Managed inference API
DeepInfra
Shared model APIs with model-specific token, output-unit or execution-time pricing, plus custom LLM/LoRA deployments on selectable dedicated GPUs.
Product facts checked October 2, 2026Editorial scores last reviewed September 3, 2026
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Best for
Cost-first hosting of popular open models (Llama, Qwen, DeepSeek, embedding models)
Watch out
Shared API defaults to 200 concurrent requests per model; burst throughput/rate limits and per-model billing differ. Private deployments use dedicated GPU capacity and GPU-hour billing; verify GPU availability and total instance uptime costs.
| Billing | Shared: model-specific tokens, output units or execution time. Private deployments: GPU-hours with minute-granularity billing, invoiced weekly. |
|---|---|
| GPU choice | Shared APIs manage hardware. Private deployments select A100, H100, H200, B200 or B300 GPUs; dedicated clusters are a separate contact-sales option. |
| Cold start | Not verified for shared endpoints; measure cold-start behavior on the selected model and deployment. |
| Model access | Shared hosted model catalog, custom Hugging Face LLM deployments, language-model LoRA adapters and LoRA image models. Private deployments use dedicated infrastructure and support autoscaling. |
Sources for the dated product and announcement claims
- deepinfra official product and billing documentation — source checked October 2, 2026
- deepinfra official product and billing documentation — source checked October 2, 2026
- deepinfra official product and billing documentation — source checked October 2, 2026
- deepinfra official product and billing documentation — source checked October 2, 2026
Fit detail
When DeepInfra is the right shortlist — and when it is not
Use these lists to kill bad comparisons early, before you compare logos.
Ideal for
- High-volume chat and embedding work on popular open models
- Prototypes that must not carry a monthly minimum
- Teams comparing open-model providers on price
Usually not ideal for
- Requiring raw SSH or custom driver access through a managed model endpoint
- Assuming every model has the same price, context, quota or service tier
- Treating allocated private GPUs as token-only spending
Spend & ops
How cost behaves — and what breaks first
No invented $/hour quotes. These notes explain the failure modes that show up on the first real invoice.
Cost mental model
Shared prices depend on exact model, output unit and service tier. Private GPUs bill allocated running time regardless of traffic, by minute at hourly rates. Compare utilization and replica bounds; this review establishes no cheapest-provider ranking.
Ops notes
- The documented shared limit is 200 concurrent requests per model, not 200 requests per minute; busy models can still return 429 below that limit.
- Choose shared versus private deployment before comparing model tariffs or GPU uptime. Configure private autoscaling and spending limits.
- The B300 memory figure differs between the pricing page (270GB) and private-model overview (288GB); confirm the deployment's actual specification.
Fit scores
DeepInfra on the decision axes
Same editorial scale as the hub chart — useful for shortlists, not as a price quote.
DeepInfra
- GPU choice2/10
- Time-to-serving (editorial)4/10
- Price clarity5/10
- Production ops3/10
- Open-model breadth5/10
Among all 16 providers
GPU choice
Pick exact GPUs
Time-to-serving (editorial)
Warm-path fit
Price clarity
Easy to forecast
Production ops
Less DIY ops
Open-model breadth
Catalog depth
Scores are editorial planning ratings (1–10) for product shape — not published $/hour quotes or vendor SLAs. Verify current pricing on each provider site.
Related
Keep the decision attached to the rest of the stack
Cloud rental is one path. Local VRAM and open-weight fit still matter.