Browse 7 startups in San Francisco working in Infrastructure. Every listing below is matched on location and skill set so you can discover the right company faster. Compare profiles and reach out directly.
PrimeVault, a Y Combinator Summer 2022 company, is building what it calls the all-in-one digital asset and stablecoin platform for enterprises and financial institutions. As stablecoins move from crypto curiosity to serious financial infrastructure, the companies issuing, holding, and moving them are running into a familiar problem: enterprise-grade tooling has not caught up with enterprise-grade […]
Aravolta, a Y Combinator-backed company, is building the unified software layer that data center operators have long wanted. Data centers have become the beating heart of the AI economy, but the software running them is often surprisingly primitive. Operators juggle a patchwork of tools to track power usage, environmental conditions, and hardware health — tools […]
Telemetron, a Y Combinator Fall 2025 company, is building an AI-powered support and operations platform tailored for hardware products — and going after a category that most modern support platforms quietly ignore: the messy, high-stakes world of hardware customer support. The platform serves medical devices, consumer electronics, industrial hardware, robotics, and the rest of the […]
Lilac, a Y Combinator Summer 2025 company, is on a mission to turn idle GPU capacity into a market — automatically. The same data centers that everyone is racing to fill with new GPUs are sitting on a quieter problem: a meaningful share of the silicon they already have is idle at any given moment. […]
Modelence, a Y Combinator Summer 2025 company, has emerged from stealth with an open-source, full-stack platform designed to eliminate the repetitive infrastructure work that slows teams down when moving AI applications from weekend demo to production. Backed by the founders’ decade of experience scaling one of the world’s largest skill-assessment platforms, Modelence offers developers a […]
Enterprise AI projects tend to fail at a specific, unglamorous step: parsing the documents. Research teams can spend six months building a document pipeline and still watch fewer than ten percent of those projects make it into production, because generic large language model parsers and legacy OCR systems fall apart on the multimodal PDFs, scanned […]
InsForge thinks the current generation of backend platforms was built for the wrong user. Databases, authentication systems, and infrastructure APIs have spent the last two decades optimizing for human developers – their cognitive load, their dashboards, their documentation. But with AI coding agents now shouldering more of the actual work of shipping software, InsForge argues […]
