack a compelling reason to manage IT, security, or QA functions internally. While outsourcing has long been an option, AI is materially shifting the cost–quality frontier. Service providers can now deliver faster, more consistent, and more scalable outcomes at equal or lower price points. As a result, we expect a growing portion of these functions to be externalized over time. This shift is playing out through both AI-native service providers that deliver outcomes directly and AI-native software platforms that enable existing providers to automate more and operate at higher margins.

8. Open Source Alternatives Are Gaining Favor

Open source tools offer a practical alternative for teams with the capability to self-manage infrastructure. By avoiding the high costs of managed hosting and usage-based pricing, OSS provides greater cost control and data ownership while reducing vendor lock-in. As infrastructure costs rise and scrutiny around SaaS sprawl and data privacy intensifies, more teams with in-house expertise are expected to adopt open source as a preferred path. For example, as Postman has matured and raised pricing, many companies have found it difficult to justify the spend versus emerging OSS alternatives like Bruno. Even in more frontier areas, OpenAI’s release of GPT OSS—an open-weight model suite—signals the growing importance of openness in AI.

Observability: Datadog → Prometheus + Grafana

9. Vendor-Native Tooling Is Quietly Becoming the Default Path

In parallel with the shift back toward "best of suite," we are seeing increasing adoption of vendor-native tooling—particularly from hyperscalers. In security, the rapid growth of Google SecOps and Microsoft Defender underscores how bundling continues to gain traction. With executive teams under pressure to rein in budgets, many are steering functional groups toward platform-native solutions, especially when they have pre-committed platform spend. As owners of the underlying infrastructure, hyperscalers can squarely outcompete other vendors on cost

A similar dynamic is unfolding in AI, where OpenAI and Anthropic are actively moving up the value chain into the application layer. Anthropic’s Claude Code and OpenAI’s Codex are examples of this shift in software development, while OpenAI’s investment in Endex signals broader ambitions to expand into verticals such as financial services. Just as hyperscalers benefit from owning infrastructure, OpenAI and Anthropic enjoy a strategic advantage by owning the underlying models.

Security: Choosing Microsoft Sentinel, Google Chronicle over Splunk

DevOps: Choosing AWS CloudFormation over Terraform

10. There Is Still Appetite to Pay for Clean Abstractions of Hard Problems

Despite tighter budgets, teams remain willing to pay for clean, reliable abstractions of foundational but operationally complex problems—especially those at the intersection of application logic and infrastructure complexity. In many cases, the cost of replicating these capabilities internally exceeds the price of adoption when accounting for headcount, opportunity cost, and ongoing maintenance. Even in lean environments, the tradeoff often favors outsourcing over in-house build-and-maintain efforts. For example, replicating Recall’s functionality in-house could require a team of 5–6 engineers working for several months, followed by ongoing operational overhead—making its API abstraction a far more cost-effective path for teams looking to move quickly and leanly.

This dynamic—coupled with the rapid growth of agentic applications—is powering the rise of a new category of API-based, AI-native abstractions purpose-built to support these emerging systems. Just as Stripe abstracted payments and Twilio simplified messaging, platforms like Composio, Browserbase, Daytona, Tavily, and Zep offer simple, clean interfaces for handling auth, integrations, runtime environments, web search, and context management. These abstractions are becoming the default path for teams looking to offload complexity, maintain development velocity, and build reliable, production-grade agent workflows without reinventing core infrastructure.

The infrastructure software landscape is undergoing a meaningful reset. As AI-native solutions command attention, buyers are simultaneously reassessing the traditional infra stack—seeking solutions that are simpler, leaner, and more cost-effective. The trends we highlight reflect a broader shift in mindset: away from sprawling, over-engineered systems and toward software shaped by pragmatic design.

The bar for infrastructure products has never been higher. Buyers are increasingly discerning—ruthlessly evaluating tools based on performance, ROI, and total cost of ownership. We expect a range of models to win in this new era: from next-gen platforms and open core products to AI-enabled services, clean abstractions, and systems that meaningfully reduce operational burden or cost. The next generation of infrastructure will be defined by products that are pragmatic, performant, and deeply aligned with how modern teams build.

As always, if you're building anywhere in the infrastructure stack, please feel free to reach out to jacob@headline.com and ram@headline.com to connect.

The Infra Stack Reset: 10 Trends Reshaping Spend in 2025

Explore the 10 key infrastructure software trends transforming 2025, where simplicity, transparent pricing, modular data stacks, AI-native services, and pragmatic design are replacing bloat and vendor fatigue in favor of leaner, cost-effective infrastructure solutions.

The Infrastructure Stack Reset | Headline

In early 2020, we met a tiny French team building the new Panini, digitally. 

A fantasy football game where players could own and trade the cards in their collection, courtesy of the Ethereum blockchain. It would be our last in-person meeting before the COVID-19 lockdowns. The timing couldn’t have been worse as all football leagues were shutting down. The tiny team was Sorare, who just closed Europe’s largest-ever Series B - $680 million at a $4.3 billion valuation. This is the story of what set them apart and ultimately made us lead that early round.

At Headline we have always been fascinated by blockchain technologies and the markets forming around them. We invested in a Bitcoin-Ripple exchange more than 15 years ago. Spoiler: It didn’t work out. We were surprised and equally worried about the early ICO craze in 2016, back when even Steven Seagal had one.

It was then that we got excited about a new segment, Non-Fungible Tokens. NFTs prove ownership of a digital item - image, sound file, tweet - in the same way, people own crypto coins like Bitcoin. But unlike cryptos that are identical and worth the same (1 BTC = 1 BTC, duh), NFTs are indivisible and strangely unique. They are more akin to artwork.

Early projects like Crypto Punks made big waves at launch that today echo in the proud owners of Bored Ape Yacht Club snobby primates avatars. We chalked most off as speculative hype, but the idea stayed in our heads.

It wasn’t until early 2020 that we spotted a truly intriguing NFT concept. Through the grapevine of football nerds, we heard of a couple of French entrepreneurs building the new Panini, digitally. We loved the sound of it.

Our good friends at Seedcamp made the connection (forever grateful, Sia!). Back then, Sorare’s Discord only had a handful of members. We joined the Discord and saw they truly believed in the community and the sport. NFTs felt special and personal.

When we spoke with Sorare’s CEO and co-founder Nicolas Julia, we found him both precise and structured, and highly passionate. He had a staggering clear vision despite the early stage BTW: here’s the funding deck he approached Headline with.

He envisioned a scenario greater than “just” a fantasy football game. His was a platform for fans from all over the world to meet, own their games, track up-and-coming players. An entirely new kind of media platform.

Early traction was impressive. Three months in, their user retention was  >60%, with an incredibly high average revenue per user. 50+ football clubs had already been onboarded.

“It’s a brand new market for the clubs and leagues. It’s not competing with video games or existing media. We are expanding their reach to new audiences and geographies. And it’s a way to consolidate the digital leadership of their brands” says founder Nicolas Julia.

All of this with a tiny pre-seed round, no paid marketing, and only 5 members in the team. This was unique. What were they “doing right”? We recently spoke with some of the early users, to recount what stopped us in our tracks during our research:

“Two things made Sorare stand out: Their capacity of consistently onboard official clubs to the platform and how, from Day 1, they made any new iteration of the platform a joint effort between the community and the development team” says Mark Sharman, one of the original Sorare beta testers who now earns from professionally trading Sorare cards and other NFT assets.

“In 2018, I was playing an NFT baseball game. I liked the sport, the design of the cards, and the official license. But I felt the owners weren’t listening to the community. They made mistakes on the card's rarity that turned users off. Nicolas from Sorare found me on that game’s Discord’s channel and asked me how I would feel around a collectible game around football. Others had tried before, but they held no official rights and relied on cartoon characters or infringing copyright licenses.”

“They didn’t build Sorare in a vacuum and then try to market it. They were never too proud to keep pushing in the wrong direction. When they made mistakes, they were always open to admit them. They’d take that back, and rebuild the product. What they built is a community. There is no space for sunken-cost fallacies in that.”

“Nicolas started a new Discord with me, his co-founder, and a handful of soccer and crypto enthusiasts that are still involved to this day. 30 beta testers were trying the platform in late February 2019. They took the time to try over and over until they got the formula right. It got competitive from the beginning, even if the first cards were from the Belgian league and most of us didn’t know much about it”.

Another user who prefers to stay as a background source echoes these thoughts:

“Other companies go wrong because they are building the game they want while pretending it’s the game you want. Sorare got a bunch of people that really liked football together and kept asking relevant questions. They kept working with them and it eventually became a collective achievement. They didn’t build Sorare in a vacuum and then try to market it. They were never too proud to keep pushing in the wrong direction. When they made mistakes, they were always open to admit them. They’d take that back, and rebuild the product. What they built is a community. There is no space for sunken-cost fallacies in that.”

Like every company, Sorare had its fair share of faux pas in the early days. But it was always quick to address them, as explains founder Nicolas Julia himself: 

“Something we did wrong, in the beginning, was thinking we could just move from the early adopters to a mainstream audience. Nobody can. The product takeaway is constantly moving from a niche audience to a slightly larger niche over and over.”

"Sorare is built around three core principles that reflect this”, continues the Paris-based entrepreneur. “The first is empathy towards the community. I know everyone says this and it might come across as bullshit, but we truly stick to it. Everyone in the company talks daily with our users and listens. Building in the open is a key part of Sorare’s success”.

“The second is velocity. We can make mistakes along the way, but we need to keep the agility to fix them fast and move on. It’s about having a mentality where every team moves fast, not just product design or engineering”. 

“Finally, there’s ambition: We are creating something entirely new. How can we achieve it if we limit ourselves to the goals we had set? We could have never raised this kind of Series without this mindset.”

So far, Nicolas’s mental framework is reflected in the tremendous growth of Sorare: The number of monthly active paying Sorare users continued growing by 34X between Q2 2020 and Q2 2021, with quarterly sales increasing by 51X.

180 professional football clubs have now adopted the platform and granted their licensing rights, with world stars like Barcelona’s Gerard Piqué and Atlético Madrid’s Antoine Griezmann involved both as investors and Sorare players. 

“Antonie wants to compete” continues Mark Sharman, “He fights for the leaderboards. He makes some great purchases, but he’s fighting for the leaderboards. And he’s winning prizes.” 

“You know how at the start of the season players proudly compare their own FIFA cards and ratings? Many professional football players reach out to us to check how their Sorare cards are doing after their last played game. This kind of special relationship with all users is one of the strengths of our company” tells us Brian O’Hagan, an early team member of the company who leads the Growth team.

“There are three kinds of Sorare users: Casual football fans can seek their own players and compete in fantasy tournaments. Professional traders are after a return on their investments. Crypto enthusiasts collect and hold. They all get something out of the platform”.

This diverse community is super active in building all kinds of third-party applications. SorareData, a platform that uses the Sorare API to provide market data, player stats, and scouting features even bagged a $700K funding round of its own.

Of course, we couldn’t know Sorare would go on redefining best-in-class metrics for consumer business and building an ecosystem when we first invested in 2019, but the mindset was already clear back then: Where others focused on the collectibles, Sorare used the technology to give fans new ways to connect with one another and football.

At the core, we saw three key advantages in Sorare:

1) It’s truly a community product, and our thesis is that it will be successful as long as it stays one. 

2) Utility is a core feature of any successful NFT. If your NFT is just beautiful, it might not be enough. But if it has certain perks to it, NFTs unleash their power. Sorare's cards can be used on any given match day. That’s a retention driver.

3) Europe: There is no better place on earth to start a soccer-focused company, build relationships with clubs and leagues. Europe is the cradle of soccer and this plays to the strengths of the business.

It’s mind-boggling what Sorare can achieve when they leave the beta and focus on consumer acquisition and/ or to more verticals. One could easily imagine a media arm spinning from the main game alone to be worth €1bn+. 

As Nicolas Julia himself puts it:

“Every time we close a funding round, I come back to the team and explain it’s a milestone towards the next step. But that we need to keep focusing on what has been important for us since Day 1: sticking to the values we share with our users and continue growing. What has been working the past six months might not work for the following six.”

With its passionate community, long-term utility, and deep relationships in the European soccer universe, Sorare built three defensible moats that will be very hard to compete against. To the entire Sorare team: this is your day, you deserve this!

Congratulations to Softbank (SoftBank Vision Fund 2 and the SoftBank Latin America Fund), Bessemer Ventures, IVP, Hillhouse, Atomico, Blisce, D1 Capital, and Eurazeo. Great to have you alongside Headline, Benchmark, Accel, Partech, Futury Capital, business angels like Reddit co-founder Alexis Ohanian, and football players Gerard Piqué, Rio Ferdinand, and Antoine Griezmann.

Europe’s Largest B Round: Why We Invested in Sorare

Christian Miele and Dominic Wilhelm on why Headline invested in the fantasy sports video game Sorare. When we first invested in 2019, the mindset was already clear back then: Where others focused on the collectibles, Sorare used the technology to give fans new ways to connect with one another and football.

Europe’s Largest B Round: Why We Invested in Sorare | Headline

This is part 2 of a 6 part series on the trends that are changing Software-As-A-Service (Saas) for Small & Medium Enterprises (SMEs):

🛒Trend #1: Shopify’s growth creates new markets
🍉Trend #2: Restaurants fight for independence from delivery and discovery platforms - Below :)
👋 Trend #3: Work, reinvented
👔 Trend #4: The rise of vertical biz management software
🤴 Trend #5: HR made simple(r)!
👹 Trend #6: Bookkeeping becomes understandable

If you'd like to have your company featured in this article, feel free to reach out! We would love to hear from you.

Intro: Restaurants want to avoid third-party apps

As a result of the COVID-19 pandemic, restaurants were some of the first businesses to undergo (read: be forced into) large-scale digitization. Online delivery platforms such as Doordash, Uber Eats, Deliveroo, and Delivery Hero (amongst others) have been some of the largest beneficiaries of the pandemic by allowing restaurants to continue serving customers during the lockdowns.

As online food delivery becomes more prevalent, a dichotomy between the interest of restaurants and digital platforms emerges. Food delivery platforms (Uber Eats, Deliveroo, JustEat, Doordash...) and discovery platforms (Trip Advisor, Yelp, Zomato...) have now assumed a more dominant position in their relationship with the restaurants using their platforms. 

Restaurants have become dependent on them to access their customers and to generate traffic for their services. These third-party platforms cannibalize their own traffic by charging restaurants a 20 to 30% commission for directing customers to them. 

Recognizing the increasingly dominant position of food delivery and discovery platforms, a number of companies focusing on developing alternative third-party software for restaurants have emerged. 

These companies offer software that recreates many of the experiences provided by traditional food delivery aggregators while providing a greater degree of independence and customization to restaurants themselves.

Services offered by these companies include processing online orders, allowing restaurants to develop their own brands online, and fostering word of mouth through marketing campaigns.  

Some of these solutions are looking to go even deeper into a restaurant's technology stack and allow them to reassess long-accepted offline operations. These include processes like food production and supply management, personalizing the client service experience, and accelerating table turnover in physical dining areas.

In order to simplify the presentation of these various solutions, we have categorized them below.

Restaurants are seeking new ways of managing their online operations while also creating new sources of revenue.


⭐  Website Builders: ChowNow (US), BentoBox (US), PopMenu (US), Lunchbox (US), Flipdish (Dublin), Slerp (UK), Orderyoyo (Denmark),

Onvi (UK), ZenChef (France), PepperHQ (UK) help brick-and-mortar restaurants to build their own ordering website using low code solutions.


Compared to classic horizontal website builders (like Wix), these solutions are specifically designed to display an online menu in an appealing way.  The idea behind these software solutions is to keep customers on a restaurant’s own website by incorporating third-party reviews, images, recommendations, and menu descriptions on the web page.


Customers are then driven to spend more time on a restaurant’s website allowing for greater conversion and an increased amount of data points to assess future interactions.  At scale, these businesses aim to allow businesses to control their own end-to-end online experience with their customers.

Ultimately, these solutions intend to have customers order and pay directly for their meals from a restaurant’s website.
(unsurprisingly, BentBox has recently been acquired by payment giant Fiserv)

⭐  Marketing Solutions. These tend to be industry-agnostic, but over the last few years, there have been a number of businesses that have been developing dedicated marketing tech for the restaurant industry. As they process customers' orders through their platform-generated websites, companies such as Lunchbox, ZenChef, and BentoBox also capture specific customer data. This can then be leveraged to collect feedback, target customers via email, create personalized loyalty programs and/or gift services. 

Mapping SMEs SaaS #2: Restaurants fight for independence. Jonathan Userovici from Headline provides an overview of companies offering software that recreates many of the experiences traditional food delivery aggregators provide while ensuring a greater degree of independence and customization to restaurants themselves.

Joseph Huang

A Conversation with Joseph