Infrastructure Startups funded by Y Combinator (YC) 2026

Fivetran automates data movement out of, into and across cloud data platforms. We automate the most time-consuming parts of the ELT process from extracts to schema drift handling to transformations, so data engineers can focus on higher-impact projects with total pipeline peace of mind. With 99.9% uptime and self-healing pipelines, Fivetran enables hundreds of leading brands across the globe, including Autodesk, Conagra Brands, JetBlue, Lionsgate, Morgan Stanley, and Ziff Davis, to accelerate data-driven decisions and drive business growth. Fivetran is headquartered in Oakland, California, with offices around the world. 

Your agent processed a refund without looking up the order ID, costing you thousands. You only found out three hours later from a support ticket. Evals, output scoring, and observability can reduce the likelihood of mistakes like these occurring - but there's no solution that inspects and prevents an action as it’s about to execute. Salus does that. We’ve built an API that wraps around your agent and checks its actions at run time, blocking incorrect ones and providing immediate feedback to guide retries. Kevin and Vedant were roommates at Stanford, where they both studied computer science.

Autumn is building the first real-time signal intelligence platform for GTM teams. We monitor posts, commits, blogs, and announcements, surfacing buying signals the moment they appear. Define your ICP and the signals that matter, and we deliver a condensed, real-time feed filtered by intents.

Get your personal OpenClaw instance on the cloud and connected to your Slack or other messaging app in 3 minutes. Pre-installed security features and common skills.

Oximy helps enterprises understand and manage AI usage across their workforce. Used by security, finance, and transformation teams to track adoption, control spend, and manage governance as AI scales.

APIs weren’t built for agents. Orthogonal fixes that. Developers and agents get instant access to hundreds of APIs through our MCP or SDK. No API key management, no billing headaches, pay as you go. API providers list once and become instantly discoverable.

Compresr provides an API that compresses LLM context without losing what matters. It’s a drop-in for agents and RAG that cuts token costs and improves accuracy.

Shofo builds large-scale social media training datasets by collecting, labeling, and enriching public content for pre-training and fine-tuning AI models. Our indexes continuously collect and update every video across hard to access social media platforms. Companies use Shofo to avoid building their own data collection and processing systems and spending months turning unstructured social content into model-ready data. We started with TikTok and are expanding across every major social platform.

Cumulus Labs is building a performance-optimized GPU cloud for AI training and inference, where customers are only charged by physical resource use. The platform aggregates idle GPU capacity from public clouds, private data centers, and vetted individual hosts into a single, unified Cumulus pool. For training and fine-tuning, workloads are predictively packed alongside other jobs to maximize efficiency and dynamically migrated live during execution to faster or cheaper clusters as they become available. For inference, the platform captures and replicates execution state across our global compute CDN, enabling ultra-fast cold starts and performant serving where your users are. The Cumulus Scheduler constantly diagnoses failures, auto-recovers workloads, and intelligently orchestrates across the entire pool. The Cumulus Prediction system will learn usage patterns to optimize resources available to customer jobs. Getting started takes less than 20 lines of config. Fine-tuning becomes as simple as specifying your data and model architecture, while inference deployments automatically optimize for latency and cost. Cumulus handles all GPU orchestration complexity, allowing teams to save on cost and significantly boost performance in real-time while focusing on what matters: building & serving better models. The result is 50-70% cost savings, faster cold starts, and never spending time managing infrastructure ever again.

Chamber puts your AI infrastructure on autopilot. Our agentic AI enabled platform orchestrates, governs, and optimizes your AI infrastructure so teams can run ~50% more workloads on the same GPUs without manual intervention. It operates like an autonomous infrastructure team: continuously monitoring GPU clusters, forecasting demand, detecting unhealthy nodes, and reallocating resources in real time to where they create the most impact. Your ML teams move faster, infra waste drops, and GPU bottlenecks disappear.

Tensol lets you deploy and manage AI employees for your team - built on OpenClaw. Your AI employee has full context of your company, is running 24/7 and can monitor Slack, track Sentry errors, update your CRM. We handle the setup and deployment of isolated VMs, no need for a Mac mini, a dedicated phone number or a devOps engineer. Tensol also lets you create specialized AI employees for different jobs, like engineering, sales, and support, from a simple UI. You can connect the tools your team already uses (Slack, GitHub, Sentry, HubSpot, Linear, Gmail, and more) with one-click integrations and a quick OAuth flow that takes about two minutes. Each AI employee also gets its own dedicated identity, so it can operate like a real teammate.

Edge AI is inevitable, but shipping it is painful: every device class behaves differently, runtimes vary, models are huge, and performance collapses under memory/power constraints. RunAnywhere turns that into an enterprise-ready workflow: one SDK to run models on-device, plus a control plane to manage models, enforce policies, and measure outcomes across thousands of devices.

Haladir is an AI-powered mainframe modernization suite that helps enterprises understand and transform their legacy systems. Our docs product generates comprehensive documentation from COBOL codebases, enabling teams to understand millions of lines of legacy code without months of manual analysis. Our translation tool migrates COBOL and other legacy languages to modern languages like Java and C# .NET while preserving business logic through property-based testing, formal verification, and custom models.

Mantis provides the foundational infrastructure layer that enables companies to build human-in-computer models. We unify fragmented data sources, simulate human anatomy and physiology, and validate against real-world outcomes. From motion capture and biometric sensors to medical imaging and training logs, our platform transforms disparate data into validated digital twins ready for production applications.

Didit is an all-in-one identity platform that lets humans prove who they are starting with a face scan. It powers the fastest identity verification in the market, fights fraud by default, and unifies all identity checks with simple pay-per-use pricing.

Luel is a sourcing and licensing platform for rights-cleared multimodal training data at scale. We work with frontier AI teams to provide high-quality bespoke data collections and off-the-shelf datasets.

Captain delivers the most accurate general-purpose retrieval engine for unstructured data. Connect file stores and effortlessly retrieve knowledge with much higher accuracy than RAG (Avg: 78% → 95% + citations).

Castari lets developers deploy AI agents in secure, autoscaling sandboxes with one command. We handle tool/MCP connectors, snapshots, and observability so teams go from prototype to production in hours instead of weeks. Developers focus on building agents, Castari provides the autoscaling sandbox infrastructure to ensure your agents and its tools are secure.

Nivara gives engineering leaders visibility into how humans and AI build together, helping teams spot bottlenecks, measure impact, and turn AI adoption into real outcomes. We're building the much needed copilot for Engineering Managers and empower every team to work intelligently with AI.

Hyperspell (YC F25) gives AI agents memory. It connects to tools like Slack, Gmail, Notion, and Drive so agents can recall, reason, and remember across company knowledge. Developers use Hyperspell’s API to turn disconnected agents into AI coworkers that understand their work. Conor and Manu founded Hyperspell after building their own workplace agent and seeing how limited AI is without memory. They have over 15 years of experience in machine learning and have scaled API-based products to $30M ARR.

AI researchers should be pushing the boundaries of what's possible with new architectures and training methods. Instead, they waste weeks configuring cloud infrastructure, debugging distributed systems, and optimizing their GPU code. We know because we lived it: While training our own models across thousands of GPUs earlier this year, we spent more time fighting our infrastructure than doing actual research. That why we're building two things. First, Elastic Cloud: a managed platform that automatically finds the cheapest GPUs across all providers, handles spot instance preemption, and cuts compute costs by up to 80%. Second, automatic kernel optimization that makes training code run faster by modeling hardware topology, often beating hand-tuned implementations. The problem is that getting high performance across different hardware is genuinely hard. NVIDIA's CUDA moat exists because writing fast kernels requires deep expertise. Most teams either accept vendor lock-in or hire expensive kernel engineers. Our goal is to break the CUDA moat. The compute bottleneck is the biggest constraint on AI progress. NVIDIA can't manufacture enough GPUs, and their monopoly keeps prices astronomical. Meanwhile, AMD, Google, and Amazon are shipping capable alternative hardware that nobody uses because the software is too hard. We're breaking that moat. If we succeed, anyone will be able to train state-of-the-art models without thinking past their PyTorch code.

Dome makes it easy to build on prediction markets like Polymarket and Kalshi. We provide the developer infrastructure to access, analyze, and build on prediction markets. With our unified API, developers can access live and historical data, including granular datasets not available natively, across multiple platforms at once. Dome makes it simple to trade, embed market data into products, and deploy strategies across multiple platforms through one interface.

S2 is a serverless datastore for real-time, streaming data. The more we worked on data systems, the more we felt like there was a missing building block. The seamless experience of object storage simply did not exist for durable streams – so we set out to fix that! S2 reimagines streams as an unlimited and access-controlled web resource. Like if Kafka and S3 had a baby. - Building agents? Use granular streams to persist context in real time, make workflows auditable, and coordinate between agents. - Publishing fast-moving data? Streams support high fanout and can be accessed directly over the internet, so they are a great fit for broadcasting real-time feeds. - Sandboxing? Emit to per-instance streams, and read history or live events just as easily. - Local-first and multiplayer experiences? Propagate updates reliably and efficiently. Stop dealing with clusters, and start streaming.

Brickanta is building the AI-native operating system for construction, starting with the pre-construction workflows that determine the outcome of entire projects: bid analysis, cost estimation, and procurement. Our platform combines state-of-the-art AI with real project data, standards, and construction best practices. This enables construction, contracting, and real estate companies to identify gaps, risks and opportunities earlier, and to produce project and procurement documentation significantly faster and more efficiently.

We build infrastructure enabling AI agents to reliably perform complex tasks in production

The first advertising infrastructure designed specifically for AI apps. Seamlessly integrate contextual brands & products that enhance user experience while generating revenue to pay the inference bills.

Truth Systems provides a compliance agent to govern the use of generative AI. It translates operational policies into intelligent, real-time guardrails that monitor every AI interaction. This empowers companies to proactively block non-compliant actions and align access to AI tools with user competency. By eliminating the risks and operational blind spots associated with uncontrolled AI adoption, our platform enables enterprises to fully leverage the technology's potential without compromising security. We are establishing the new standard for AI governance, building a future where every professional can innovate safely and confidently.

Kernel is crazy fast, open source infra for AI agents to access the internet 🧠🕸️ - Built on unikernels for crazy fast cold starts and low resource usage - Sub-150ms cold starts on managed headful browsers - Built-in stealth mode with residential proxies and automatic CAPTCHA solving - Full observability: session recording, live view, and full logs - Persistent sessions that maintain cookies and auth across invocations - Native support for all browser and computer use frameworks - SOC 2 compliant and HIPAA-ready Trusted by Cash App, Rye, and 1000+ others. Backed by Accel & Y Combinator (S25).

DeepAware builds an AI-driven automation system for GPU-intensive data centers. Our reinforcement-learning scheduler, real-time market integration, and unified dashboard slice energy waste by up to 30%. Coming soon: autonomous “robot-hand” inspections and maintenance to enable 24/7 operations with minimal staff. More info: https://deepawareai.com/ Jobs/Internship: https://www.deepawareai.com/careers

Manufact provides open-source dev tools and infrastructure for MCP to help dev teams quickly build and deploy MCP Servers for AI agents & MCP Apps for ChatGPT and Claude. We developed the mcp-use SDK, which has over 5M downloads and 9,000 GitHub stars, and is used by 20% of the US 500. Create MCP servers/apps in minutes, connect your GitHub repo, and make them immediately available to AI agents and ChatGPT.

Pangolin is an open-source, identity-based remote access platform built on WireGuard that enables secure, seamless connectivity to private and public resources.

You built the demo in a weekend. Now you need auth, a prod-grade database, real-time events, AI observability, cron jobs, email, monitoring… and you needed them yesterday. We spent a decade at our previous startup, building a platform while scaling to millions of users and customers like Meta & Netflix. Now we put that into an open-source framework, so others wouldn’t have to do the same from scratch. Modelence is a batteries-included framework that abstracts away boilerplate so you can just focus on building your product logic and nothing else - it’s like Next.js + Vercel + Supabase on a single platform, built for full-stack TypeScript. Shipping production apps is much harder than building a prototype that runs locally and 95% of all production apps share the same building blocks. We did a massive amount of work on our previous platforms that was not specific to our product, and we’ve created Modelence to abstract that away: - Authentication - Database setup - Hosting / CDN - AI integration (LLM observability, prompts) - Real-time events - Cron jobs - Email service - Monitoring - Analytics - Admin dashboards Andrej Karpathy recently posted about this same exact problem: https://x.com/karpathy/status/1905051558783418370 and explained it further in his YC AI Startup School talk: https://youtu.be/LCEmiRjPEtQ?feature=shared&t=1940

Luminal builds an ML framework and compiler that generates GPU code. Our stack 10x's model speed while simplifying deployment and cutting idle GPU costs Github: https://github.com/luminal-ai/luminal Discord: https://discord.gg/APjuwHAbGy

AgentMail is the email inbox API for AI agents. It gives agents their own email inboxes, like Gmail does for humans.

Janus automates AI evaluations by using high-fidelity simulation environments, catching failures in reasoning, compliance, tool usage, and performance. The resulting datasets benchmark products and feed post-training loops to continuously improve performance over time.

Aravolta provides a single pane of glass software for data centers to increase uptime, reduce costs, and improve performance. A single utility node onboards every asset within an hour and detects hardware failures, hotspots, and other issues. It also generates reports on important metrics like Power Usage Effectiveness (PUE), energy metering, resource utilization, and much more.

ParaQuery is a fully-managed GPU-accelerated Spark solution providing double the performance at half the cost compared to solutions like Databricks and BigQuery. It is fully Spark-compatible, cloud agnostic, and has seamless integrations everywhere, all without vendor lock-in. ParaQuery makes it trivial to run large SQL and Spark workloads efficiently and serverlessly, without any data migrations.

Morphik is open-source multimodal search for AI apps. We're used by customers ranging from space-tech teams searching across research papers to developers building brokerage agents.

PgDog is an application for sharding PostgreSQL. It understands SQL and can distribute queries automatically between databases. It's built for managed databases, like AWS RDS, and doesn't require any changes to application code or schema. In addition to sharding, PgDog is a load balancer and pooler, so it can act as a replacement for PgBouncer, RDS Proxy, and other Postgres scaling products. It brings the simplicity and performance of HTTP load balancing to the database. Built from the experience of sharding Postgres at Instacart during peak growth in 2020, PgDog is the answer to the old question: does Postgres scale? It does now.

Open-source context retrieval for AI agents across apps and databases. It connects to productivity tools, databases, or document stores and transforms their contents into searchable knowledge bases for agents.

RunRL improves LLMs and AI agents with reinforcement learning. Do you have a model that needs to get better at a certain task? Are you tired of messing around with prompts? Do you spend a lot of money on observability, and wish that all this data could let your model self-improve? Well, head on over to RunRL.com, and see what we have to offer! If you give us a model, a prompt, and a reward, we’ll make your model’s reward go up.

AI models are terrible in non-English languages because it's nearly impossible to find training data in other languages. So, we're building the world's largest and highest-quality multilingual data library.

assistant-ui helps frontend developers add AI chat to their apps. Our product is used by several dozen companies, like LangChain, Stack AI, Browser Use, and Athena Intelligence. We're building an open source Typescript/React library and a backend as a service, with first class integrations into popular agent frameworks like LangGraph and Vercel AI SDK.

Our CLI makes ML model training effortless - just describe your job, and we handle GPU orchestration and execution at half the cost of major cloud providers.

AfterQuery is a research lab (backed by Y Combinator) investigating the boundaries and capabilities of artificial intelligence through novel datasets and experimentation. We believe that pushing the frontiers of AI requires both rigorous scientific inquiry and exceptional human data.

Flowtel is building the next generation operating system for hospitality, powered by modern AI agents that can handle everything from booking to room service.

Butter is building an LLM proxy that records and deterministically replays tool call trajectories. Our goal is to get LLMs out of the hotpath for repetitive tasks, increasing speed, reducing variability, and eliminating token costs for the many cases that could have just been a script.

We are on a mission to build the world’s most accurate search engine over complex and unstructured documents. Most AI products - whether Q&A bots or AI agents - depend on retrieval systems to provide relevant context from knowledge bases. Yet, the vast majority of these systems rely on basic semantic or hybrid search methods, which still frequently fail. These mistakes lead to inaccurate responses and hallucinations by LLMs, frustrating developers and end users alike. That is why we’re building ZeroEntropy: to add intelligence to retrieval and empower developers to create AI products that are more reliable and accurate.

We're building a protocol for large language models to directly interface with image sensors.