If you're leaving a fast-chip inference cloud because of tier limits or a retired model, there are three ways out. Move to another speed-first host on custom chips, move to a wide open-model catalogue with self-serve limits, or run an open-source serving engine yourself so no vendor's retirement calendar can switch your model off.
You built on SambaCloud's API, it was quick, and then something got in the way. Maybe it was a rate limit you can't lift without a sales call, a feature that's Enterprise only, or a notice saying the model your app depends on is leaving the cloud. Those are the three reasons people go looking, and each one has a different fix.
Below are nine places to go next, grouped by the move you want to make. They're in no particular order and nothing's crowned, because a team chasing speed and a team chasing control need very different things.
Where SambaCloud's tiers stop
SambaNova runs open models on its own chips. SambaCloud is the pay-as-you-go API, and SambaStack is the dedicated, sales-led deployment. Its rate-limits policy says the Free tier applies when no payment method is linked, and the Developer tier applies once one is. So the free tier is really the no-card tier: add a card and you move up.
Developer accounts are limited to 20M tokens per day across all models, as of Nov 2026, and higher limits mean contacting sales. The plans page also lists custom rate limits and advanced features such as bring your own cloud as Enterprise only. None of that's unusual for an inference cloud. The real question is whether your next host lets you grow limits on your own, or sends you to the same sales call at the same point. If you want to see how hosts in this space differ more widely, our broader comparison of AI inference platforms is a good place to start.
Three next moves
Stay fast, change chips
If speed is why you chose SambaCloud, you'll probably want another host that builds its own silicon. Cerebras runs inference on its own chips and claims it's up to 30x faster than GPU systems, which is the vendor's own claim and worth checking on your workload. Groq builds its platform around its own LPU chips. Both sell speed first, so read their developer limits before you assume they're looser than the ones you're leaving.
Go wide on open models
If the limits bothered you more than the speed, a big open-model cloud with self-serve tiers is the more natural move. Fireworks AI's docs describe spend tiers whose limits grow as you add credits, which answers the sales-call problem directly. Together AI covers inference, fine-tuning and GPU clusters. DeepInfra keeps things simple with hosted models behind a simple API. Nebius Token Factory adds fine-tuning on your own data, useful if you'd like to adapt a replacement model rather than just swap it.
Two more sit slightly apart. Crusoe Managed Inference pitches predictable spending and resilient scaling, and it keeps a sales path open for teams that want one. Cloudflare Workers AI runs models on serverless GPUs across Cloudflare's network, called from your own code, which suits small projects that already live on that platform.
Own the serving layer
The only way to stop a provider retiring your model is to serve it yourself. vLLM is an open-source engine for serving large language models on your own hardware. You take on the servers, the scaling and the updates, and in return no deprecation notice ever lands in your inbox. Our guide to hosting platforms for running open models covers places to rent that hardware if you don't own it.
Each host in brief, in no particular order
Cerebras
Best for: Developers who want to stay speed-first on a host that runs inference on its own chips, reached through an OpenAI-compatible API.
Cloudflare Workers AI
Best for: Small projects and app builders who want inference on serverless GPUs inside an app platform, with a Free plan listed in its docs as of Nov 2026.
Crusoe Managed Inference
Best for: Teams that want open models run for them by a cloud that pitches fast time-to-first-token and resilient scaling through its MemoryAlloy technology.
DeepInfra
Best for: Developers who'd rather sign up and call hosted machine-learning models through a simple API than negotiate an Enterprise contract for higher limits.
Fireworks AI
Best for: Teams that want to run, ensemble and train open models, with limits its docs say grow as you add credits rather than through a sales call.
Groq
Best for: Speed-focused builders who want infrastructure, inference and control on one integrated platform, as the vendor describes it, built around LPU chips.
Nebius Token Factory
Best for: Anyone losing a retired model who wants a self-serve open-model service on the Nebius AI cloud where they can also fine-tune on their own data.
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Together AI
Best for: Growing products that have outgrown free and developer limits and want inference, fine-tuning and GPU clusters from one AI native cloud.
vLLM
Best for: Engineers who want off every vendor's retirement calendar by serving large language models on their own hardware with an open-source engine started at UC Berkeley.
The nine hosts in one table
| Tool | Kind of host | Good for |
|---|---|---|
| Cerebras | Chip-based fast inference | Speed on custom chips with an OpenAI-compatible API |
| Cloudflare Workers AI | Serverless edge inference | Models called from your own code on a global network |
| Crusoe Managed Inference | Managed open-model inference | Managed open models with a vendor to talk to |
| DeepInfra | Hosted model API | Self-serve hosted models through a simple API |
| Fireworks AI | Open-model inference and training cloud | Run, combine and train open models |
| Groq | Chip-based fast inference | Speed-first platform on its own LPU chips |
| Nebius Token Factory | Managed open-model inference | Serving plus fine-tuning on your own data |
| Together AI | Open-model inference and training cloud | Inference, fine-tuning and GPU clusters in one place |
| vLLM | Self-run open-source engine | Serving models on your own hardware |
My advice on choosing where to move
Be honest about why you're leaving. If it's speed, test a chip-based host on your real prompts and your real output lengths, because headline speed claims rarely match a specific workload. If it's limits, pick a host whose docs say how limits grow without a sales call, and read that page before you write a line of migration code.
If it's a retired model, think one step further. Moving to another hosted API swaps one retirement calendar for another, so either pick a host that tells you its notice period in writing or run the model yourself. Most of these hosts offer an OpenAI-style API, so keep your calls behind your own thin wrapper and switching becomes a config change. Our list of OpenAI-compatible model APIs is useful if you want a few more options with that shape.
Before you migrate a retired model
Here's the part that's easy to miss. A model retired from SambaCloud isn't necessarily gone. SambaNova's deprecation page says models scheduled for cloud deprecation may still be available on SambaStack, which is sales-led, and its 1 Oct 2026 entry moved some models to SambaStack and SambaManaged only. So your real choice is often between a sales conversation and a move to a self-serve host.
The same page promises at least two to three weeks' notice on production models, while preview models may be deprecated at short notice. That's enough time to plan a move rather than rush one, but only if you've built on a production model in the first place. Whichever host you land on, ask for its own deprecation notice period, check whether preview models are treated differently, and keep a second model tested and ready so a retirement notice becomes a routine switch rather than an outage.
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Frequently asked questions
Why would I leave a fast inference cloud?
Usually for one of three reasons: you hit a rate limit that only sales can raise, you need a feature that's on an Enterprise plan, or the model you rely on is being retired from the cloud.
What happens when SambaCloud retires a model?
Its deprecation page says production models get at least two to three weeks' notice and preview models may go at short notice. Some retired models may still be available on SambaStack, its sales-led deployment.
Which alternatives use their own chips?
The two chip-based hosts on this list build their own silicon for inference. Most of the others run open models on GPU clouds, and the open-source engine runs on whatever hardware you give it.
Can I move without rewriting my code?
Often, yes. Many hosts offer an OpenAI-compatible API, so if your calls go through one small wrapper, switching is mostly a change of base address, key and model name. Test outputs, because the same model can behave a little differently on different hosts.
Is running an open-source serving engine myself worth it?
It's worth it if you need full control over which model stays live and you've got the engineering time to run servers. If you don't, a managed host with a clear deprecation policy is usually the better trade.
Do these hosts have free tiers?
Some mention a free trial, a Start free option or a Free plan as of Nov 2026, and the terms vary. Check each vendor's own page before you plan a migration test.
