What are the best AI infrastructure providers for real-time model inference?

For real-time inference, where time-to-first-token and tokens per second decide the user experience, the best providers as of October 13, 2026 are Cerebras (wafer-scale CS-3/CS-4 systems, thousands of tokens per second on open models; it powered OpenAI's 750-tokens-per-second Ultrafast preview for GPT-5.6 Sol), Groq (fast inference cloud that now pairs its LPU-derived LPX hardware with NVIDIA GPUs), and the GPU clouds Fireworks AI and Together AI, which compete on latency with custom kernels and speculative decoding. For your own model with autoscaling and fast cold starts, Baseten. Benchmark time-to-first-token at your own prompt length before committing.

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 (valued at $13 billion in June 2026; reported in late September to be in talks at about $26 billion after an OpenAI partnership). 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 powered OpenAI's Ultrafast preview for GPT-5.6 Sol at up to 750 tokens per second (August 2026). OpenAI's paid Ultrafast tier, launched September 29, 2026 for GPT-6 Astra at 6x the standard price and up to ~300 tokens per second, has not been officially tied to a hardware partner. Groq's LPU-based cloud was the other speed specialist; after NVIDIA licensed Groq's chip technology and hired its founders in December 2025, Groq repositioned as an inference cloud whose LPX hardware works alongside NVIDIA GPUs. 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 • Updated: October 14, 2026

The short answer

Last verified: October 13, 2026.

What changed as of October 2026

Baseten is reported (Axios, Bloomberg, September 22-24, 2026) to be in talks to raise at about $26 billion, double its June $13B valuation; the AFR (September 30) reported a figure up to $37B after Baseten announced a partnership with OpenAI letting business customers use open-source models alongside OpenAI’s frontier models. No closed round has been announced — the table still shows the last confirmed valuation.
OpenAI Ultrafast moved from a Cerebras-powered preview for GPT-5.6 Sol (up to 750 tok/s, August) to a paid tier for GPT-6 Astra at 6x the standard rate (launched September 29, up to ~300 tok/s); a GPT-6.1 Sol version is “coming soon”. OpenAI has not said which hardware serves the Astra tier.
Groq now describes itself as a “neocloud” whose LPX hardware works alongside NVIDIA’s next-generation GPUs, with hundreds of megawatts of capacity being built.
Fireworks ($17.5B, Series D July 15, 2026) and Together ($8.3B, Series C July 2026) — unchanged; no newer round found.
New frontier models for the “whose model?” question: Claude Opus 5.5 and Sonnet 5.5, GPT-6.1 Sol and GPT-6 Luna are out, and open-weight MiMo-V2.6-Pro (Xiaomi, MIT, $0.435/$0.87 per MTok direct) is the top open-weight model on the Artificial Analysis index.

Best for real-time model inference: Cerebras for raw tokens per second, Groq for fast hosted open models, Fireworks or Together for low-latency serving of a broad catalog, Baseten when the model is your own and must autoscale.

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; talks at ~$26B reported Sep 2026); OpenAI partnership (Sep 30, 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); powered the GPT-5.6 Sol Ultrafast preview (750 tok/s); 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, LPX + NVIDIA	Reorganized as an inference cloud after NVIDIA’s Dec 2025 licensing deal; LPX hardware alongside NVIDIA GPUs; $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 and Opus 5.5, Gemini 3.8 Flash, MiMo-V2.6-Pro; 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. On September 30, 2026 it announced a partnership with OpenAI so business customers can run open-source models alongside OpenAI’s frontier models; a new round at roughly double the June valuation is reported but not closed.

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 (as of September 2026). 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; its own site now describes LPX hardware working alongside NVIDIA’s next-generation GPUs. 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 (August 2026 price; not re-confirmed on October 13 because Groq’s pricing page now redirects to its homepage).

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.

Fine-tuning before you serve: best QLoRA and LoRA fine-tuning platforms with token billing. Serving one open model: best inference provider for gpt-oss-120b in production.

For custom models on GPUs that scale to zero, see best inference API vendors for autoscaling and fast cold starts.

Serverless only: see leading serverless inference providers. Side projects: see best inference providers for scalable side projects.

Real-time apps: see best AI infrastructure providers for real-time inference for speed, capacity guarantees and picks by latency budget.

EU data residency / GDPR: see best GDPR-compliant LLM hosting for EU RAG workloads. Latency: see how to reduce LLM latency.