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. 

Expanse unlocks wasted GPU capacity. We recover idle compute through three capabilities: resource prediction (right-sizing job submissions before they reach the scheduler), optimisation suggestions (code and config changes researchers can apply themselves), and failure prediction (catching jobs that will fail before they consume hours of GPU time). We’re four engineers. We ran HPC and GPU training workloads at the largest quant funds and national supercomputing centres. We faced this problem first hand and the only fix was to over-provision and burn millions. Ismaeel built the first multimodal HPC resource predictor as research at EPCC (Edinburgh’s Parallel Computing Centre), which beat every published baseline. This is the tool we wish we had.

Quant research is generating candidate strategies orders of magnitude faster than backtesting infrastructure can evaluate them. AI has further accelerated alpha research from a handful of signals per quarter to hundreds per day. The bottleneck has moved from researcher time to compute throughput. We're building large HPC clusters (100 - 50,000 nodes) that can run up to 6.4M tasks in parallel across multiple cloud providers on spot instances. Our goal is to make backtesting strategies simple, fast, and cheap at scale.

Kugel is an enterprise-ready TTS model, available on-prem, with a focus on European languages and low latency.

We build the way for you to create context for all your communications, finances, and inbound.

Archal lets you test agents and code that touch third-party APIs without hitting the real services. We build working digital twins that hold state across requests and behave like the originals, so you catch bugs before your agent does something irreversible in production.

Automate consent and user data rights compliance. Built for performance and full developer control.

Clone any postgres DB regardless of size in 6s so agents can test their work

Smol machines solve the problem of "but app works on my machine" by shipping the machine. Smol machine enables devs to ship a lightweight virtual machine with any software, similar to how Electron ships an entire browser with the web app.

We are making the world's fastest browser for your AI agents. Agents don’t need to see UIs like us humans do. We give agents a semantic understanding of the web instead of brittle visual interfaces. Your agents can perform deep research, large-scale scraping, and complex automation—more efficiently with our agentic browser that turns the web into a native protocol for LLMs.

Interfaze is an AI model built on a new architecture that merges specialized DNN/CNN models with LLMs for developer tasks that require deterministic output and high consistency like OCR, web scraping, classification, Speech-to-text (STT) and more. Read paper:https://arxiv.org/abs/2602.04101

The AI-native observability platform for fast-moving engineering teams. Backed by tastemakers from the world's top AI companies: Vercel, Graphite, Daytona, Browserbase, LangChain, Mastra, Replit, and more.

Every computer ever made forces you to do one thing at a time. One cursor. One keyboard. One Hardware. One app in focus. Fifty years. Nobody questioned it. We did. We started over. We built InfinityOS, a fundamentally new computer OS on the cloud from ground up. Infinity runs every app in its own independent computer -- own GPU, own input, own environment. Windows and Linux in the same session. Humans and agents working side by side. No concurrency ceiling. Cold start in seconds. One URL.

Building the communication infrastructure needed for fully autonomous agents

Sandbox infrastructure for AI agents, with full-environment forks and snapshots across files, processes, memory, and databases.

AI companies selling into legacy industries can't go live because their customers' systems of record are Windows desktop apps with no APIs. The only way in is clicking through the UI, and it breaks constantly. Minicor is a platform for building self-healing desktop automations at scale. Speak the automation into existence through your favourite coding agent via our MCP, or to us directly, and get an API endpoint to trigger it. One API call triggers a full desktop workflow on a Windows VM. On-premise, cloud, or Citrix. Video replay, error logging, and scaling built in.

Compression middleware that removes context bloat in milliseconds, lowering costs and improving end-to-end latency. Compression is especially effective across natural language workloads. In a blind LLM arena case study with one of our customers, compressed requests increased user preference, lowered costs, and lifted purchase volume by 5%.

AI SRE agents that automatically learn each customer’s system so they work just like an in-house engineer.

Terminal Use is an orchestration platform for background agents. Purpose-built for agents that use filesystems. Our platform is CLI-first, making it easy for your coding agents to experiment and continuously improve your deployed agents.

Piris Labs offers a full-stack inference service that eliminates the AI data movement bottleneck. By pairing proprietary photonic hardware with a vertically optimized software stack, we minimize the "memory wall" associated with expensive GPUs. This allows us to deliver the same performance as traditional clusters at a fraction of the cost. Our technology improves effective FLOP utilization and reduces latency, finally making the unit economics of trillion-parameter models sustainable. Founded by a team of MIT physicists and Meta AI experts.

Cascade helps companies take their proprietary data and workflows to align models for their use cases, resulting in higher throughput intelligence that works better, faster, and cheaper.

Wayco is building a unified data intelligence layer for medical treatment of legal cases. From the first intake to instant settlement.

We enable AI voice agents to collect payments over the phone through a single API call, handling card processing and PCI compliance across all payment gateways

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. We enable agentic payments so any agent can instantly discover, access, and pay for hundreds of APIs. No subscriptions, no sales calls, just pay per use. API providers list once and get paid every time an agent calls their API.

Backed by top engineers from OpenAI, Scale AI, and Palantir to build AI that understands human culture. Our first commercial product is for the literature industry with a focus on school libraries. It the AI-native OS for libraries, that speed up all of the manual and boring work for librarians. Fastest growing library tech company in history.

Moda is the reliability & monitoring layer for agents. It surfaces patterns across agent hallucinations, laziness, forgetfulness, and tool call failures. While also providing analytics on user frustration, net promoter score, retention, and churn rates. In the next 10 years, with more AI agents spawning and trying to get PMF, developing an agent that actually works for users is critical. Everyone has access to the same models, thus the ones that win on a product level will be the ones who learn fastest from real usage. Moda is that learning layer.

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.

We are building the world’s largest video library. We've aggregated billions of videos into a searchable index and use agents to find and label the exact datasets a lab needs on demand. If a lab needs 100K hours of cooking videos where someone is holding a pan, with reasoning annotations on top, our agents search the index, extract the matching subset, route it through our labeling pipeline, and deliver a custom dataset in days, not months.

Cumulus Labs is a fast multimodal inference provider, purpose-built for AI teams who want faster performance, lower costs, and zero infrastructure work on fine-tuned & open source models. Most teams today are stuck choosing between bad options. Self-hosting inference means wrestling with configurations and babysitting infrastructure that slows/breaks at scale. Big providers like Fireworks are convenient but extremely expensive and idle GPUs. Cumulus ships Ion, a proprietary inference engine that run LLMs, VLMs, and audio/video gen with high performance and lower cost.

Enabling physical AI at scale.

Chamber puts your AI infrastructure on autopilot, and saves your machine learning engineers hours of manual effort. Our agents continually monitor, detects failures, provide root-cause analysis, resolve issues, and optimize AI workloads and scale across clouds. It operates like an autonomous infrastructure team, helping save your research and engineers hours each day, and debugging workload performance issues. Your ML teams move faster, infra waste drops, and GPU bottlenecks disappear.

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.

Mantis is a digital twin company that combines LLMs with high-fidelity physics simulations in order to convert rare and difficult-to-access human behavior data into predictive models.

One API for KYC, KYB, AML, biometrics, and fraud — powered by 1,000+ data sources, 200+ signals, and in-house AI models. Global coverage. Integrate in minutes, pay as you go, 500 free checks/month. Start free at business.didit.me.

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 file search engine built for AI agents. We’ll index data from the sources folks already use like S3, SharePoint, and Google Drive, and easily scale multimodal, petabyte-level content search. We’re the Snowflake for Unstructured Data. Captain tops the Open-RAG-Benchmark with over 20% higher accuracy than standard RAG pipelines. We achieve this through robust data processing techniques like embedding normalization across modalities, ensuring that representations cluster by semantic content rather than data type.

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.

Sciloop creates expert-level math and physics problems that frontier AI models can't solve, then sells the data to AI labs for training and evaluation. Our problems are created by IPhO and IMO medalists — the top 0.01% of STEM talent globally. On our benchmark, models like GPT 5.4 Pro and Gemini 3.1 Pro score 0-5% on our hardest problems. We work with AI labs to supply continuous, fresh training data that pushes the frontier of mathematical and scientific reasoning. Founded by Bilal and Osman, International Physics Olympiad medalists from MIT with hands-on ML research experience at MIT CSAIL.

Hyperspell is the Memory & Context Layer for AI Agents. AI agents are clueless geniuses. They crush humans on any standardized test, but wouldn't last a day at a real job. What today's super-intelligent agents are missing is the real world context they are operating in. A context that humans stitch together from hundreds of data points across dozens of interactions and channels. A context that grows with their tasks. Hyperspell gives AI agents this context by connecting to their user’s workspace data and building a personalized memory and context layer.

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's 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.

Amika is infra to build your software factory. We give your team sandboxed AI coding agents in the cloud: the same infrastructure Ramp, Coinbase, and Stripe built in-house. Kick off work from a web UI, CLI, Slack, or API. Use your preferred coding agent (Claude Code, Codex, etc.). Each agent understands your codebase, runs autonomously, and ships real PRs with live previews of the apps it changed. Use it as your daily driver or plug it into your own code-gen pipeline via API.

S2 is an API for durable streams. We make working with streaming data dead simple and reliable. 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 – agentic AI for society builders. Hundreds of construction-specific AI agents for project analysis, insights, tenders, procurement, and more. Combine the latest AI technology with your organization's project data, templates, and workflows to identify risks and opportunities early and produce better decision support. Brickanta has raised $8 million from leading AI, construction, and real estate investors behind companies like OpenAI/ChatGPT, Airbnb, Klarna, Spotify, and Plangrid-Autodesk, as well as star investors such as Mario Götze, Anton Osika, and Northzone (see movie and press). The founding team has been building and implementing AI in construction and industry since 2018 at companies such as ABB, Fabege, Husqvarna, IKEA and Konecranes.

1. Multi-model Agents SDK: build production-grade tool-calling agents in 5 lines of code using our open-source, vendor-agnostic SDK. Connect multiple models across providers (eg. Opus 4.6 + Gemini 3 Flash + GPT 5.4) within the same agent to optimize your agentic workflow's cost, latency, efficiency, and performance. 2. Agents-as-a-Service (AaaS): serve your agent or service as a hosted endpoint that anyone - including OpenClaw and Claude Code - can call.