Provider-agnostic AI: choosing between Azure OpenAI, OpenAI, Claude, and open-source models per use case

Teams often pick an AI provider once, early, and then bend every subsequent feature to fit it. That's backwards. Different use cases have genuinely different requirements, and the model that's right for a customer-facing chat feature is often the wrong choice for a back-office document pipeline.

What actually varies between providers

• Data residency and contractual terms — some regulated workloads need a specific cloud boundary (e.g. Azure OpenAI inside your existing Azure tenant).
• Cost per token at your actual volume, which can swing a decision once you're processing millions of documents rather than a demo's worth.
• Latency requirements — a real-time copilot has different tolerances than an overnight batch job.
• Structured-output and tool-use reliability, which varies meaningfully between model families and versions.

Why we don’t default to one model

Locking every feature to a single provider trades away exactly the flexibility that matters most as AI moves fast: pricing changes, new model versions land, and yesterday’s best choice for a use case isn’t always tomorrow’s. Building the retrieval, evaluation, and guardrail layers to be provider-agnostic means swapping the underlying model is a configuration change, not a rewrite.

Where open-source models fit

For workloads with strict data-residency requirements, high volume, or a need to fine-tune on proprietary data, a self-hosted open-source model can be the right call even though it takes more operational work to run. It’s one more option in the set, chosen for the same reasons as any other: does it meet the accuracy, cost, and privacy bar for this specific use case.

How we actually decide

• Define the accuracy bar the use case needs before comparing models against it.
• Test candidates against your real data, not a generic benchmark.
• Price out the decision at your expected production volume, not a pilot’s worth of queries.
• Confirm the data-handling terms match what your customers and regulators actually require.

The result is a system that can use Azure OpenAI for one feature, Anthropic Claude for another, and an open-source model for a third — each chosen deliberately, and each swappable later without re-architecting the product around it.