AI Inference API Providers Compared (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.
At a glance
Verified numbers on gpt-oss-120B, a current open-source frontier model, sourced from each provider's pricing page and Artificial Analysis (April 2026). Use these as starting points, not as the final word, since pricing and speed shift month to month.
- Throughput. Cerebras reports ~3000 tokens/sec on gpt-oss-120B. Artificial Analysis measures Groq at ~476 tokens/sec on the same model and DeepInfra Turbo at ~161 tokens/sec. Speeds vary by model and reasoning mode.
- Time to first token. Groq measures 0.60-0.87s across its catalog on Artificial Analysis, consistent across model sizes. DeepInfra Turbo lands around 0.49-0.77s depending on model. Relevant for chat and agent workloads where the first token matters more than sustained throughput.
- Blended price on gpt-oss-120B. DeepInfra lists $0.08/1M blended, Groq $0.26/1M, Cerebras $0.35 input / $0.75 output. Per-token pricing on other models (Kimi K2, Qwen3.5, GLM-5, DeepSeek V3.2) varies widely, so check the provider pages directly.
- Model catalog breadth. DeepInfra currently hosts the widest catalog of current open-source models (Kimi K2 family, Qwen3.5 family, GLM-5, DeepSeek V3.2, MiniMax-M2, gpt-oss-120B, NVIDIA Nemotron). Groq supports gpt-oss-20B/120B, Llama 3.3 70B, Llama 4 Scout, Qwen3 32B, Kimi K2. Cerebras currently lists 4 models.
- Fine-tuning on the same platform. Together.ai and Fireworks both support training and serving through the same API, which matters when iterating on a custom fine-tune.
- Unified routing. OpenRouter exposes one API and routes requests to the underlying provider. Adds one hop and a small markup over going direct, in exchange for avoiding lock-in across providers and models.
- Bring-your-own-container. Modal, Baseten, and RunPod run your own models on GPUs (code-first for Modal, UI-first for Baseten, raw GPU access for RunPod). You manage packaging, cold starts, and scaling in exchange for full control over the stack.
- EU hosting. Nebius, Scaleway, and Mistral La Plateforme run inference inside the EU. See the European providers page for the full list with hosting regions.
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
The speed picture changed in 2026 with the rise of specialized inference hardware. Cerebras reports roughly 3000 tokens/sec on gpt-oss-120B using its WSE chips. Groq runs the same model at around 476 tokens/sec on custom LPUs with a consistently low time-to-first-token (0.6-0.9s across its catalog). The trade-off on both is a narrower model catalog than GPU-based providers, and picking between them depends on whether throughput or first-token latency matters more for the workload.
DeepInfra currently hosts the widest catalog of current open-source frontier models (Kimi K2 family, Qwen3.5 family, GLM-5, DeepSeek V3.2, gpt-oss-120B, MiniMax-M2) and ranks among the cheapest per-token, with gpt-oss-120B at roughly $0.08 per 1M blended tokens on Artificial Analysis. They run on H100 and A100 GPUs with aggressive optimization.
Together.ai and Fireworks sit in a similar space: broad model support and solid performance, with the added option of fine-tuning on the same platform. Together.ai's fine-tuning workflow is convenient when iterating on a custom fine-tune without moving weights to a separate training service.
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
| Provider | Best for | Pricing model | OpenAI-compatible |
|---|---|---|---|
| Cerebras | High sustained throughput (WSE) | Per token | Yes |
| Groq | Low first-token latency (LPU) | Per token | Yes |
| DeepInfra | Low cost, broad model catalog | Per token | Yes |
| Together.ai | Inference + fine-tuning | Per token | Yes |
| Fireworks | Production inference, function calling | Per token | Yes |
| RunPod | Custom models, GPU access | Per second (GPU) | N/A |
| Replicate | Multi-modal, open-source models | Per second (GPU) | No |
| novita.ai | Budget, multi-modal APIs | Per token / per request | Yes |
| OpenRouter | Unified gateway, provider routing | Per token (pass-through) | Yes |
| Baseten | Model deployment, custom serving | Per second (GPU) | N/A |
| Cerebrium | Serverless model deployment | Per second (GPU) | N/A |
How to choose
Start with your constraints. If sustained throughput is the primary concern, test Cerebras and Groq against each other on the specific model you plan to run. If first-token latency drives the experience (chat, interactive agents), Groq's LPU stack has a consistent edge. 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.
Frequently asked questions
What is the cheapest AI inference API?
DeepInfra consistently ranks among the cheapest per-token providers on current open-source frontier models like gpt-oss-120B, Kimi K2, and Qwen3.5. On Artificial Analysis as of April 2026, gpt-oss-120B on DeepInfra is listed at roughly $0.08 per 1M blended tokens. Pricing shifts month to month, so the comparison table above is worth checking before committing to a provider for high-volume workloads.
What is the fastest AI inference API?
It depends on whether sustained throughput or first-token latency matters more. Cerebras reports around 3000 tokens/sec on gpt-oss-120B using WSE hardware, the highest measured throughput in the category as of April 2026. Groq uses custom LPU hardware and runs the same model at ~476 tokens/sec on Artificial Analysis, with a consistently low time-to-first-token (0.6-0.9s) that matters for interactive chat. Both trade off a narrower model catalog than GPU-based providers.
Which AI inference APIs offer a free tier?
Cerebras and Groq both offer free usage with daily token limits, useful for prototyping. Most of the serverless providers (DeepInfra, Together, Fireworks, Novita) hand out signup credits rather than a permanent free tier. Amounts vary and change often, so check each provider page on Infrabase before committing.
Which inference providers are OpenAI-compatible?
DeepInfra, Together.ai, Fireworks, Novita, OpenRouter, and Groq all expose a drop-in OpenAI-compatible endpoint. Switching between them usually means changing the base URL and API key, nothing more. Replicate uses its own API format, and raw GPU providers like RunPod and Modal are not endpoints at all, they host whatever gets deployed to them.
Are there EU-hosted AI inference APIs?
Yes. Nebius, Scaleway, and Mistral La Plateforme run inference inside the EU, which matters for GDPR-sensitive workloads. The full list lives on the European providers page, with hosting regions noted for each.
What is the best alternative to the OpenAI API?
For the highest sustained throughput on open-source models, Cerebras. For the lowest first-token latency, Groq. For the lowest cost per token, DeepInfra. For fine-tuning on the same platform as inference, Together.ai or Fireworks. For routing across providers from a single API, OpenRouter. The comparison table above covers the main trade-offs.
Browse all Inference API providers on Infrabase.ai
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