The right AI inference platform depends on response speed with your own real prompts, not the headline benchmark, plus which open models it hosts and how it treats your data. This page lists ten inference platforms in no particular order, from serverless token APIs to dedicated deployments for open and custom models.
An AI inference platform runs trained models for you and returns answers through an API. You pick an open model, send requests and the platform handles the GPUs, scaling and serving software behind it.
The ten platforms below are listed alphabetically and in no particular order. Every description comes from the provider's own website, read this week. If you're new to how these models think, my guide to reasoning models explains why some answers take longer than others.
AI inference platforms at a glance
I checked the free plan column on each provider's own website, as of Oct 2026. Yes means the page I read names a free plan, free tier, free credits or open source software. Trial only means a free trial or starter credit. Not stated means the pages I read named neither, even where there's a button inviting you to start, so check the provider's page before you sign up.
| Tool | Type | Good for | Free plan |
|---|---|---|---|
| Cerebras | Wafer-scale AI inference cloud | Very fast responses from open models | Trial only |
| DeepInfra | Model hosting and inference API | Many model types behind one API | Not stated |
| Fireworks AI | Open model inference and training | Running and tuning open models | Not stated |
| FriendliAI | Inference cloud for open-weight models | Extra speed from open-weight models | Not stated |
| Groq | LPU-based inference cloud | Low-latency inference at scale | Not stated |
| Hyperbolic | Open-access GPU and AI cloud | Inference plus on-demand GPUs | Not stated |
| Inference.net | Inference platform for AI-native teams | Moving to tuned open models | Not stated |
| SambaNova | Fast AI inference platform | Serving very large models efficiently | Not stated |
| SiliconFlow | Inference for LLMs and multimodal models | One platform for many model types | Not stated |
| Together AI | Serverless and dedicated inference | Open models through serverless APIs | Not stated |
Ten inference platforms worth a look
Cerebras
Best for: Agent and coding products that want ultrafast inference from wafer-scale hardware, with a free trial to start.
DeepInfra
Best for: Developers who want text, speech, image, embedding and reranker models from one inference API and GPU service.
Fireworks AI
Best for: Businesses that want to run, orchestrate and train open models instead of relying only on closed frontier models.
FriendliAI
Best for: Teams deploying open-weight or custom models who want custom GPU kernels, smart caching and continuous batching handled for them.
Groq
Best for: Real-time apps that want fast inference on Groq's LPU hardware, which now works alongside NVIDIA GPUs.
Hyperbolic
Best for: Researchers who want inference, on-demand GPU compute and managed Kubernetes on one platform built from global capacity.
Inference.net
Best for: AI-native startups that want to switch from closed APIs to fine-tuned open-source models, with tracing and evaluation built in.
SambaNova
Best for: Enterprises that want to serve some of the largest AI models on a dedicated, efficiency-focused inference engine.
SiliconFlow
Best for: Builders who want language and multimodal models through one inference platform with predictable bills at any scale.
Together AI
Best for: Product teams that want serverless, batch or dedicated inference for open-source models, plus GPU clusters as they grow.
Which platform fits the job
- Agents and coding tools that need very fast replies: Cerebras.
- Running and training open models: Fireworks AI.
- Extra speed from open-weight models: FriendliAI.
- Inference plus on-demand GPUs: Hyperbolic.
- Moving from closed APIs to tuned open models: Inference.net.
- Serving very large models: SambaNova.
- Language and multimodal models in one place: SiliconFlow.
Why a fast demo can feel slow in your app
NVIDIA's guide to LLM inference benchmarking says time to first token includes request queuing, prefill and network latency, and grows with longer prompts because of key-value cache computation.
Most speed claims are measured with short prompts. Your product probably sends long ones: a system prompt, a chat history, a pasted document. That pushes up the wait before the first word appears, even on a fast platform. So test with your own prompt lengths, and measure both the time to the first token and the total time to the last one, because users notice the first and finance teams notice the second.
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Serverless tokens, dedicated deployments or batch?
Serverless inference shares hardware across customers. You send requests to a hosted model and you're billed for what you use, which suits early products.
Dedicated deployments reserve GPUs for your model. Response times are steadier and you can run custom or fine-tuned versions.
Batch inference processes large jobs in the background, such as tagging a whole product catalogue overnight, where speed per request matters less than throughput.
Plenty of products use all three. Chat features run on serverless or dedicated inference, while overnight jobs like summarising support tickets go to batch. It's worth asking each platform which of these it offers before you build around one.
Six things to measure in a trial
- Time to first token with your longest normal prompt.
- Total response time for a typical request.
- Error rate when you send many requests at once.
- Which open models and versions are available.
- Whether you can bring a fine-tuned model.
- Where requests and logs are processed and stored.
Do you make an AI inference platform? Tool makers can ask to be considered for a featured place on this list.
My plain advice on choosing an inference platform
Pick two platforms that host the model you want and run the same test on both for a week. Real prompts, real traffic patterns and real error rates will show you more than any leaderboard.
Keep your code provider-neutral where you can, so you can move a model between platforms without rewriting your app. My guide to routing requests through one API shows one way to keep that flexibility.
Frequently asked questions
What is an AI inference platform?
It's a service that runs trained AI models and returns their outputs through an API, so you don't manage the GPUs yourself.
What is time to first token?
It's the wait before the first word of a reply appears. NVIDIA says it includes queuing, prefill and network latency and grows with longer prompts.
Is inference different from training?
Yes. Training builds the model, and inference is running the finished model to answer requests.
Can I run open-source models on these platforms?
Yes. Most platforms on this list focus on open models, and several also let you deploy custom or fine-tuned versions.
Do inference platforms keep my data?
Policies differ, so read each provider's data terms before you send customer information.
Is there a free AI inference platform?
Some offer a free trial or starter credit, but none I read named an unlimited free plan.
