10 Best AI Inference Platforms of 2026

Together AI takes first place because it covers the whole route a team follows when serving open-weight models, and prices each step in public. The serverless API lists per-token rates for current models such as GLM-5.3, Kimi K3, DeepSeek V4.1 Flash and gpt-oss-120B, the last at $0.15 per 1M input tokens and $0.60 per 1M output tokens, with cached input rates shown for many models. The same API handles vision, image, audio, video, transcription, embeddings, rerank and moderation, and Together documents it as OpenAI-compatible. When traffic grows, Provisioned Throughput reserves fixed capacity at $0.05 per PTU per minute, and Dedicated Inference puts a model on single-tenant GPUs, with an H100 at $5.49 an hour. Fine-tuning and GPU clusters sit on the same account. The drawbacks: reserved capacity and several GPU types go through sales, and the dedicated H100 rate is higher than DeepInfra's. For most teams that want breadth and a clear upgrade path, it is the safest default.

Fireworks AI places second on the strength of its serving options. Every serverless request bills on input, cached input and output tokens, and you can switch between Standard, Priority, Fast, Batch and Spot modes per request on the same endpoint. Batch takes 50% off, Spot takes 30% off on best-effort capacity, and cached input is billed at half price or less with no write fee. Prices start at $0.05 per 1M input tokens, and gpt-oss-120B is $0.15 input and $0.60 output. For steady traffic, Reserved Throughput adds a throughput SLA and 99.9% uptime terms, and on-demand GPU deployments bill per GPU second with no charge for start-up time. Fireworks says it serves one endpoint across 30+ regions and is a first-party provider in Microsoft Foundry. The costs to watch are GPU rates, with an H100 at $8.00 an hour, and a 50% premium for US-only serverless on select models.

DeepInfra ranks third because it is the value leader for teams that care most about the bill. Its pricing page lists per-token rates for DeepSeek, Kimi, GLM, Qwen, Llama and Nemotron models, many with cached input prices and temporary discounts, and small models go very low: Llama 3.1 8B Instruct Turbo is $0.02 per 1M input tokens and $0.04 per 1M output. All LLMs use an OpenAI-compatible chat completions API, so switching means changing the base URL and model name. When you need your own model, DeepInfra rents dedicated A100, H100, H200, B200 and B300 GPUs billed at minute granularity, with an H100 at $2.90 per GPU hour and a B200 at $4.89. The docs add a Batch API, LoRA adapters, sandboxes and guides for LangChain, LlamaIndex and CrewAI. It offers less around reserved throughput and uptime guarantees than the top two, and multi-node clusters go through sales.

Baseten is strongest when a team moves its own model into production. Dedicated Inference runs custom models on GPUs from a T4 at about $0.63 an hour to a B200 at about $9.98, billed per minute, and Baseten states 99.99% uptime with capacity spread across 30+ cloud providers and failover during disruptions. It reports more than 15.5 billion inference calls a day. For teams that do not want to deploy anything, Model APIs price current open models per token, including GPT OSS 120B at $0.10 per 1M input tokens and $0.50 per 1M output, which is cheaper than Together or Fireworks for that model. Training compute and agent sandboxes sit on the same platform. The serverless catalog is shorter than Together's or Novita's, and the dedicated H100 rate of about $6.50 an hour is above DeepInfra and Hugging Face. Larger accounts negotiate capacity, regions and SLAs with sales.

Modal is the most flexible option here because you serve models with your own Python code instead of picking from a catalog. You define a function, attach a GPU, and Modal scales containers up and down with request volume, billing GPU, CPU and memory by the second. An H100 costs $0.001097 per second and an L4 $0.000222, and you pay nothing while no container runs. The Starter plan is $0 plus compute and includes $30 of free credits each month, three seats and 10 GPU concurrency. Team costs $250 a month plus compute, includes $100 of credits and raises limits to 50 GPU concurrency and 30 days of log retention. Modal also offers model endpoints for popular open models, sandboxes for agents and training. The trade-off is effort: you choose the engine, handle cold starts and maintain the code, which per-token APIs do for you.

Novita AI earns its spot on price and catalog size. The serverless API covers 200+ models across text, image, audio and video, billed by the token, and the pricing page lists cache read rates for most current LLMs. OpenAI GPT OSS 120B costs $0.05 per 1M input tokens and $0.25 per 1M output, the lowest rate for that model on this list, and small models such as Llama 3.1 8B Instruct start at $0.02 input. Batch inference runs at an introductory 50% discount on supported models. Beyond serverless, Novita sells dedicated endpoints with isolated resources, GPU instances and an agent sandbox billed per second. Its prices are not lowest everywhere: DeepSeek V4 Flash 0731 is $0.44 per 1M input tokens here against $0.06 at DeepInfra. The site states 99.5% uptime, below Baseten's figure, and it publishes less detail on regions and reserved capacity than the top three.

Hugging Face Inference Endpoints is the dedicated-hardware option for teams whose models already live on the Hub. You pick a model, a cloud and an instance, and the service deploys it on autoscaling infrastructure with a choice of engines that includes vLLM, TGI, SGLang, Text Embeddings Inference and llama.cpp, or your own container. Hardware runs from Intel CPUs on AWS at $0.03 an hour, which is where the $0.033 starting price comes from, through Nvidia T4 at $0.50, H100 at $4.50 and B200 at $9.25 an hour, plus TPU v5e on GCP and Inferentia 2 on AWS. The docs cover autoscaling, logs, analytics, AWS PrivateLink and a custom router. The model is billing by instance time rather than by token, so a deployment that never scales down keeps costing money, and setup takes more choices than a serverless API. Serverless access is sold separately as Inference Providers.

Cerebras Inference sells speed. Its Developer tier lists GPT OSS 120B at about 3,000 tokens per second and Qwen 3.8 27B at about 1,850, far above typical GPU serving, through an OpenAI-compatible API at api.cerebras.ai/v1. The docs cover reasoning, streaming, structured outputs, tool calling and prompt caching, with image inputs in preview, and the service is also sold through AWS Marketplace, OpenRouter, Hugging Face and Vercel. Pricing is simple: GPT OSS 120B is $0.35 per 1M input tokens and $0.75 output, and a valid payment method unlocks a one-time $5 credit that expires after 30 days. The limits are real. The self-serve tier offers two models, Cerebras says it is meant for development and evaluation rather than production, and production capacity, higher rate limits and custom weights need an Enterprise quote. The same GPT OSS 120B call is cheaper at Novita, Baseten, Together and Fireworks.

Replicate is the easiest way to call a wide range of generative models through one API, especially for images, video and audio. Thousands of community and official models are available, and its home page shows runs counted in the millions for models such as gpt-image-2 and nano-banana-2. Most public models bill by the time they run on hardware, and some bill per input and output, for example $0.04 per image for FLUX 1.1 Pro. Each model page carries a cost estimate. You can deploy your own model by packaging it with the open source Cog tool. The cost catch is private models: most run on dedicated hardware, so you pay for setup and idle time as well as active requests, except for fast booting fine-tunes. Hardware runs from a CPU at $0.09 an hour to an H100 at $0.001525 per second, which is $5.49 an hour. For cheap LLM tokens the leaders on this list are better value.

Amazon Bedrock is on this list for teams whose infrastructure already runs on AWS. It gives access to hundreds of foundation models, with open-weight families such as DeepSeek, Qwen, Meta Llama and OpenAI models next to proprietary ones, all under AWS identity, security and billing. AWS says more than 100,000 organizations use it. Bedrock bills on-demand inference per token, offers batch inference at a 50% lower price for select models, and supports provisioned throughput. Custom Model Import lets you bring your own weights, such as a fine-tuned Llama, with metering in 5-minute windows and monthly storage charges. Guardrails, evaluation tools and AgentCore cover safety and agents. The trade-offs are cost and effort: prices sit in long per-model, per-region tables we could not confirm in USD from here, setup needs IAM and model access, and dedicated open-model hosts are usually cheaper per token. We list the price as pending.