Parasail raises $32M to build ‘AI Supercloud’ that deploys and scales AI agents in minutes

The race to control AI infrastructure is shifting fast, and Parasail wants to sit right in the middle of it.

The AI startup says it has raised $32 million in Series A funding, bringing its total to $42 million. The round was co-led by Touring Capital and Kindred Ventures, with backing from Samsung NEXT, Flume Ventures, Banyan Ventures, and earlier investors.

Parasail is going after a growing problem: companies want to build with AI, but getting models into production still feels slow, expensive, and fragmented. GPUs are scattered across providers, performance tuning takes time, and scaling often turns into a negotiation-heavy process.

Parasail’s pitch is simple. Remove that friction.

The company is building what it calls an “AI Supercloud,” a global layer that aggregates compute from multiple sources and makes it instantly usable for developers. The goal is to let teams deploy AI endpoints in minutes and scale them without having to make infrastructure decisions or incur vendor lock-in.

That idea lands at a moment when the entire cloud stack is being rebuilt around AI. Data centers are filling up with GPUs, and spending is climbing into the trillions. Yet access to that compute remains uneven, especially for startups trying to move quickly.

Parasail positions itself as a bridge between the supply and the developers trying to use it. Its system routes workloads across a mix of internal and external GPU capacity, then adjusts performance and cost in real time. Instead of tuning models manually or locking into a single cloud, developers get a layer that handles it behind the scenes.

The company says customers can spin up production-ready endpoints with a few lines of code and scale through traffic spikes without having to rewrite their stack. The focus is less on raw speed benchmarks and more on keeping latency, throughput, and cost in check as usage grows.

“AI builders shouldn’t have to become infrastructure experts to ship great products,” said Mike Henry, founder and CEO of Parasail. “AI is becoming the core infrastructure for modern software. But the infrastructure layer itself hasn’t kept up. We built Parasail so teams can deploy custom AI at massive scale without negotiating contracts, managing fragmented GPU supply, or hiring performance engineering teams.”

That message reflects a broader shift. More companies want direct control over their AI systems instead of relying on closed APIs. Open models are gaining traction, and teams are looking to run their own workloads with tighter control over cost and performance.

The problem is that the underlying AI infrastructure hasn’t caught up. GPU supply fluctuates, inference tuning can be tedious, and scaling often involves long setup cycles.

Parasail’s approach is to treat infrastructure as a programmable layer. It abstracts away where compute comes from and focuses on how workloads run. That includes automated optimization across its network, so performance adjusts without constant manual work.

Investors see that layer as a missing piece.

“AI infrastructure is moving beyond single-cloud models,” said Samir Kumar, General Partner at Touring Capital. “As inference workloads scale, companies need flexibility across hardware, geography, and cost structures. Parasail has built the control layer that makes that possible. The team combines deep systems expertise with a clear product vision, and we believe they are well-positioned to define how modern AI applications are deployed.”

The shift toward AI agents adds another layer of urgency. These systems don’t just call a single model; they chain multiple models together, operate across tasks, and generate large volumes of tokens. That creates heavier and less predictable workloads.

“The main product construct of this AI wave is the agent – replacing the notion of the manually-operated application world of the last thirty years,” said Steve Jang, Managing Partner at Kindred Ventures. “These agents are directed but can operate autonomously, call multiple models at runtime, and will require massive amounts of tokens. This new world and its developers need powerful customized inference and reinforcement learning capability that are flexible, instant, and dependable. Parasail offers the first agent-focused inference and training solution, which simplifies the model and compute complexity of today’s dynamic generative AI market.”

Since launching in April 2025, Parasail says it processes more than 500 billion tokens per day. Its customer list includes companies like Elicit, mem0, Gravity, Kotoba, and Venice, and it reports 30% month-over-month revenue growth.

The company is part of a new wave of infrastructure startups focused on inference rather than training. The bet is that as AI usage grows, the real bottleneck shifts from building models to running them at scale, reliably, and at a predictable cost.

Parasail wants to be the layer that makes it possible, turning a fragmented supply of GPUs into a single, unified cloud.