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Use this page to sanity-check Deepinfra quickly. We surface the evidence tier, freshness, and failure posture here, then put the official links where you can actually act on them, especially on mobile.
Evidence
Assessed
Docs reviewed · Mar 21, 2026
Freshness
Updated 2026-03-21T04:38:28.607705+00:00
Mar 21, 2026
Failures
Clear
No active failures listed
| Dimension | Score | Bar |
|---|---|---|
| Execution Score Measures reliability, idempotency, error ergonomics, latency distribution, and schema stability. | 7.5 | |
| Access Readiness Score Measures how easily an agent can onboard, authenticate, and start using this service autonomously. | 6.9 | |
| Aggregate AN Score Composite score: 70% execution + 30% access readiness. | 7.3 | |
No active failure modes reported.
Published review summaries with trust provenance attached to each card.
Docs-backed Built from public docs and product materials.
Test-backed Backed by guided testing or evaluator-run checks.
Runtime-verified Verified from authenticated runtime evidence.
The API follows the OpenAI chat completions, completions, embeddings, and audio transcription surface — agents use standard OpenAI SDK calls with a DeepInfra endpoint. The model catalog covers major open-source foundation models with frequent additions. The embeddings API covers leading open-source embedding models for RAG and semantic search pipelines.
Reliability is appropriate for a serverless inference platform. DeepInfra maintains warm pools for popular models to avoid cold start latency. Teams using DeepInfra for latency-sensitive applications should verify the warm availability of their specific model choices and implement appropriate timeout and retry logic for inference requests.
DeepInfra is a serverless AI inference platform providing an OpenAI-compatible REST API for running popular open-source models including Llama 3, Mistral, Qwen, Stable Diffusion XL, and Whisper. Its OpenAI-compatible interface means agents using the OpenAI SDK can switch to DeepInfra models by changing the base URL and API key — enabling cost optimization for applications where open-source model quality is sufficient. Per-token pricing without infrastructure management makes it accessible for teams needing model flexibility without GPU cluster operations.
Authentication uses API keys for the inference API. Keys carry account-level billing authorization — usage costs accumulate against the account for all API calls made with the key. Teams should implement per-service key management for applications using multiple AI services to maintain billing attribution and rotation control.
Documentation is clear and focused on the OpenAI compatibility layer with model-specific notes where behavior differs. The model catalog documentation lists per-token pricing for each model, enabling cost comparison across model choices. Teams evaluating DeepInfra versus Together AI, Fireworks AI, or Groq for open-source model inference should compare latency, model availability, and per-token pricing for their specific model requirements.
Trust shortcuts
This score is documentation-derived. Treat it as a docs-based evaluation of API design, auth, error handling, and documentation quality.
Read how the score works, how disputes are handled, and how Rhumb scored itself before launch.
Overall tier
7.3 / 10.0
No alternatives captured yet.