DeepInfra
Low-cost inference API for 100+ models with up to 1M-token context
DeepInfra delivers production-grade inference at aggressive prices, especially for high-volume users. Its wide model catalog and zero-retention policy make it a strong choice for startups and enterprises alike. The main trade-off: no fine-tuning or training capabilities, and data residency is limited to US data centers.
Verified 17d ago · liveness 95/100 · cite: rightaichoice.com/tools/deepinfra
- Startups deploying LLMs with tight budgets needing scalable inference
- Developers seeking fast, low-cost APIs for production AI applications
- Enterprises requiring privacy compliance (SOC 2, zero retention) for inference
- Teams experimenting with cutting-edge open models like DeepSeek, Qwen, Nemotron
- Users who need fine-tuning or model training services
- Teams requiring data residency outside the United States
- Projects relying on a vast community model hub like Hugging Face
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Skip DeepInfra if you need fine-tuning or model training, data residency outside the US, or a free tier to start prototyping.
Private deployments require contacting sales and may have minimum monthly commitments
DeepInfra's pay-per-token pricing is ideal for startups and SMBs that want to keep costs low, with no minimums or seat fees. For heavy usage, cached input tokens cost ~20% of full input price. Competitors like Together AI or Fireworks AI may offer comparable prices on some models, but DeepInfra often has the lowest prices for open-source models and offers GPU rental for custom workloads. Enterprises needing dedicated capacity can negotiate volume discounts through private deployments.
In short
DeepInfra — Low-cost inference API for 100+ models with up to 1M-token context. Best for Startups deploying LLMs with tight budgets needing scalable inference, Developers seeking fast, low-cost APIs for production AI applications, Enterprises requiring privacy compliance (SOC 2, zero retention) for inference. Paid pricing.
What's new in DeepInfra
Checked 16 days agoAcross the latest 4 updates: 4 feature updates.
Step 3.7 Flash is Live on DeepInfra: An Agentic, Multimodal Model Built for Production
DeepInfra adds Step 3.7 Flash, a 198B-parameter MoE vision-language model with 256K context, optimized for agentic and multimodal tasks.
Nemotron 3 Ultra, 3.5 Content Safety and ASR models are now live on DeepInfra platform.
DeepInfra adds Nemotron 3 Ultra (550B MoE) and content safety models to its inference catalog.
DeepInfra Launches Access to NVIDIA Cosmos 3 World Foundation Models for Physical AI
DeepInfra now serves NVIDIA Cosmos 3 Nano and Super models for robotics and simulation workloads.
New Models: DeepSeek-V4-Flash and DeepSeek-V4-Pro now available
DeepInfra adds DeepSeek-V4-Flash and DeepSeek-V4-Pro with up to 1M token context and competitive pricing.
Viability Score
How likely is DeepInfra to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- 100+ models via API (DeepSeek, Qwen, Llama, Nemotron, Gemini, Step, Cosmos)
- Zero data retention policy
- SOC 2 and ISO 27001 certified
- Pay-as-you-go, per-token pricing
- Cached input pricing (up to 80% discount)
- Up to 1M-token context windows (DeepSeek-V4, GLM-5.2, Nemotron-3-Ultra)
- Inference-optimized US data centers
- Text, image, speech, video, world model inference APIs
- Private deployments with dedicated support
- On-demand DGX B300 GPU rental ($4.20/instance-hour)
- OpenAI SDK compatible API
- Step 3.7 Flash (198B MoE vision-language model)
- NVIDIA Cosmos 3 World Model for physical AI
- Automatic speech recognition (ASR) models
- Text-to-music, text-to-video, world model inference
About DeepInfra
DeepInfra is a cloud inference platform providing developer-friendly APIs for over 100 open and proprietary AI models, including text generation, image generation, speech, embeddings, rerankers, and world models. Designed for cost-conscious startups and enterprises, it offers pay-as-you-go pricing with no long-term contracts, zero data retention, and SOC 2/ISO 27001 certifications. The platform runs on inference-optimized US data centers, delivering low-latency performance and high throughput. Key features include support for up to 1M-token context windows (e.g., DeepSeek-V4-Flash at $0.09/M input tokens), fractional cached pricing (up to 80% discount), and private deployments with dedicated support. Recent additions include Step 3.7 Flash (198B MoE vision-language model), NVIDIA Nemotron 3 Ultra, and NVIDIA Cosmos 3 for physical AI. DeepInfra also offers on-demand DGX B300 GPU rentals at $4.20/instance-hour for custom workloads. Compared to generic cloud GPU rentals or other inference APIs, DeepInfra provides a fully managed inference experience with simple APIs, broad model selection, and hands-on technical support.
Behind the Verdict
If you're paying per-token for LLM inference and your volume is climbing, DeepInfra is one of the cheapest ways to get production-grade performance without signing a contract. The per-model pricing table is transparent, caching discounts cut costs significantly, and the model selection now includes everything from DeepSeek-V4 to Gemini and Nemotron. The $107M Series B and NVIDIA participation suggest the infrastructure will keep scaling. Where it bites: no fine-tuning or training, no edge deployment, and data stays in US data centers only. If you need a full MLOps pipeline or require GDPR-explicit data residency in Europe, this isn't the right fit. Also, while the OpenAI SDK compatibility is solid, some niche models don't have clear latency SLAs. Compared to Together AI or Fireworks AI, DeepInfra often edges them on pricing for the same open models, particularly with cached tokens. But those competitors offer more integration examples and stronger community SDKs. For pure inference cost per token, DeepInfra is hard to beat, especially for high-throughput workloads where the caching discount kicks in. In practice, we'd reach for DeepInfra when we need to serve a large-volume chat app, run a RAG pipeline with rerankers, or experiment with cutting-edge open models like DeepSeek-V4 or Qwen3.7 without committing to a long-term contract. For low-latency edge use cases or multimodal generation at scale, you might want to benchmark latency first.
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Real-world workflow fit
Concrete scenarios for the personas DeepInfra actually fits — and what changes day-one when you adopt it.
You are building a customer support chatbot using open-source LLMs and want to minimize costs while handling high throughput.
Outcome: Integrate the OpenAI SDK with DeepInfra's base URL, deploy using DeepSeek-V4-Flash at $0.10/M input tokens, and leverage cached pricing for common customer questions. Achieve 20-50ms response times with no idle GPU costs.
Your team needs to deploy a fine-tuned LoRA adapter for a private LLM with autoscaling and ensure data privacy.
Outcome: Use DeepInfra's private deployments to deploy your model on H100 GPUs with autoscaling, configure a custom SLA, and benefit from zero data retention and SOC 2 compliance. You get a dedicated endpoint with full control over throughput.
You want to experiment with the latest multimodal models (e.g., Step 3.7 Flash) without committing to a subscription.
Outcome: Create a DeepInfra account, generate an API key, and call Step 3.7 Flash via the chat completions endpoint. Pay only for the tokens you use, with no upfront cost. The model supports 256K context and vision capabilities, enabling rich applications.
Use Cases
- Build a chatbot using any open-source LLM via drop-in OpenAI SDK
- Run vision and OCR on documents with Qwen3-VL or Gemini models
- Deploy a custom fine-tuned LLM on a private GPU instance with autoscaling
- Generate images at scale using FLUX or Stable Diffusion APIs
- Create a RAG pipeline using embeddings and reranker models
- Power multi-agent systems with low-cost MoE models like Nemotron 3 Super
Models Under the Hood
as of 2026-07-06
Limitations
- Context windows range up to 1M tokens for some models (e.g., DeepSeek-V4, GLM-5.2, Nemotron-3-Ultra) but may be smaller for others.
- Private deployments require contacting sales and may have minimum commitments.
- No explicit rate limits documented on scraped pages; API limits likely vary by plan.
as of 2026-06-25
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published DeepInfra tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Pay-as-you-go
Per-token pricing
Where the pricing makes sense
The company stage and team size where DeepInfra's pricing actually pencils out — and where peers do it cheaper.
DeepInfra's pay-per-token pricing is ideal for startups and SMBs that want to keep costs low, with no minimums or seat fees. For heavy usage, cached input tokens cost ~20% of full input price. Competitors like Together AI or Fireworks AI may offer comparable prices on some models, but DeepInfra often has the lowest prices for open-source models and offers GPU rental for custom workloads. Enterprises needing dedicated capacity can negotiate volume discounts through private deployments.
Setup time & first value
How long it actually takes to get something useful out of DeepInfra — broken out by persona, not the marketing-page minute.
For most developers, you can make your first API call within 60 seconds by signing up, generating an API key, and using the OpenAI SDK with the base URL https://api.deepinfra.com/v1/openai. Private deployments, GPU clusters, or deep customization may take a few days to set up, including provisioning and configuration.
Switching to or from DeepInfra
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From OpenAI: Replace the base URL in your existing OpenAI SDK with https://api.deepinfra.com/v1/openai and update the API key. Most code works without changes.
- →From Together AI: Update the base URL and API key; DeepInfra supports many of the same open models, but verify model names in their catalog.
- →From Hugging Face Inference Endpoints: Export your fine-tuned model weights, upload to DeepInfra's private deployment portal, and configure autoscaling.
- →From a custom Docker deployment: Use DeepInfra's GPU clusters (DGX B300) with SSH access to run custom containers.
- ↗To OpenAI: Change the base URL back to https://api.openai.com and update your API key; adjust model names as needed.
- ↗To Together AI: Similar drop-in swap with base URL; verify model availability.
- ↗To AWS SageMaker: Export your model artifacts and deploy on SageMaker endpoints; note that SageMaker offers broader MLOps features.
- ↗To Fireworks AI: Update base URL and API key; Fireworks also offers cached pricing and quick deployment.
Integrations
Resources & Guides
- Resourcedeepinfra.com
Blog | Fast & Reliable AI Inference
Discover the latest machine learning models and infrastructure! Learn how to enhance your AI applications, and more!
- Documentationdeepinfra.com
GPU Instances
Rent dedicated B200 GPU instances with SSH access for training, fine-tuning, and custom workloads.
- Documentationdeepinfra.com
Anthropic SDK & Claude Code
Use DeepInfra models with the Anthropic Messages API, Claude Code, and the Anthropic SDK.
Official links
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