Hosting and Inference
Run model inference through hosted infrastructure and APIs.
Inference providers run supported models through managed infrastructure or APIs. Compare the model class first: text generation, images, and other media can require different endpoints and controls. Hosted APIs and dedicated deployments also imply different operational responsibilities. Benchmark a representative workload for latency, output quality, and expected usage. Check model versions, regions, capacity, and data handling before choosing the service around a headline performance number.
Compare by workflow
- Model coverage
- Deployment options
- Latency
- Usage pricing
Shortlist by use case
| Tool | Consider it for |
|---|---|
| Fal.ai | Generative media platform for developers |
| Groq | Run supported AI models on inference hardware |
| Hugging Face | The AI community building the future |
| Replicate | Run AI models with one line of code |
| Together AI | Build on the AI Native Cloud |
Explore all Hosting and Inference
How to choose
- Benchmark a representative workload with the actual model.
- Check deployment, region, and capacity options before committing.
Frequently asked questions
- How do a managed API and a dedicated deployment differ?
- A managed API exposes supported models through a service endpoint. A dedicated deployment can provide more control over resources and configuration. Availability and operational responsibilities depend on the provider.
- Which workload should I use to compare providers?
- Use your intended model, input sizes, output lengths, concurrency, and model class. Compare the full request path rather than an unrelated benchmark or a different model version.
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