The right replacement for a hosted open-model API depends on the limit you hit. If concurrency caps or busy-model errors hurt, look at hosts with provisioned capacity or a router that fails over; if billing is hard to forecast, look at flat plans; if you want control, run your own containers or rent GPUs.
You're in production, traffic spikes, and the logs fill with HTTP 429 errors. DeepInfra's rate limit docs, as of Nov 2026, set a default of 200 concurrent requests per model and say you can still see 429s when a model gets very busy, even under your limit, while auto-scaling catches up. That's usually the moment people start comparing DeepInfra alternatives.
It isn't the only reason, so this page sorts nine options by the limit that sent you looking. They're in no particular order, with no crowned pick.
Write down two numbers before you switch
Most hosts here serve the same open-weight models, so leaving rarely changes what the model can do. What changes is the billing unit, how many requests run at once, what happens when a model is busy and where your data goes. Two numbers decide which of those limits you'll hit first on any host.
- Peak concurrent requests. Not requests per day: how many are in flight at the busiest moment.
- Average request time. Long generations hold a slot for longer, so the same traffic needs more concurrency.
- Then compare. Ask each host what limit applies to your model at that peak, and what happens above it.
Billing is the other thing to model. DeepInfra's plans page says some language models bill per token while most other models bill for inference execution time, so a mixed workload is harder to forecast. Write down which models you call and how each host bills them before you compare totals.
Grouped by the limit that sent you looking
You need guaranteed capacity
Together AI lists provisioned throughput with SLAs and dedicated endpoints above its shared serverless tier. Parasail is a smaller inference cloud aimed at AI-native startups. Novita AI describes moving from shared APIs to dedicated clusters on the same platform. For more hosts, see our guide to AI inference platforms.
You want failover when a model is busy
OpenRouter puts one API in front of many models and providers and is built to fall back to another provider when one goes down, per the vendor. It's a router, so the capacity still comes from the hosts behind it.
You want billing you can forecast
Featherless sells plan tiers that differ by context and concurrency, with a dedicated GPU option for teams. Replicate offers one API for a large catalogue of image, speech, video and language models, plus custom deployments. Hyperbolic pairs serverless inference at model-specific token rates with GPU instances under one login; its own page notes requests are subject to availability and rate limits too.
You'd rather run it yourself
Modal runs your own code and containers on GPUs that spin up in seconds, so concurrency is something you size yourself. Lium rents GPU pods by the second, the answer if you were using the GPU rental side rather than the hosted models. Our roundup of cloud GPU providers covers more ways to rent the card.
The nine at a glance
| Tool | Type | Answers which limit |
|---|---|---|
| Featherless | Flat-plan open-model host | Plan tiers with stated concurrency limits |
| Hyperbolic | Inference plus GPU rental | Serverless inference and GPUs under one login |
| Lium | GPU pod rental | GPU pods rented by the second |
| Modal | Serverless GPU platform | Your own code and containers on fast-start GPUs |
| Novita AI | Model APIs plus GPU cloud | Shared APIs that grow into dedicated clusters |
| OpenRouter | Model router | One API with fallback across providers |
| Parasail | Startup inference cloud | New open models quickly for startups |
| Replicate | Model API for media and custom models | Image, speech, video and custom models by API |
| Together AI | Large inference and GPU cloud | Provisioned throughput and dedicated endpoints |
Nine inference options, in no particular order
Featherless
Best for: Developers wanting one API key for a very large open model catalogue, on plans that differ by context and concurrency, with dedicated GPUs for teams.
Hyperbolic
Best for: Teams that want serverless inference with model-specific token rates and on-demand or reserved GPU instances, plus managed Kubernetes, in one account.
Lium
Best for: Engineers who'd rather rent the GPU itself than call a hosted model, with pods billed per second and an API, CLI and Python SDK.
Modal
Best for: Builders who want to serve LLMs or media models from their own code and containers, on GPUs that spin up in seconds, sizing concurrency themselves.
Novita AI
Best for: Startups planning to start on shared model APIs and move to dedicated clusters later, with agent sandboxes, on the same platform.
OpenRouter
Best for: Apps that need one API in front of many models and providers, with routing that falls back to another provider when one goes down.
Parasail
Best for: AI-native startups wanting a smaller inference host that claims day-0 support for new frontier models.
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Replicate
Best for: Products built mostly on image, speech, music or video models, or custom models you deploy, through one API.
Together AI
Best for: Production teams that need capacity they can count on: provisioned throughput with SLAs, dedicated endpoints, batch inference, fine-tuning and GPU clusters.
My plain advice on leaving DeepInfra
If 429s are your only problem, don't move everything. Put a router or a second host behind your own retry logic, so busy moments fail over instead of failing. If you've outgrown shared capacity, move your busiest model to a host with provisioned throughput or a dedicated endpoint, and leave the long tail alone.
If forecasting is the headache, model your real traffic on a flat plan and see whether the limits fit. And if you've got the engineers, running your own containers gives you full control over concurrency, with the operations work that comes with it. Whatever you choose, test with a copy of your real peak traffic, not a handful of sample prompts.
Data handling questions to ask any host
DeepInfra's docs say inputs and outputs aren't stored on disk or used for training, with exceptions: Google and Anthropic models follow those companies' policies, bulk inference may be stored encrypted for a short retention period, and image outputs are kept briefly. Ask every alternative the same questions.
- Are prompts and outputs stored? For how long, and where?
- Are they used for training? Including for models served on behalf of other companies.
- Does batch work differ? Bulk jobs often follow different retention rules.
- Where are servers? Data rules vary by country and sector, so check your regulator's guidance.
Do you make an AI inference API or GPU cloud? Tool makers can ask to be considered for a featured place on this list.
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Frequently asked questions
What is DeepInfra?
An AI inference cloud with an OpenAI-compatible API, hundreds of hosted open-source models, private model deployments and on-demand GPU rental, per its own site.
Why do I get 429 errors on DeepInfra?
Its rate limit docs say 429s happen when you exceed the concurrent request limit for a model, and can also appear when a model is very busy while auto-scaling catches up. Retrying usually helps.
Can I raise my concurrency limit?
DeepInfra's docs say you can ask for a higher limit through the dashboard with context about your use case. Other hosts offer provisioned or dedicated capacity.
What's a model router?
A single API that sends your request to one of several providers serving the same or similar models, and can fall back to another provider if one is down.
Do all inference hosts serve the same models?
Many serve the same open-weight models, so quality is often similar. Limits, billing units, speed and data handling differ more than the models do.
Should I rent GPUs instead of using an inference API?
If you have steady traffic and engineers to run it, renting GPUs gives control. For spiky traffic or small teams, a hosted API is usually less work.
