Choosing an AI model hosting & inference provider
Together AI, Fireworks, Replicate, Groq, Modal, and Baseten are all usage-billed compute for running models — the real differences are latency, which models are actually hosted, and how much infra you want to manage yourself.
Crail Editorial · Published 2026-07-23 · Last verified 2026-07-27
If you’re running an open-weight model rather than calling a closed frontier model’s own API, someone has to actually host and serve it — that’s this category. All six vendors Crail tracks here bill on usage, so the pricing comparison matters less than fit for your workload.
What actually differentiates them
Raw inference speed. Groq’s whole pitch is custom silicon (LPUs) for very low-latency token generation — worth it specifically if latency is your bottleneck, less relevant if you’re doing batch/offline work.
Serverless vs. dedicated compute control. Modal and Baseten both lean toward “bring your own custom model/pipeline and we manage the infrastructure,” which suits teams with non-standard serving needs (custom pre/post-processing, fine-tuned models) rather than just calling a hosted open-weight model over an API.
Breadth of pre-hosted open models. Together AI, Fireworks, and Replicate all offer a large menu of ready-to-call open models with no setup — the right fit if you want to swap between Llama, Mixtral, or similar without standing up your own serving infrastructure.
A practical shortlist process
- Are you calling a standard open-weight model, or running something custom? Standard model → Together/Fireworks/Replicate. Custom pipeline or fine-tune → Modal/Baseten.
- Is latency the binding constraint? If yes, benchmark Groq specifically against your actual workload rather than assuming it wins by default — LPU speedups are workload-dependent.
- Check self-serve access before committing. All six are usage-billed, but self-serve API key issuance and sandbox availability still vary — see each vendor’s agent-readiness breakdown on Crail for the specifics rather than assuming.
Where to start
Full pricing, deployment options, and agent-readiness scores for all six are on the AI Model Hosting & Inference category page.
FAQ
Is this the same thing as an AI gateway like Portkey or LiteLLM?
No — a gateway routes requests across providers you already have accounts with. A model-hosting/inference provider is where the model actually runs; you'd often use a gateway *in front of* one or more of these.
Do I need a specialized inference provider, or can I just use the model vendor's own API?
For frontier closed models (Claude, GPT), you use the vendor's own API directly. Inference providers matter most for open-weight models (Llama, Mixtral, etc.) where you're choosing who serves the model, not the model vendor itself.