Inference Provider Comparison: Serving Models Without Running Them

Inference providers serve open-weight models without requiring infrastructure. This guide covers comparing them, drawing on FISTA Solutions' AI enablement delivery work.

What should the comparison cover?

Six dimensions, with price last.

Dimension	What to verify	Why it matters
Model and version availability	How long versions persist	Forced migrations
Latency	Tail, under your concurrency	User experience
Rate limits	Against projected peak	Binds before price
Version pinning	Explicit version selection	Silent behaviour change
Data handling	Retention, training, location	Compliance
Pricing	Per token, at your volume	One factor among several

Why does version longevity matter?

Because withdrawal forces work on someone else's schedule.

A provider that rotates models quickly means periodic migration: re-evaluating, adjusting prompts, and revalidating. A provider keeping versions available for long periods removes that recurring cost.

For regulated processes requiring a frozen model, this may be decisive regardless of price. Ask about the deprecation policy and check the historical behaviour. See how to run a model migration.

How should latency be measured?

At the tail, under your real concurrency, over several days.

Providers serving the same open-weight model can differ substantially in latency depending on their infrastructure and load. Published figures are measured in favourable conditions.

Measure time to first token and total completion at the ninety-ninth percentile, at your expected concurrency, repeated across days. See AI performance tuning checklist.

Why do rate limits bind first?

Because they are set conservatively and peak traffic is spiky.

A limit comfortable for average volume can be exceeded during a busy hour, producing throttling exactly when it hurts. Check both request and token limits against projected peak rather than average.

Also check the lead time to raise them, since that is a planning constraint. See AI capacity planning checklist.

What does pinning protect against?

Behaviour changing with no change on your side.

If a provider updates the model behind an alias, your outputs shift and nothing in your change log explains it. Explicit version selection means you choose when to move.

Check whether pinning is available and how long a pinned version remains. See AI model change log template.

What data terms should be read?

Retention, training use, processing location, and logging.

Providers serving the same open-weight model differ in these, sometimes considerably. Whether your inputs are retained, for how long, and whether they contribute to anything else are questions with different answers per provider.

For sensitive workloads, region restriction and zero-retention options may be requirements. See AI subprocessor checklist.

Where does pricing sit?

As one factor, weighted by your volume.

Per-token pricing varies between providers for the same model, sometimes substantially. At high volume that matters; at moderate volume, latency and reliability matter more.

Model cost at your projected volume including output tokens, which are usually priced higher and are easy to underestimate. See LLM cost control checklist.

How do you run your own comparison?

Run your evaluation suite against each provider serving the same model and check the outputs are comparable — implementations can differ. Then measure latency at the tail under concurrency over several days.

Finally, test throttling behaviour by exceeding the rate limit deliberately, so you know what production will look like at peak.

What does switching cost later?

Low, which is the main appeal. The same open weights served elsewhere means switching is a configuration change plus re-evaluation.

Keep an abstraction over the provider and your evaluation suite ready, and switching is a day's work.

What do people get wrong here?

Comparing on price alone. Latency measured serially. Rate limits checked against average volume. No version pinning. And data terms assumed to match another provider serving the same model.

How does this compare with self-hosting?

It removes the operational burden while keeping model portability, which is what most organisations actually want.

Self-hosting adds control over residency and version freezing at substantial operational cost. Unless one of those is a requirement, a hosted open-weight endpoint is usually the better trade. See open weight vs hosted models for enterprise.

Which should you choose?

Compare on version longevity, tail latency under real concurrency, and rate limits against peak, with data terms checked per provider. Price matters at volume and rarely decides it alone.

What should you do first?

Run your evaluation suite against two providers serving the same model. Comparable outputs confirm portability; differences tell you the implementations are not identical.

How FISTA Solutions helps

FISTA Solutions builds and operates production AI systems through AI agents, AI enablement, and forward deployed engineering: providers compared on version longevity and tail latency under real concurrency, with throttling behaviour tested before it occurs at peak, decisions documented with their reasoning, and handover that leaves your team able to maintain what was delivered. The record is 150+ projects for 50+ companies across 12+ countries.

To run this comparison against your own workload, message FISTA on WhatsApp, or read open weight vs hosted models for enterprise.