Best AI GPU Cloud & Inference Platforms in 2026

1. Modal — best for Python-native serverless GPU deployment

Modal is a serverless, Python-first cloud for running AI and GPU workloads — you write ordinary Python functions, annotate the hardware they need, and Modal handles containers, autoscaling, and networking with no Dockerfiles or Kubernetes required. Billing is usage-based and granular: the free Starter plan includes $30 per month in compute credits, then per-second rates apply across a wide GPU catalog, from Nvidia T4 at $0.000164/sec up to H100 at $0.001097/sec (about $3.95/hour) and Blackwell B200/B300. The Team plan adds a $250/month base fee plus $100 in credits and higher concurrency limits. Functions scale to zero when idle, so workloads that see no traffic cost nothing. It’s built for developers and ML teams deploying inference endpoints, fine-tuning jobs, or GPU batch processing without becoming infrastructure engineers. The honest caveat: Modal’s SDK is Python-only, with no first-class support for other languages.

2. Groq — best for the fastest, cheapest open-model inference

Groq runs open models — Llama 3.1/3.3/4, GPT-OSS, and Qwen3 — on its own custom LPU (Language Processing Unit) chip, built specifically for fast inference rather than general compute. GroqCloud’s free tier requires no credit card and issues an API key immediately, rate-limited to around 30 requests per minute; the pay-as-you-go Developer tier lifts that roughly 10x and adds a 50%-off Batch API and 50%-off prompt caching. Pricing is transparent and low: Llama 3.1 8B costs about $0.05 per 1 million input tokens while running at roughly 840 tokens per second, among the fastest inference available. The OpenAI-compatible API means existing code can point at Groq with little more than a base-URL change. It’s the pick for developers building latency-sensitive chat, voice, or agent products. The caveat: Groq only hosts open and licensed models — there’s no closed frontier model like GPT-4 or Claude-class access, and free-tier rate limits are tight for production traffic.

3. Fireworks AI — best for production-scale open-model inference and fine-tuning

Fireworks AI is an inference and fine-tuning cloud built for open-weight models — DeepSeek, Llama, Qwen, and others — processing more than 30 trillion tokens a day for customers including Cursor, Vercel, and Notion. Serverless inference is billed per token by model size: about $0.10 per 1 million tokens under 4B parameters, $0.20 for 4B-16B, and $0.90 for anything larger, with cached input and batch inference both discounted 50%. Dedicated GPU deployments run from $7.00/hour for an H100 or H200 up to $12.00/hour for a B300, and fine-tuning (LoRA, full-parameter, SFT, DPO) starts at $0.50 per 1 million training tokens. OpenAI- and Anthropic-compatible APIs make migrating existing code straightforward. It suits teams that have already chosen an open model and need a fast, production-grade place to serve and fine-tune it. The caveat: new accounts get only $1 in free credits, leaving little room to evaluate at real scale before paying.

4. Replicate — best for running thousands of pre-built open-source models via API

Replicate runs thousands of open-source AI models — image, video, audio, and language — behind a single API call, handling autoscaling from zero so you don’t provision GPUs or write serving code yourself. Public models are typically billed by output or active processing time — FLUX 1.1 Pro costs $0.04 per generated image — while custom deployments are billed per second of hardware, from CPU at $0.000025/sec up to an Nvidia H100 at $0.001525/sec (about $5.49/hour). Cog, Replicate’s open-source packaging tool, covers fine-tuning and deploying your own model on the same infrastructure. It’s built for developers who want to ship an image, video, audio, or language feature fast without becoming infrastructure engineers. The caveat: there’s no permanent free tier — only limited free runs on a curated model set — and private deployments bill for all online time, including setup and idle periods, not just active inference.

5. Together AI — best for the broadest open-model catalog and full-stack GPU infrastructure

Together AI is a full-stack cloud for open-source models — Llama, DeepSeek, Qwen, Mistral, and 200+ others — spanning serverless inference, fine-tuning, dedicated endpoints, and raw GPU clusters in one account. Serverless inference is billed per token and varies by model: Llama 3.3 70B runs around $0.88 per 1 million tokens, with no monthly minimum. Dedicated single-model endpoints start from $6.49/hour, and on-demand GPU clusters — H100, H200, B200 — start from $2.99/hour on reserved terms, scaling to thousands of GPUs for training. Free signup credits let teams test before committing spend. It fits developers who want the widest open-model selection and the option to move from a hosted API to raw GPU clusters without switching providers. The caveat: per-token rates vary widely by model and change often, making cost forecasting harder than with flat-rate competitors.

6. RunPod — best for cheap raw GPU rental and self-hosted control

RunPod is a GPU cloud for renting Nvidia hardware by the second with no monthly subscription — Pods for persistent instances you SSH into directly, Serverless for autoscaling inference that scales to zero, and Instant Clusters for multi-node training across 200+ GPUs. Secure Cloud on-demand pods run from $0.27/hour for an RTX A5000 up to $7.39/hour for a B300, with a 99.99% uptime SLA; Community Cloud spot pods cost roughly half as much on the same hardware. Serverless inference workers range from $0.58 to $9.98/hour with sub-200ms cold starts. It suits developers and small teams self-hosting open-weight LLMs or Stable Diffusion who want the lowest raw GPU cost and root-level control rather than a managed API. The caveat: Community Cloud spot pods can be interrupted anytime with only a 5-second SIGTERM warning, and unlike Modal’s ongoing monthly credits, RunPod has no standing free tier — only referral-driven signup bonuses.

How we picked

We ranked these six GPU cloud and inference platforms on four criteria: breadth and recency of GPU and model catalog, pricing transparency and value (published per-second or per-token rates versus opaque custom quotes), developer ergonomics (how fast you can go from code to a running deployment), and fit for actually running or deploying a model on GPU compute rather than for consumer chatbot use. Modal, Fireworks AI, Replicate, and Together AI all support deploying, fine-tuning, or packaging your own model; Groq and RunPod are narrower specialists — Groq trades deployment flexibility for the fastest, cheapest inference on a curated open-model set, while RunPod trades a managed API for the lowest raw GPU dollar cost and full root control. Pricing verified on 2026-07-11; no tool paid to appear.