DeepInfra
by DeepInfra
Purpose-built inference cloud serving 200+ open-source AI models on OpenAI-compatible APIs
DeepInfra is a cloud platform built specifically for high-throughput AI inference, giving engineering and platform teams production access to 200+ open-source models through OpenAI-compatible APIs. It owns and operates its own GPU fleet across nine data centers and is SOC 2 and ISO 27001 certified with a zero data retention policy.
At a Glance
- Category
- Infrastructure & Cloud
- Pricing
- Usage-based
- Target Market
- CTOs, Platform Engineering Leaders, Enterprise Developers, Data Scientists, MLOps Engineers
- Founded
- 2022
- Headquarters
- Palo Alto, United States
Key Features
- ✓200+ open-source models on one API
Serves text generation, embeddings, speech recognition and text-to-image models including Llama, Mistral and Claude through a single endpoint.
- ✓OpenAI-compatible APIs
Existing OpenAI SDK integrations can point at DeepInfra with minimal code changes, lowering migration cost.
- ✓Owned GPU fleet across nine data centers
DeepInfra owns and operates its own infrastructure, including a 1.7 MW Toronto site with 1,000+ NVIDIA Blackwell B300 GPUs opened July 2026.
- ✓Dedicated GPU deployments
Custom LLM deployments bill hourly per GPU — A100 80GB at $0.89, H100 80GB at $2.20 and B200 180GB at $3.69 per GPU-hour.
- ✓Flex, Standard and Priority scheduling tiers
Three service tiers priced at 0.8x, 1x and 1.5x base rates let teams trade latency guarantees against cost.
- ✓Enterprise security posture
SOC 2 and ISO 27001 certified with a zero data retention policy on inference traffic.
Capabilities
Use Cases
- •Production LLM serving at scale
Runs high-throughput text generation workloads that collectively process close to five trillion tokens per week.
- •Agentic AI backends
Supplies the continuous, high-volume inference that autonomous agent systems generate in production.
- •Multimodal AI pipelines
Provides embeddings, automatic speech recognition and text-to-image inference alongside text generation on one platform.
Ideal For
Best For
- ✓Serving open-source LLMs in production without managing GPU infrastructure
- ✓High-volume agentic workloads that need predictable per-token inference costs
- ✓Migrating off proprietary model APIs using an OpenAI-compatible drop-in endpoint
Integrations
Market Analysis
Pros
- ✓Very low per-token pricing versus proprietary model APIs
- ✓Owned infrastructure gives capacity and pricing control others lack
- ✓Drop-in OpenAI compatibility keeps migration cost low
- ✓Zero data retention plus SOC 2/ISO 27001 suits regulated buyers
Cons
- ✗No free tier or trial listed in pricing documentation
- ✗Focused on open-source model serving — not an end-to-end MLOps or agent platform
- ✗Cloud-only; no on-premise or hybrid deployment option
- ✗Geographic footprint is still heavily U.S.-weighted despite the Toronto expansion
Pricing
Serverless inference (per token)
From $0.02/1M input tokens
- ✓Per-million input and output token billing
- ✓200+ open-source models
- ✓OpenAI-compatible API
- ✓Example: Meta-Llama-3.1-8B-Instruct at $0.02/$0.04 per 1M tokens
Dedicated GPU deployment
From $0.89/GPU-hour
- ✓A100 80GB at $0.89/GPU-hour
- ✓H100 80GB at $2.20/GPU-hour
- ✓B200 180GB at $3.69/GPU-hour
- ✓Custom LLM deployments
Scheduling tiers
0.8x-1.5x base rate
- ✓Flex at 0.8x base
- ✓Standard at 1x base
- ✓Priority at 1.5x base
Language models bill per million input/output tokens; most other model types bill on inference execution time. Monthly invoicing thresholds span $20-$10,000 across five usage tiers. No free tier or trial is listed in the pricing documentation.
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