Sail Research Raises $80M in Seed and Series A Funding Round

The round was also Sequoia-led Seed. 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 company plans to use the funding to grow its operations and continue developing its technology.

The next phase of AI is moving toward agents that can work on complex tasks independently for hours or even days, instead of the short, back-and-forth interactions most systems are built for today. Current infrastructure was designed for human-style prompts, not for long-running AI agents.

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These agents are limited mainly by the compute and context they can access—the more they have, the better they perform. But with global AI spending expected to reach $2.5 trillion in 2026, many advanced agent use cases are still out of reach due to high costs and system limits not designed for long-running workloads.

Sail Research is designed to remove the limits of current AI infrastructure. It provides a platform built specifically for long-running AI agents.

The system includes two main parts: a high-efficiency inference stack built for heavy, long-context workloads, and Sailboxes, a sandbox environment that can run for hours or days while only charging for active work time. Together, they let teams build more powerful agents at scale without running into the cost and performance limits of traditional systems.

Sail’s efficiency comes from several infrastructure improvements, including customizing open-source inference engines to better use GPU power, distributing workloads across different providers for reliability, and using underutilized compute resources. In a recent benchmark on BrowseComp-Plus, a deep research evaluation test, Sail achieved 90.72% accuracy while costing up to 10x less 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."

"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."

"We and Sail share a belief that background agents are about to do far more useful work. Getting there takes efficient, scalable inference paired with the highest-quality context, including from the web," said Travers Nisbet, co-founder of Parallel. "Sail is building the inference side of that, and we're glad to be aligned on where this is going."

"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."

About Sail Research

Founded in 2024 by Neil Movva and Samir Menon, Sail Research is an infrastructure company built for long running AI agents. Its platform has two main parts: a high efficiency inference system that reduces cost per token by up to 10x and Sailboxes, a sandbox environment that can run continuously for days. Together, they help teams build more powerful and scalable AI agents.

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