Start with the deployment model
Together AI's documentation distinguishes serverless inference on a shared fleet from dedicated endpoints billed for allocated hardware. Hugging Face Inference Providers routes requests across multiple providers with centralized access and pricing. These are different operating models, not interchangeable product labels.
Serverless is easiest to justify before demand is predictable. Dedicated capacity trades variable pricing for isolation and control, but economics depend on utilization. Multi-provider access is useful when the product tests many models or wants a portability layer.
Evaluate the hidden constraints
Model availability is only the first filter. Check context limits, structured output, streaming, regional availability, data retention, rate limits, observability, fine-tune support and whether capacity is shared or dedicated. Confirm each requirement in current official documentation before purchase.
Prices and supported models change too quickly for a static ranking to remain reliable. Build a small replay harness against two finalists and store the date, model identifier, latency distribution, error rate and effective cost per successful request.
Use an exit plan as part of procurement
An OpenAI-compatible endpoint can reduce application changes, but compatibility does not make providers identical. Extensions for caching, batch jobs, fine-tuning and observability can still create lock-in. Keep a provider-neutral request layer and regression suite where the expected switching value exceeds the maintenance cost.
The best platform is the one that meets quality, latency, compliance and reliability at the lowest total cost for the current stage. Re-evaluate when volume or model mix changes, instead of treating the initial provider decision as permanent.