AI Inference API Providers Compared

March 4, 2026 / Arvid Andersson

Running open-source models in production means picking an inference provider. The options have multiplied: some optimize for raw speed, others for cost, and others for breadth of model support. This post compares the major providers across pricing, latency, API compatibility, and what each does best.

The landscape

The big model providers, OpenAI, Anthropic (Claude), Google, and AWS Bedrock, are where most teams start. They offer proprietary models through polished APIs. But when you want to run open-source models, need lower per-token costs, or want more control over your inference stack, a growing set of specialized providers fills the gap.

At one end, providers like Groq compete primarily on speed, using custom hardware (LPUs) to deliver the lowest latency. At the other end, platforms like RunPod and Modal give you raw GPU access to run any model with full control over the stack.

In between sit the serverless inference providers: DeepInfra, Together.ai, Fireworks, and Replicate. These handle the infrastructure and expose models through APIs, typically with OpenAI-compatible endpoints.

Speed vs. cost

Groq leads on raw inference speed thanks to its custom LPU hardware. For applications where time-to-first-token matters (chatbots, interactive agents), Groq's sub-100ms TTFT is hard to beat. The trade-off is a smaller model catalog and higher per-token pricing compared to GPU-based providers.

DeepInfra consistently ranks among the cheapest for per-token pricing on popular models like Llama 3.1 and DeepSeek V3. They run on H100 and A100 GPUs with aggressive optimization. Their average latency (around 0.6s per Artificial Analysis benchmarks) is competitive with GPU-based alternatives.

Together.ai and Fireworks sit in a similar space: competitive pricing, broad model support, and solid performance. Together.ai also offers fine-tuning workflows, which makes it convenient if you need both training and inference on the same platform.

API compatibility and routing

Most providers now support the OpenAI API format, meaning you can switch between them by changing a base URL and API key. DeepInfra, Together.ai, Fireworks, and novita.ai all work with the standard OpenAI Python and Node.js SDKs. This makes it practical to route different models through different providers without rewriting integration code.

OpenRouter takes this a step further by acting as a unified API gateway across multiple providers. You send requests to OpenRouter, and it routes them to the cheapest or fastest provider for a given model. This is useful for teams that want to avoid vendor lock-in or automatically fail over between providers.

Beyond text: image, audio, video

Some providers go beyond LLM inference. Replicate hosts a wide range of open-source models across modalities, from Stable Diffusion to Whisper. fal focuses on fast image and video generation. novita.ai covers LLMs, image generation, video, and audio APIs in one platform. If your application needs multiple model types, consolidating on a multi-modal provider can simplify your infrastructure.

GPU cloud for custom setups

When you need full control, whether for custom models, specific framework versions, or multi-GPU training, GPU cloud providers are the way to go. RunPod offers on-demand and spot GPU instances with per-second billing and no egress fees. Modal takes a code-first approach where you define your environment in Python and Modal handles the provisioning. Baseten and Cerebrium focus on model deployment, letting you package models in containers and serve them as auto-scaling endpoints. For the inference engine itself, vLLM has become the standard open-source serving framework that many of these providers use under the hood.

Provider comparison

How to choose

Start with your constraints. If latency is the primary concern, test Groq first. If cost dominates, benchmark DeepInfra and novita.ai against your expected volume. If you need fine-tuning alongside inference, Together.ai and Fireworks both offer that. If you want a single API across multiple providers, OpenRouter handles the routing. For deploying custom models, Baseten, Cerebrium, RunPod, and Modal each take a different approach to packaging and serving.

The OpenAI-compatible API standard makes it easy to test multiple providers without significant code changes. Many teams run different providers for different use cases: a fast provider for user-facing chat, a cheaper one for batch processing, and a GPU cloud for custom models.