Best Dedicated Inference Endpoint Providers for Production
The short answer
A dedicated endpoint is a model running on GPUs reserved for you, billed by time instead of by token. It is the step after shared serverless when latency, rate limits or a custom model start to matter. As of October 11, 2026:
- Together AI — best for open models you already call on its serverless API: same model, dedicated GPUs, H100 $5.49 / B200 $8.99 per GPU-hour on demand, plus provisioned throughput units (PTUs) for reserved capacity.
- Fireworks AI — best for high-throughput open-model serving: on-demand deployments on dedicated GPUs, billed per GPU-second, no hard rate limits, cheap preemptible deployments for evals and batch.
- Baseten — best for custom or fine-tuned models with an SLA: H100 $0.10833 per minute (
$6.50/h), B200 $0.16633 per minute ($9.98/h); Enterprise adds custom SLAs and self-hosting in your cloud. - Modal — best value if your team writes its own serving code: about $3.95 per H100-hour, billed per second, scale to zero.
This page is the dedicated-capacity spoke of our best LLM inference providers ranking.
The comparison
| Provider | What you rent | Billing | List price (Oct 11, 2026) | Scaling | Production extras |
|---|---|---|---|---|---|
| Together AI | Dedicated endpoint per model; or PTUs | Per GPU-hour; PTU per minute | H100 $5.49/h · B200 $8.99/h · H200 and GB200 by quote | Replica autoscaling; reserved by contract | Same API as serverless; fine-tuning on the same cloud |
| Fireworks AI | On-demand deployment on dedicated GPUs | Per GPU-second while replicas run | Shown in console and on the pricing page | Min/max replicas; scales to zero after 1 idle hour by default | No hard rate limits; preemptible deployments; custom model upload |
| Baseten | Dedicated deployment of any model (Truss) | Per minute, including deploy and scale-up time | H100 $0.10833/min · B200 $0.16633/min | Autoscaling, scale to zero, fast cold starts | Pro: priority GPUs; Enterprise: custom SLAs, self-host, data residency |
| Modal | Your container on a GPU | Per second | ~$3.95 per H100-hour | Instant autoscaling, scale to zero | Starter $30/mo credits, 10 GPU concurrency; Team $250/mo, 50 GPUs |
| Hyperscalers (Bedrock, Azure, Vertex) | Provisioned throughput on managed models | Hourly or monthly commitment | By model and term | Fixed units | Your cloud account, VPC, existing commit spend |
Picks by workload
Open model in production, steady traffic: Together AI. If you already call GLM-5.3, DeepSeek V4.1 Flash or Qwen 3.8 on Together’s serverless API, a dedicated endpoint moves the same model to GPUs that nobody else shares. PTUs let you reserve a fixed slice of capacity measured in tokens per minute instead of GPUs, which is easier to budget.
High throughput, many replicas: Fireworks. On-demand deployments have no hard rate limits — you are limited only by the replicas you run — and Fireworks applies its batching and KV-cache optimisations underneath. Two cost details: deployments scale to zero after an idle hour unless you set a minimum, and deployments with zero minimum replicas are deleted after seven days without traffic. Preemptible deployments borrow idle reserved GPUs for evaluation and batch jobs at no holding cost.
Custom, fine-tuned or non-LLM models with an SLA: Baseten. You package the model with Truss and push it; Baseten handles autoscaling and cold starts. Billing is per minute and includes deploy and scale-up minutes, so watch replica churn. Enterprise is the tier with custom SLAs, self-host deployments and data-residency control. LoRA-heavy workloads should also read inference providers for LoRA fine-tuned models.
Python team that wants the lowest GPU rate: Modal. You write the serving code (vLLM, SGLang, TensorRT-LLM) yourself and Modal bills by the second. It is the cheapest per H100-hour on this list, but you own throughput tuning. Modal’s own example: 50 GPUs on average for 24 hours at $3.95 costs $4,740, against $5,400 for 75 reserved GPUs at $3.
Compliance-bound enterprise: your hyperscaler. Provisioned throughput on Bedrock or Azure keeps inference in your account and draws down existing commitments, usually at a higher effective price.
Serverless vs dedicated: the break-even
Take DeepSeek V4.1 Flash on Together serverless at $0.30 input / $1.20 output per million tokens (Together’s price, October 11, 2026). A workload of 2 billion input and 400 million output tokens a month costs about $1,080 serverless. One dedicated H100 running all month at $5.49 costs about $4,000. Dedicated wins on cost only when sustained token volume is several times higher, or when you need it for latency, isolation or a custom model. Run the numbers with our self-host vs API break-even guide and current API prices.
Checklist before you sign
- Load-test at your real prompt length; throughput per GPU drops sharply with long contexts.
- Set a minimum replica count for anything user-facing so the first request after a quiet hour is not a cold start.
- Ask for the SLA in writing — only enterprise tiers carry one.
- Check region: data-residency and region-pinned deployments usually cost more.
- Keep a serverless fallback for overflow traffic.
Related: best inference API vendors for autoscaling and cold starts and best providers for real-time inference.
Last verified: October 11, 2026.