The Inference Cloud for AI-native startups

Built for reliability, performance, and flexibility to scale.

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750B

Tokens served daily

30×

Cheaper than legacy clouds

Day 0

Support for frontier LLMs

Built for production

Inference that scales with you

A global fleet on the latest hardware

26 data centers across 15 regions and every current-gen chip class, behind one endpoint that places each request to hit your latency and concurrency SLA.

Tuned to your targets for quality, speed, and cost

Strike your own balance of speed, quality, and cost — an optimization agent tunes your deployment to hit it. Lossless by default, with no hidden quantization; any lossy speedup is yours to opt into.

Commit to spend, not GPUs

Flexible drawdown billing: burn one commitment across any model, scale up or down freely, and our reserve absorbs your spikes in real time — so you never pay for idle GPUs or get locked into hardware.

What our customers are saying

“

Elicit is using LLMs to screen more than 100,000 scientific papers each day, but the cost of high-quality real-time processing was prohibitive. Parasail was essential for removing this bottleneck. Working with Mike and the Parasail team has been refreshingly straightforward — they're responsive, technically excellent, and helped us get high-throughput screening into production with minimal engineering overhead. We're already exploring the next use case for their platform.

Andreas StuhlmüllerCEO, Elicit

“

We needed to deploy our custom model quickly and cost-effectively. Parasail got us up and running in no time. Their team responded immediately to our request for lower latency in Europe, setting up an endpoint that improved user experience for our customers. The economics were so favorable that we could make our tutorial model publicly accessible for free without asking customers to enter API keys or credit cards.

Alan NicholCo-Founder & CTO, Rasa

“

Parasail's batch processing made it significantly easier for us to generate millions of responses for dataset building and researching. Running large batches of requests allowed us to easily coordinate access among our researchers and saved us tremendous time and effort compared to handling millions of individual requests with retries and rate limitations. It's been a seamless experience that enabled us to move faster.

Oussama ElachqarCo-Founder, Oumi

Model library

One API for any model

2M+ open models Day-0 access to frontier open models Your custom fine-tunes

FAQ

Questions, answered

We're paying a closed-model vendor directly. Can we switch?+

Yes — one of the most common reasons teams come to us. Parasail runs open-source models on dedicated infrastructure, giving you the same capability without single-vendor dependency, rate limits, or throttling. Most teams run Parasail alongside their existing setup first, then migrate workloads over.

Are the models as capable as Claude or GPT for my use case?+

For most production use cases, yes — and for some, better. The best open models (Llama, DeepSeek, Qwen, Kimi) have closed the gap, and for domain-specific tasks a well-tuned open model often outperforms a general closed one. We'll run a side-by-side PoC on your actual workload before you commit to anything.

Can I use specialized or fine-tuned models?+

Yes. Any model on Hugging Face is deployable — including fine-tunes, custom architectures, and sidecar containers. We run specialized models for reranking, OCR, vision, voice, and retrieval all on the same platform, so you don't need a separate vendor per modality.

If something breaks, can I talk to a person who'll fix it?+

From day one you get a shared Slack channel with your dedicated solutions engineer and our performance team — not a ticket queue. When something breaks, you're talking directly to the engineers who run your deployment. Response time is measured in minutes, not days.

How fast can we get up and running?+

Optimized endpoints are typically live the same day — many customers integrate right after the first call. No legal back-and-forth — just a standard ZDR and SLA agreement. You pick a workload, we configure and deploy. The complexity stays on our side.

Why not just self-host?+

Self-hosting looks cheaper until you account for MLOps headcount (two to three engineers), idle GPU burn, scaling complexity, and constant maintenance as models evolve. Parasail gives you the control of self-hosting — any model, any configuration — without the operational burden or capital commitment.

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