What is the best LLM inference provider in 2026?

There is no single winner; the best provider depends on the job. For hosted open-weight models with the broadest catalog and fastest new-model support, Fireworks AI and Together AI lead — both crossed $1 billion in annualized revenue in 2026 and were valued at $17.5 billion (Fireworks, July 2026) and $8.3 billion (Together, July 2026). For deploying your own or fine-tuned models on dedicated GPUs with autoscaling, Baseten ($13 billion valuation, June 2026). For the lowest latency, Cerebras and Groq. For the cheapest per-token pricing on popular open models, DeepInfra. For one API across 400+ models from every provider, OpenRouter.

Which inference provider is fastest?

Cerebras. Its wafer-scale CS-3 and (from August 2026) CS-4 systems serve open models at several thousand tokens per second and power OpenAI's Ultrafast mode for GPT-5.6 Sol at roughly 750 tokens per second. Groq's LPU-based cloud was the other speed specialist, but after NVIDIA licensed Groq's chip technology and hired its founders in December 2025, Groq repositioned as an NVIDIA-based inference cloud; it remains fast but no longer has a unique silicon advantage. Among GPU providers, Fireworks and Together compete on time-to-first-token with speculative decoding and custom kernels.

Which is cheapest for open-weight models?

DeepInfra generally lists the lowest per-token prices on models such as DeepSeek V4, Qwen 3.8 and GLM-5.3, with a Flex tier for latency-tolerant traffic. Together and Fireworks are close and add batch APIs at roughly 50% off. Groq's GPT-OSS 120B is $0.15 input / $0.60 output per million tokens as of August 2026. Note that most providers ended free tiers in 2026: Cerebras replaced its free plan with a $5, 30-day trial in July, Together requires a $5 purchase, and Groq removed Llama models from its free plan in August. Always compare tokens-per-task, not just price per token — output-token efficiency varies 3-8x between models.

Should I use OpenRouter instead of a direct provider?

Use OpenRouter when you want one API key and one bill across OpenAI, Anthropic, Google, xAI and the open-model hosts, automatic fallback between providers, and easy model switching in prototypes. Go direct when you need the lowest latency, provider-specific features (Anthropic prompt caching tiers, OpenAI Batch, Gemini Flex), enterprise contracts, or dedicated capacity. OpenRouter adds a small routing margin and a network hop; for high-volume production on a single model, direct is usually cheaper and faster.

Quick Answer

Best LLM Inference Providers 2026: 8 Ranked by Use Case

Published: September 15, 2026

The short answer

Pick by workload, not by brand. As of September 2026 the inference market has sorted into clear lanes:

Rank	Provider	Best for	Why (2026 facts)
1	Fireworks AI	Hosted open models at scale, enterprise	$1B+ ARR, 40T+ tokens/day, $17.5B valuation (Jul 2026); 400+ models; fine-tuning + dedicated deployments
2	Together AI	Open models + training/RL on one cloud	$8.3B valuation (Jul 2026); $240M IBM Cloud deal (Aug 2026) on NVIDIA HGX B300; 200+ models
3	Baseten	Deploying your model (custom, fine-tuned, non-LLM)	$13B valuation (Jun 2026); Truss packaging; per-minute GPU billing (T4 → B200) with scale-to-zero; 1B+ daily calls
4	Cerebras	Lowest latency, highest tokens/sec	Public since May 2026 IPO ($5.55B raised); CS-4 (Aug 2026); powers GPT-5.6 Sol Ultrafast; cloud revenue up ~280% YoY in Q2 2026
5	DeepInfra	Cheapest per-token on popular open models	Consistently lowest list prices; Flex tier for batch-tolerant traffic
6	Groq	Fast open-model serving, now on NVIDIA	Reorganized as an inference cloud after NVIDIA’s Dec 2025 licensing deal; $3.5B valuation (Aug 2026 down round) but ~$1B raised in 2026 for capacity
7	OpenRouter	One API across all providers	400+ models incl. GPT-6 Astra, Claude Fable 5.1, Gemini 3.8 Flash; provider fallback; per-model pricing pass-through
8	Hyperscalers (Bedrock, Vertex AI, Azure AI Foundry)	Compliance, VPC, existing cloud commits	Claude, Gemini and OpenAI models plus open weights inside your cloud account; Cerebras disaggregated inference on Bedrock since Mar 2026

How to choose in five questions

1. Whose model?

A frontier closed model (GPT-6 Astra, Claude Fable 5.1, Gemini 3.8 Flash): go direct to OpenAI, Anthropic or Google, or through your hyperscaler for compliance. Inference clouds cannot host these.
An open-weight model (DeepSeek V4, Qwen 3.8, GLM-5.3, Kimi K3, Muse Spark when released): Fireworks, Together, DeepInfra, Groq, Cerebras.
Your own fine-tune or a non-LLM model (vision, audio, embeddings): Baseten first; Fireworks and Together for LLM fine-tunes on their catalogs.

2. Latency or throughput?

Interactive agents, voice, coding assistants: time-to-first-token and tokens/sec dominate → Cerebras, Groq, then Fireworks/Together.
Bulk extraction, evals, offline pipelines: throughput per dollar dominates → DeepInfra, Together batch, Fireworks batch, hyperscaler batch APIs at ~50% off.

3. Serverless or dedicated?

Serverless per-token is right until you have steady traffic on one model. Past roughly 60-70% utilization of a GPU, dedicated capacity (Baseten, Fireworks on-demand deployments, Together dedicated endpoints) is cheaper and gives predictable latency.

4. Where must data stay?

VPC, HIPAA, FedRAMP, data residency: hyperscalers first; Baseten, Fireworks and Together all offer SOC 2, HIPAA and VPC options, but the paperwork is faster inside a cloud you already contract with.

5. How much model churn do you expect?

If you swap models monthly, OpenRouter (or a gateway such as LiteLLM in front of direct keys) saves integration time. If you have one model for a year, go direct.

Provider notes (September 2026)

Fireworks AI. The biggest independent inference cloud by revenue. Strengths: fastest to host new open releases, FireAttention kernels, fine-tuning, and dedicated GB300/Vera Rubin capacity via partners such as Firmus in Asia. Customers include Samsung, Uber, DoorDash, Notion and Shopify.

Together AI. Positions as an “AI native cloud”: inference plus training and reinforcement learning on the same GPUs. The IBM Cloud agreement (August 11, 2026) brings its stack to IBM’s enterprise base; an Equinix/NVIDIA reference architecture targets on-prem distributed inference. Plans to grow capacity ~50x over five years.

Baseten. The choice when the model is yours. Truss lets you define load() and predict() plus hardware; billing is per GPU-minute with scale-to-zero, and the Baseten Delivery Network cut cold starts 2-3x in 2026. Less convenient if you just want a hosted DeepSeek endpoint.

Cerebras. Wafer-scale inference at thousands of tokens/sec; CS-4 (August 2026) claims 2x CS-3 speed. Public company since May 2026; 600 MW of data-center capacity under contract and a $25.4B backlog. Model catalog is narrower than the GPU clouds; pricing is premium for the speed.

DeepInfra. The budget benchmark. Lowest list prices on the popular open models, a Flex tier for latency-tolerant traffic, and cached-input pricing. Verify TTFT at your prompt lengths before committing agent traffic.

Groq. Still fast, still developer-friendly, but after NVIDIA paid ~$20B to license its LPU technology and hired CEO Jonathan Ross (December 2025), Groq’s cloud runs substantially on NVIDIA hardware. Raised $650M (June 2026) and $350M (August 2026) at a $3.5B valuation to expand capacity. GPT-OSS 120B at $0.15/$0.60 per MTok.

OpenRouter. The universal adapter: one key, unified billing, automatic provider fallback, and access to closed and open models alike. Adds a routing margin; best for prototyping, multi-model apps and small teams that do not want five vendor accounts.

Hyperscalers. Amazon Bedrock, Google Vertex AI and Azure AI Foundry host the frontier models (Claude on all three; Gemini on Vertex; OpenAI on Azure) plus open weights, with the compliance and networking your security team already approved. Regional endpoints typically carry a ~10% premium over the model vendor’s direct API.

Price reality check

List price per token is a weak predictor of spend in 2026. Artificial Analysis measured GPT-6 Astra (high) consuming ~16M output tokens to run its full Intelligence Index versus ~123M for Gemini 3.8 Flash (high) — a 7.7x gap that erases Flash’s ~13x price advantage. The same applies to open models: a chatty model on the cheapest host can cost more per task than a terse one on a pricier host. Benchmark cost per completed task on your own workload before choosing a provider. Reference prices: Best AI model API pricing 2026.

What changed in 2026

Free tiers largely disappeared (Cerebras → $5 trial in July; Together → $5 minimum; Groq trimmed free models in August).
Valuations reset: Fireworks $17.5B and Together $8.3B (up), Groq $3.5B (down), Cerebras public.
Disaggregated inference (separate prefill and decode clusters) went mainstream — Cerebras on Bedrock, NVIDIA Dynamo on the GPU clouds.
Batch and Flex tiers at ~50% off became standard from Google, OpenAI, Together, Fireworks and DeepInfra.

The short answer

How to choose in five questions

Provider notes (September 2026)

Price reality check

What changed in 2026

Related

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