Parasail

Parasail is a pay-per-token inference cloud for open-weight models. It is built for AI-native startups and engineering teams that need production LLM endpoints without long-term GPU contracts. One OpenAI-compatible API covers serverless, dedicated GPU, workload-tuned elastic and batch inference, with work spread across Parasail's own GPUs and partner GPU capacity.

Parasail is a San Francisco inference cloud that sells open-model LLM inference on a pay-per-token basis, with no long-term GPU contracts. The company came out of stealth in April 2025. Its founder and CEO, Mike Henry, previously built Groq's cloud offering. Instead of running a single GPU fleet, Parasail runs what it calls an AI Supercloud. This orchestration layer combines Parasail's own GPUs with rented capacity from outside providers across roughly 40 data centers in 15 countries, according to TechCrunch and SiliconANGLE. It also automates kernel configuration, quantization and endpoint tuning, so developers can deploy with a few lines of code. Buyers get four products behind one OpenAI-compatible API at api.parasail.io/v1. Serverless endpoints are billed per token; for example, DeepSeek V4 Flash costs $0.14 per million input tokens and $0.28 per million output tokens. Elastic Endpoints, still in early access, are private endpoints tuned for each workload. Dedicated deployments run on reserved NVIDIA B300, B200, H200, H100, RTX PRO 6000 or RTX 5090 GPUs with negotiated SLAs. An OpenAI-format batch API is priced at 50% off serverless, according to the docs. Parasail is also a provider on OpenRouter, where it lists 39 models from DeepSeek, Qwen, MoonshotAI, Z.ai, Meta, Mistral and others. In April 2026 it raised a $32 million Series A co-led by Touring Capital and Kindred Ventures, bringing total funding to $42 million. At that time it said it served 500 billion tokens a day; its homepage now claims trillions of tokens served daily. Customers named in the funding announcement include Elicit, mem0, Gravity, Kotoba and Venice. Parasail targets AI-native startups and competes with better-funded inference specialists such as Fireworks AI and Baseten.

Category

Infrastructure & Cloud

Pricing

Usage-based, Contact for pricing

Target Market

CTOs, Enterprise Developers, ML Engineers, AI-native startups

Deployment

Cloud-only, API-based

Headquarters

San Francisco, USA

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  Serverless endpoints

  Pay-as-you-go access to popular open models with no minimums or setup, billed per million input and output tokens.

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  Dedicated deployments

  Reserved NVIDIA B300, B200, H200, H100 or RTX-class GPUs sized to your roadmap, with negotiated SLAs and private control of the model.

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  Batch processing API

  OpenAI-compatible batch API for offline jobs of millions of requests, priced at 50% off serverless according to the docs.

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  Elastic Endpoints (early access)

  Private endpoints tuned for each workload and priced per million tokens, with capacity that scales with actual traffic instead of reserved GPUs.

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  OpenAI-compatible API

  Point an existing OpenAI SDK at api.parasail.io/v1 with a bearer API key, so moving from another provider needs minimal code changes.

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  AI Supercloud orchestration

  Routes workloads across Parasail's own GPUs and outside providers in about 40 data centers, and automates kernel and quantization tuning.

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  Commit-based drawdown billing

  Prepaid commitments can be spent on any model, instead of locking spend to one GPU reservation or one model.

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  Agent backends on open models

  Startups building agent products on models such as Kimi K3, GLM-5.3 or DeepSeek V4 Flash get tool-calling endpoints without buying GPU capacity.

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  Bulk document summarization

  Publishers and research tools summarize large archives through the batch API. SiliconANGLE cites a scientific publisher summarizing its archive of academic papers this way.

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  Serving a fine-tuned custom model

  Teams deploy their own fine-tuned open model to a dedicated endpoint, optionally FP8-quantized to halve its memory needs.

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  Retrieval-augmented generation

  Engineering teams build RAG pipelines on hosted embedding and chat models behind the same OpenAI-compatible API, with no separate vendor integration.

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  Reducing inference spend

  Cost-sensitive teams move open-model traffic to per-token and discounted batch pricing to cut inference bills without signing long-term capacity contracts.

Serverless is pay-as-you-go per million tokens with no minimums. Examples: DeepSeek V4 Flash costs $0.14 input and $0.28 output; Mistral Small 3.2 24B costs $0.09 and $0.30; Qwen3-Next 80B costs $0.10 and $1.10. Batch pricing is tiered by model size and precision, from $0.02 input and $0.04 output for 0-4.1B models in FP4 up to $0.97 input and $2.51 output for 500B+ models in FP16; the docs describe batch as 50% off serverless. Elastic Endpoints (early access) are priced per million tokens through sales. Dedicated GPU hourly rates are available only on request, with volume discounts. Prepaid commitments can be spent on any model. No free tier or free trial was confirmed.

This page was written from 12 sources, 10 on domains other than parasail.io.

1. 1.parasail.io — parasail.iovendor
2. 2.parasail.io — pricingvendor
3. 3.docs.parasail.io — parasail docs
4. 4.docs.parasail.io — fp8 quantization
5. 5.trust.parasail.io — trust.parasail.io
6. 6.techcrunch.com — parasail raises 32m to feed tokenmaxxing ai developers
7. 7.siliconangle.com — parasail raises 32m pay per token inference cloud
8. 8.prnewswire.com — parasail raises 32m series a to build the supercloud that pu
9. 9.kindredventures.com — parasail the ai supercloud for the agent era
10. 10.openrouter.ai — parasail
11. 11.github.com — provider transparency.md
12. 12.hn.algolia.com — search