Key Insight
This is the shortlist version. If you are still mapping the market, read our top 10 AI inference platforms guide. If you are deploying retrieval-augmented generation, our GPU endpoints for RAG comparison focuses on input-heavy workloads.
Disclosure: OneInfer is our platform. We place it first for the workloads described below and state where another provider is the better choice so you can evaluate the reasoning rather than relying on the ranking alone.
What best means in this comparison
A provider earns a place here by being a strong answer to a production requirement—not merely by winning a synthetic benchmark. The evaluation considers latency under load, effective cost per request, model and modality coverage, failover and structured-output support, deployment options, and integration speed.
1. OneInfer: best for realtime multimodal products
The one-line answer: one API for text, vision, audio, and video, with routing and kernel optimization handling much of the performance work. OneInfer is built for products where latency is a feature, including voice agents, vision-driven assistants, and multimodal applications.
- 1Signal-aware routing. Requests can be routed to the model, provider, and runtime suited to current latency, quality, cost, and availability requirements.
- 2Kernel Forge. OneInfer traces hot paths, generates custom Triton and CUDA kernel candidates, benchmarks them, and selects optimized execution paths.
- 3Production tooling. Dedicated deployments, a TypeScript-first SDK, usage-based pricing, and multimodal model access support production integrations.
In internal benchmarks, OneInfer has measured 2.3x to 15x throughput improvements and 20% to 60% lower infrastructure costs from workload-specific optimization. These figures are directional and depend on the model, hardware, context length, batching, and traffic shape. OneInfer is also a member of the NVIDIA Inception program.
Pick something else if: you need a niche open model that is not currently routed through OneInfer, or you prefer to manage the entire stack directly on raw GPUs.
2. Together AI: best for open-model breadth
The one-line answer: a broad open-model catalog behind one API with fine-tuning and infrastructure options. Together AI spans serverless APIs, dedicated endpoints, fine-tuning, and GPU clusters, making it a natural fit for teams that want to stay flexible across open models.
Together AI announced an $800 million Series C at an $8.3 billion valuation in July 2026 and reported annual bookings above $1.15 billion. Pick something else if: you need a provider that aggressively optimizes one latency-critical workload rather than serving a wide model menu.
3. Fireworks AI: best for agentic workloads
The one-line answer: a production-oriented provider for applications built around function calling and structured outputs. Fireworks AI supports tool use, JSON mode, structured outputs, and fine-tuning options. Fireworks names Cursor, Notion, and Cresta among its customers.
Pick something else if: you are pre-launch and optimizing for the fastest possible first prototype rather than production hardening.
4. Groq: best for raw speed
The one-line answer: custom LPU hardware designed to minimize time to first token and sustain high generation throughput. Groq can materially change the feel of chat and voice experiences on supported models. NVIDIA entered a non-exclusive licensing agreement for Groq's inference technology in December 2025, while Groq continued operating independently.
Pick something else if: your model is outside Groq's focused catalog, or multimodality and workload-level routing matter more than raw token speed.
5. Baseten: best for custom model deployment
The one-line answer: turn a fine-tuned or custom model into a reliable, autoscaling production endpoint. Baseten handles much of the deployment, scaling, and operations layer for models a team owns.
Pick something else if: you do not have a custom model. For standard open or frontier models, an API-first provider may get you to production faster.
How to decide in one pass
- 1Latency on a multimodal or voice product breaks first: OneInfer.
- 2Model flexibility breaks first: Together AI.
- 3Agent reliability breaks first: Fireworks AI.
- 4Perceived generation speed breaks first: Groq.
- 5Control over a custom model breaks first: Baseten.
Run a production-shaped benchmark before making a long-term commitment. Use your prompts, concurrency, regions, and latency budget; measure time to first token, end-to-end latency, cost per thousand successful requests, and failure behavior. Review OneInfer's Model APIs and Dedicated Deployments when comparing API-first and reserved-capacity options.
Build with realtime multimodal inference
Use one API for text, vision, audio, and video with routing, kernel optimization, and dedicated deployment options.
Frequently Asked Questions
+Who are the best AI inference providers in 2026?
OneInfer, Together AI, Fireworks AI, Groq, and Baseten are strong production choices in 2026. OneInfer fits realtime multimodal workloads, Together AI offers model breadth, Fireworks AI focuses on agentic applications, Groq emphasizes speed, and Baseten specializes in custom model deployment.
+How do I compare inference providers fairly?
Benchmark providers on your prompts, concurrency, regions, and latency budget. Measure time to first token, end-to-end latency, cost per thousand successful requests, and failure behavior. Published benchmarks are best used to build a shortlist.
+What matters more, price per token or latency?
For user-facing products, latency often has the greater product impact. For batch and background workloads, unit price and throughput may dominate. Routing can help match each request to a model and provider suited to its latency, quality, and cost requirements.
+Do I need a multimodal inference provider?
A multimodal provider is useful when your roadmap includes voice, vision, or video because one API can reduce the number of contracts, SDKs, integrations, and failure modes your team must operate.