Together AI Alternatives (2026): 9 Hosts for Open Models

Open models are portable in a way closed ones aren't, and that's the whole reason a list like this is useful. Together AI is a full-stack AI platform for inference, fine-tuning and GPU clusters, with serverless, batch, provisioned and dedicated inference on its own site. The people who look elsewhere are usually after one of four things: a host with different security paperwork, a different way of buying capacity, lower latency for a live product, or simply a second host so they're not relying on one.

Here are nine hosts for open models, in no particular order. The table comes first because most readers want the shape of the field before the detail.

Open-model hosts at a glance

Tool	Kind of host	Good for
Baseten	Inference for custom models	Your own fine-tuned or custom models in production
Cloudflare Workers AI	Serverless edge inference	Running models on a global network, no scaling to manage
DeepInfra	Pay-per-use model API	A private model API, with dedicated clusters too
Featherless	Serverless model catalogue	Trying lots of open models behind one key
Fireworks AI	Open-model inference and training	Compliance-minded teams that need regional data residency
Groq	Fast inference cloud	Low-latency inference on its own chips
Nebius Token Factory	Open-model inference cloud	Moving from serverless to isolated dedicated endpoints
Novita AI	Model API plus GPU instances	One API now, GPU instances later
Parasail	Open-model inference cloud	Dedicated GPU speed, billed per token

Per token or reserved: how each host sells capacity

This split matters more than raw speed numbers. Pay per token on shared capacity suits early products and uneven traffic: you're billed for what you send and you don't manage hardware. Reserved or dedicated capacity suits steady volume, strict latency targets, or customers who want isolation. Most hosts here now sell both, so the real question is how easy the step between them is.

Shared, per-token catalogues. Featherless and Cloudflare Workers AI sit at the serverless end. One is built for trying a huge range of models; the other runs inference on a global network you may already use for your site.

Serverless first, dedicated when you grow. Fireworks AI lets you start serverless and reserve throughput later. Nebius Token Factory offers the same two routes on its own page. Novita AI describes a path from APIs to dedicated clusters, and DeepInfra pairs its model API with dedicated GPU clusters.

Built around your own models or chips. Baseten is aimed at teams serving custom or fine-tuned models rather than catalogue ones. Groq is the odd one out: it runs inference on chips it designed, which is why it's a name you'll hear in latency conversations.

Nine open-model hosts, listed in no particular order

Baseten

Best for: Serving your own custom models next to open-source ones; its meta description calls it the fastest, most reliable inference platform, a vendor claim.

Cloudflare Workers AI

Best for: Developers already on Cloudflare who want serverless GPU inference on its network, with an OpenAI-compatible endpoint, LoRA support and a batch API in beta.

DeepInfra

Best for: Privacy-conscious API users; its page says inputs, outputs and user data stay private and lists SOC 2 and ISO 27001 certification (vendor claims).

Featherless

Best for: Experimenting across a very large catalogue of open models with a single API key, with a separate dedicated GPU option for businesses.

Fireworks AI

Best for: Regulated teams: one endpoint across many regions with data residency, plus SOC 2 Type 2 and several ISO badges shown on its home page.

Groq

Best for: Latency-sensitive apps, with inference running on its own LPU chips, which the company says it pioneered, now alongside NVIDIA GPUs.

Nebius Token Factory

Best for: Starting on serverless inference, then switching to dedicated endpoints on isolated capacity, with the option to adapt models using your own data.

Novita AI

Best for: Builders who want model APIs billed by the token rather than the hour, with agent sandboxes and a route up to dedicated clusters.

Parasail

Best for: Eligible models on elastic endpoints that give dedicated GPU performance billed per token, with no GPU-hour commitment, through one OpenAI-compatible API.

Straight advice before you change inference host

Don't move for a headline speed claim. Move because a host carries the exact model you use, sells capacity the way your traffic behaves, and meets the paperwork your customers ask for. If you're early and traffic is uneven, stay per token and keep your options open. If volume is steady and latency matters, ask each host about dedicated endpoints and how long they take to set up.

And if you're not tied to open models at all, a router that mixes open and closed models may suit you better; my OpenRouter alternatives list covers those. For a broader look at the whole category, there's also my guide to the best AI inference platforms.

Same model, different host, different answers

This is the part most switching guides skip. An open model with the same name isn't guaranteed to behave the same way on every host. Hosts can serve it at different precision: Together AI's own model pages label some models FP8 or FP4. They also set their own context lengths and rate limits, and Parasail's page says limits depend on your endpoint. So a leaderboard result from one host tells you very little about another.

The fix is boring and it works. Before you switch:

• Write down the exact model name, context length and settings your app uses today.
• Run a set of your real prompts on each shortlisted host and read the outputs side by side.
• Check the rate limits on the plan you'd be on, not the top tier.
• Time responses when your own traffic peaks, not at a quiet hour.

It's an afternoon's work, and it's the difference between a smooth move and a week of users telling you the answers got worse.

Do you make an inference API for open models? Tool makers can ask to be considered for a featured place on this list.

Want your tool on this list? Featured spots and reviews here are paid partnerships. Here's how to partner with me.

Frequently asked questions

What's the difference between serverless and dedicated inference?

Serverless inference runs your requests on shared capacity and bills by use, usually per token. Dedicated inference gives you endpoints or GPUs reserved for you, which suits steady traffic and strict latency targets.

Will my prompts work the same way on another host?

Not always. The same open model can be served at different precision and with different context and rate limits, so outputs can shift. Test your own prompts on each host before you move.

Is an OpenAI-compatible API the same as using OpenAI?

No. It means the host accepts requests in the same format, so you can often switch by changing the base URL, key and model name. The models behind it are still the host's own open models.

Can I fine-tune open models on these hosts?

Several say so on their own pages, whether that's adapting models with your own data or LoRA support. Check that the host can also serve the fine-tuned result, not only train it.

Which security certifications should I ask an inference host about?

That depends on your customers and sector, and requirements vary, so check with your compliance lead or regulator. Some hosts here show SOC 2 and ISO 27001 badges on their pages, which is a starting point, not a guarantee for your use.