Sail Research

Sail Research: infrastructure that lets AI agents run long and cheap

Sail Research builds inference infrastructure optimized for long-horizon AI agents — systems that work in the background for hours or days rather than answering a single prompt. Its platform has two parts: a high-throughput inference stack that serves open-source models (GLM, DeepSeek, Kimi-K2, Nemotron, Gemma, and others) through OpenAI- and Anthropic-compatible APIs, and Sailboxes, persistent hosted sandboxes where agents can execute code and compute tasks for extended periods without losing state.

The pricing model is built around the observation that most agent traffic does not need instant responses. Completion windows (asap, priority, standard, flex) let teams trade latency for cost, with the flex tier priced roughly 60–80% below typical rates. There are no strict rate limits, so bursty agent workloads and RL rollouts can scale without queuing games. Self-serve usage-based billing comes with $5 in free monthly credits; enterprise plans add volume pricing, SLAs, and optional HIPAA-compliant deployments on top of SOC 2 compliance.

Key Features

• OpenAI- and Anthropic-compatible inference APIs for drop-in migration
• Serving for major open-source models including GLM, DeepSeek, Kimi-K2, Nemotron, and Gemma
• Completion windows that cut costs up to ~60–80% on latency-tolerant traffic
• Sailboxes: persistent, stateful sandboxes for long-running agent execution
• Fine-tuning and RL rollout support with Tinker integration
• No strict rate limits; SOC 2 compliant with HIPAA-capable enterprise deployments

Ideal Use Case

Sail fits teams running high-token-volume agent workloads — background research agents, code-review pipelines, batch data processing, RL training rollouts — where latency is negotiable but cost and statefulness are not. If your agents die when a serverless sandbox times out, or your inference bill scales faster than your product, this is the profile Sail targets.

How Sail Research differentiates

Most inference providers optimize for chat latency; Sail optimizes for sustained agent throughput and cost, and couples inference with persistent execution environments in one platform. The company emerged from stealth in June 2026 with $80M in combined funding — a Sequoia-led seed and a Kleiner Perkins-led Series A — at a reported $450M valuation, with angels including John Hennessy, Lip-Bu Tan, and Tri Dao. Founders Neil Movva and Samir Menon count Parallel (web search APIs), Detail (code review), Jack & Jill (career agents), and Quadrillion (research agents) among early customers.

FAQ

What is Sail Research? An infrastructure company providing low-cost LLM inference and Sailboxes, persistent sandboxes for long-horizon AI agents.

What are Sailboxes? Hosted, stateful sandboxes where agents execute compute tasks for extended durations without losing state between steps.

How much does Sail Research cost? Usage-based self-serve pricing with $5 in free monthly credits; flex completion windows run up to ~60–80% cheaper. Enterprise volume pricing is available.

Which models does Sail serve? Open-source models such as GLM, DeepSeek, Kimi-K2, Nemotron, and Gemma via OpenAI- and Anthropic-compatible APIs.

tl;dr

Sail Research pairs cheap, burst-tolerant inference for open models with Sailboxes, persistent sandboxes built for long-running agents. $80M from Sequoia and Kleiner Perkins, out of stealth June 2026.

Related

Looking for more options? Browse the AI Infrastructure directory or read our best AI infrastructure tools listicle. Sail Research is also tracked on Crunchbase.