Guide · Dubai · MENA

How to choose an LLM provider: pick on the constraints that actually bite

Founders ask me this question earlier than almost any other, often before they've written a line of the product. That's backwards. The choice matters, but less than most teams think at the start, and in different ways than a benchmark leaderboard suggests.

Why benchmark leaderboards are the wrong way to pick

Every leaderboard ranks models on one axis: quality on some fixed test set. Your product runs on four axes at once: quality, latency, cost, and reliability. You rarely get to optimise more than one or two of these, because they trade against each other. A model that scores two points higher on a reasoning benchmark might cost three times more per request or take twice as long to respond, and your users will feel the latency and the cost before they ever notice the quality gap. Pick your provider against the constraint that actually breaks your product if it fails, not the one that looks best in a chart.

For a support assistant, latency usually wins: users abandon a slow reply regardless of how good it eventually is. For a document analysis tool run in the background, quality wins and latency barely matters. For a high-volume feature on a low-margin plan, cost sets the ceiling on which models are viable. Decide which axis your product depends on before you look at a single benchmark.

The three provider categories founders choose between

Frontier APIs (Anthropic, OpenAI, Google) give you the strongest general-purpose models with the least operational work. You call an endpoint and get a response. For most early-stage products this is the right starting point: you're testing whether the feature works at all, and you want the fewest variables between you and that answer. The tradeoff is cost at scale and less control over the exact weights you're running.

Open-model hosts (Together, Fireworks, and similar) run open-weight models like Llama or Qwen on managed infrastructure. You get lower per-token cost and more control over which version you're pinned to, at the price of more evaluation work, since open models vary more in quality by task than the frontier labs' flagship models do. This category earns its complexity once you know your use case and volume, not before.

Routers (OpenRouter and similar) sit in front of multiple providers behind one API, letting you switch models by changing a string instead of rewriting integration code. They're useful for experimentation and as a fallback layer, but add a hop of latency and another vendor in your reliability chain. I use them for prototyping and fallback routing, rarely as the primary path in a production feature where every millisecond is accounted for.

Most startup products end up on a frontier API for the core experience and add a router or a second provider only once a specific cost or latency problem shows up. Don't reach for the more complex setup before you have that problem.

The operational details that bite later, not on day one

The choice that looks settled at launch rarely stays settled. A few things catch teams off guard months in:

Rate limits. Free and low tiers cap requests per minute in ways that work fine in testing, then throttle your product the day a feature goes viral or a demo brings a room full of simultaneous users.
Model deprecation. Providers retire versions on their own schedule, not yours. A prompt tuned against one version can behave differently against its replacement, sometimes subtly enough that nobody notices until quality has already dropped in production.
Version pinning. Pin the exact version your evals ran against, and treat any upgrade as a change to test, not one to accept silently.
A wired-up fallback. When your primary provider has an outage, and every provider eventually does, a fallback model already integrated and tested is the difference between a five-minute blip and a multi-hour incident.

None of these show up in a benchmark comparison. They show up in an incident review months after launch, which is why they belong in the decision from day one.

How to build so switching stays cheap

The real insurance against picking wrong isn't picking perfectly. It's building so that a wrong pick costs you an afternoon instead of a rewrite. Two things make that true.

First, a thin abstraction layer between your product code and the specific provider SDK. It doesn't need to be elaborate: one function that takes a prompt and returns a response, with the provider-specific details behind it. This keeps your business logic from depending on any one vendor's API shape.

Second, an eval suite that travels with you across providers. This is the part teams skip, and the part that actually protects you. Without a fixed set of test cases and a way to score them, you can't tell whether a new model or provider is actually better for your use case, you're only guessing from a handful of manual spot checks. I've watched teams switch providers to chase a lower headline price and ship a quality regression nobody caught for weeks, because there was no eval set to catch it first. If you haven't built one, start there before you touch the provider question again. See our LLM evals guide for how to set up a first eval set.

What I default to on client builds

On most early-stage builds, I start on a frontier API, wire up a fallback model from day one even if it's never called, and treat the eval suite as a build requirement, not something added after launch. I don't move a client to a cheaper open-model host until the eval suite shows it holds up on their actual traffic, not a generic benchmark. That ordering, evals before optimisation, is the habit that has saved every client I've worked with from a quiet quality regression they'd otherwise have shipped without noticing.

LLM provider decision checklist

Which single constraint (quality, latency, cost, reliability) actually breaks your product if it fails?
Have you built an eval set on your own use case, not a public benchmark?
Do you know your expected request volume, and have you checked the provider's rate limits against it?
Is a fallback model already integrated, even if it's rarely called?
Are you pinned to a specific model version, with a plan to re-run evals before any upgrade?
Does your product code sit behind an abstraction layer, or does it call the provider SDK directly?

Run through this in an afternoon before committing a product to a provider. It's cheaper than the alternative.

FAQ

Should an early-stage startup use a frontier API or an open-model host? Start with a frontier API. You're still validating the feature itself, and you want the fewest variables between you and that answer. Move to an open-model host only once your eval suite shows a specific cost or latency problem that switching would actually solve.

Is it worth building a multi-provider abstraction layer from day one? A thin one, yes. It doesn't need to support every provider on day one, it just needs to keep your business logic from being hard-wired to one vendor's SDK, so a future switch is a config change, not a rewrite.

How often should you re-evaluate your LLM provider choice? Whenever a provider deprecates the model version you're pinned to, or when your eval scores drift on real traffic. Treat it as an event-driven check, not a fixed calendar review.

Do routers like OpenRouter add meaningful latency? Yes, a router adds a network hop and another point of failure. It's a reasonable choice for experimentation and fallback routing, but for a latency-sensitive core feature, calling the provider directly is usually the safer default.

What's the biggest mistake founders make choosing an LLM provider? Picking on a benchmark score instead of the constraint that actually threatens the product. The second most common mistake is switching providers to save on cost without an eval suite to catch the quality regression that switch can quietly introduce.

Want a second opinion on your provider setup before you commit to it? Book an intro chat.

For more on how AI-first teams are building and shipping faster, see Scalable News.