10 Best AI Inference Platforms of 2026

Together AI ranks first because it covers the whole path a team takes when serving open models. You start on serverless APIs that bill per token, with gpt-oss-120B at $0.15 per 1M input tokens and $0.60 per 1M output tokens and a catalog that spans chat, vision, image, video, speech, embeddings and rerank models. When traffic settles you can buy provisioned throughput units with an SLA, or move to dedicated single-tenant GPUs, listed at $5.49 per hour for an HGX H100 and $8.99 for an HGX B200. The API is compatible with the OpenAI REST API and SDKs, so most apps switch by changing the key and base URL. Fine-tuning and post-training sit on the same account, which helps teams that want to serve their own adapted model. The trade-offs are price premiums for US geo-pinned requests, sales-quoted pricing for newer hardware and per-job fine-tuning minimums.

DeepInfra is the value pick in this list. Its pricing page shows some of the lowest token rates we found for popular open models: DeepSeek-V4-Flash-0731 costs $0.06 per 1M input tokens and $0.18 per 1M output tokens with a 1024k context window, and GLM-5.3-Flash costs $0.15 and $0.50. Several models carried discounts when we checked, including 38% off GLM-5.3 and 51% off DeepSeek-V4.1-Flash-1017. The chat completions API is OpenAI-compatible for every language model, so moving an existing app usually means changing the base URL, key and model name. Beyond serverless tokens, DeepInfra offers a Batch API, LoRA adapters, private custom LLM deployments and DGX B300 GPU rentals at $5.89 per instance-hour. It ranks below Together AI and Fireworks AI on features because it publishes fewer service tiers and compliance details, and promotional rates can change, so budget on list prices.

Fireworks AI is the closest rival to Together AI and the stronger pick when you want fine control over each request. Every serverless call bills on input, cached input and output tokens, and you can switch between Standard, Priority, Fast, Batch and Spot modes on the same endpoint: Batch takes 50% off, Spot takes 30% off on best-effort capacity, and Fast targets 100+ tokens per second for interactive apps. gpt-oss-120B costs $0.15 input and $0.60 output per 1M tokens, the cheapest listed model starts at $0.05 per 1M input tokens, and prompt caching is automatic with no write fee. Billing runs on prepaid credits with monthly spend limits. For steady traffic you can reserve throughput with a 99.9% uptime SLA or run On Demand GPUs billed per second. The compliance list is long, including zero data retention by default and HIPAA with a BAA. GPU rates are higher than some rivals, and Spot traffic needs retry logic.

Baseten covers two jobs that many teams need at once. Model APIs serve popular open models per token, with GPT OSS 120B at $0.10 per 1M input tokens and $0.50 per 1M output tokens, the lowest rate for that model in this list, plus Fast variants of GLM and DeepSeek models for latency-sensitive work at a higher price. Dedicated Inference runs your own models on GPUs billed per minute, from a T4 at about $0.63 an hour to an H100 at $6.50 and a B200 at $9.98, and training uses the same rates. Baseten says each model is checked against the lab's published benchmarks and runs at the precision it publishes. Pay as you go needs no contract, while volume discounts, reserved compute and custom regions go through sales. Dedicated deployments need more setup than a serverless call, which is why it scores below the top three on ease of use.

Groq is the pick when response speed matters more than catalog size. GroqCloud runs models on Groq's own LPU hardware and documents about 1,000 tokens per second for GPT OSS 20B and about 500 for GPT OSS 120B. Prices are simple: GPT OSS 20B costs $0.075 per 1M input tokens and $0.30 per 1M output tokens, and GPT OSS 120B costs $0.15 and $0.60. There is a free plan with lower rate limits, and upgrading to the Developer tier only needs a payment method, with spend limits and budget alerts available. The API is OpenAI-compatible, and GPT OSS models get built-in browser search and code execution. The weak spot is the self-serve catalog: when we checked, only a handful of production language models were priced publicly, Llama 3.3 70B and Llama 3.1 8B were marked Enterprise, and dedicated capacity is sold through sales.

Cerebras is the speed specialist. Its docs list about 3,000 tokens per second for gpt-oss-120B and about 1,850 for Qwen 3.8 27B, which is the highest documented output speed in this list. The self-serve Developer tier is pay as you go, with gpt-oss-120B at $0.35 per 1M input tokens and $0.75 per 1M output tokens, and a one-time $5 promotional credit when you add a payment method. Rate limits are generous on the paid tier at 1K requests and 1M input tokens per minute, while the free trial is capped at 5 requests per minute and 1M tokens a day. The catch is scope: only two models are available self-serve, and Cerebras describes the Developer tier as meant for development and evaluation, with production capacity, priority and custom weights on the quote-based Enterprise tier. Per token it costs more than GPU-based rivals for the same model.

SambaNova Cloud is a smaller alternative to Groq and Cerebras that also runs on custom silicon, SambaNova's RDU dataflow chips. The public pricing page listed five models when we checked, with gpt-oss-120b at $0.22 per 1M input tokens and $0.59 per 1M output tokens, MiniMax-M3.1-Flash at $0.30 and $1.20, and GLM-5.3 at $2.80 and $8.80, which is above GPU-based rivals for that model. The API is OpenAI-compatible and documents function calling, JSON mode, vision, embeddings and prompt caching, and custom checkpoints let you serve your own fine-tuned weights. Plans are simple: the Free plan needs a payment method and purchased credits, the Developer plan is pay as you go with standard rate limits, and Enterprise is a subscription with production limits and BYOC. The short catalog keeps it in the middle of the list.

Modal is a different kind of inference platform: instead of mainly calling a hosted model, you deploy your own Python code and Modal runs it on GPUs that start on demand and bill per second. Rates run from $0.000164 per second for an Nvidia T4 to $0.001097 for an H100 SXM5 and $0.001972 for a B300, and you never pay for idle resources. The $0 Starter plan includes $30 a month in compute credits, 3 seats and 10 GPU concurrency, and the Team plan costs $250 a month plus compute with $100 in credits and 50 GPU concurrency. Modal also sells per-token model endpoints for models such as Kimi K3 and GLM 5.3, plus sandboxes and training. It scores lower on ease of use than the serverless APIs above because you own the serving code, but for custom models, multi-step pipelines or unusual architectures that control is the point.

Amazon Bedrock makes sense when your application, data and security controls already live in AWS. It serves open-weight models such as gpt-oss-20b, gpt-oss-120b and NVIDIA Nemotron next to proprietary models, all billed per token through your AWS account and governed by IAM. The pricing page lists NVIDIA Nemotron 3 Super 120B A12B at $0.18 per 1M input tokens and $0.78 per 1M output tokens on the Standard tier, and states that Flex and Batch tiers are 50% cheaper while Priority costs 75% more. Intelligent prompt routing can send each request to the model in a family that is predicted to answer best for the cost. The trade-offs are complexity and price discovery: rates vary by model and region, and onboarding through IAM and the console takes longer than signing up for an API key with Together AI or Groq. Open-model specialists usually list lower token prices for the same models.

Replicate is the broadest catalog in this list but the weakest fit for low-latency, per-token LLM serving. Thousands of community-contributed and proprietary models run through one API, and most are billed by the second of hardware time, from $0.000225 per second on an Nvidia T4 to $0.001525 on an H100. Some models are billed per input and output instead, and each model page shows a cost estimate. You can deploy your own models with Cog, Replicate's open-source packaging tool, and deployments scale up and down automatically. The costs to watch are on private models: most run on dedicated hardware and bill for setup and idle time as well as active time, except fast booting fine-tunes. Token prices on popular LLMs, such as deepseek-r1 at $3.75 per 1M input tokens, are well above the specialists, which is why Replicate ranks last for this audience.