SAN FRANCISCO — Sail Research, an infrastructure company for AI agents, has raised $80 million in Seed and Series A funding at a $450 million valuation. The Series A was led by Kleiner Perkins, and the Seed was led by Sequoia. Other participating investors included Redpoint Ventures, Theory Ventures, Vine Ventures, CRV, A*, and Abstract Ventures, in addition to angel investors, including John Hennessy, chairman of Alphabet Inc., Lip-Bu Tan, CEO of Intel, and Tri Dao, Chief Scientist at Together AI.
The next chapter of AI is agents that work autonomously on complex tasks over hours and days, not the brief, turn-by-turn interactions that today’s infrastructure was built for. While the underlying stack was optimized for responding to a human waiting at a prompt, it wasn’t built for the ways in which AI agents are fundamentally different from humans. Agents are limited only by the compute and context available to them, and the more they’re given, the better the work they produce. With global AI spend projected to reach $2.5 trillion in 2026, the most ambitious agent workloads remain out of reach for most organizations, constrained not just by cost but by the rate limits and scale ceilings of platforms never designed for long-horizon use.
Sail Research was built to remove those constraints. The company provides the first infrastructure platform purpose-built for long-horizon AI agents, with two core components: an inference stack rebuilt from the ground up around throughput and efficiency, designed for agents spending billions of tokens on a single task; and Sailboxes, a sandbox environment built to run for hours and days that only charges for time agents are actually doing work. Together, they give teams the economics and the scale to build agents that are maximally ambitious, without hitting the walls that stop most production deployments in their tracks.
“Sail exists to make intelligence abundant,” said Neil Movva, co-founder and CEO of Sail. “Every decision we make, from the chip level to the API, is about giving teams the tokens, the scale, and the runtime to build agents without limits.”
Sail’s efficiency advantage stems from a combination of proprietary infrastructure optimizations, including deep customization of open-source inference engines to push GPU performance toward frontier capabilities; intelligent workload distribution across providers for maximum resilience; and the strategic use of underutilized compute. In a recent benchmark, Sail’s inference topped BrowseComp-Plus, a leading deep research evaluation, achieving 90.72% accuracy at up to 10 times lower cost than leading alternatives.
“Most inference infrastructure was designed to minimize latency on a single request, but that’s the wrong optimization for agents, which need to sustain throughput across thousands of concurrent calls over hours,” said Samir Menon, co-founder and CTO of Sail. “We’ve rebuilt the stack around that constraint, and the efficiency gains compound across every layer.”
Co-founder and CEO Neil Movva spent years at NVIDIA pushing GPU performance to its limits, then built infrastructure expertise at Apple and Together AI. Co-founder and CTO Samir Menon also comes from Apple, where he built systems at massive scale.
“The infrastructure layer for the agent era is one of the most important bets in AI right now, and Neil and Samir are exactly the founders to build it,” said Aditya Naganath, partner at Kleiner Perkins. “They bring a rare combination of deep compute expertise and systems rigor that only comes from having built at the limits of scale. Together, they’re building the defining inference platform for long-horizon agents.”
