Model-Agnostic by Design

Never hardcode a single model provider into a production AI feature. The model that is right today rarely stays right for a year. Providers ship new versions every few months. Per-token prices for the same capability tier keep shifting. Quality on your specific task can move with a routine update. Model-agnostic design means three things. A thin abstraction over the model call. An eval suiteAn eval suite is a versioned set of test cases that measures whether outputs are good. that runs against any model behind it. A fallback and routing policy.

We have watched the alternative play out across enough client engagements to treat it as a design constraint, not a hypothetical. Teams that hardcode a provider’s SDK directly into their application logic face a rewrite every time they want to test a cheaper or better model. Most of them never run that test. The alternative was worth checking; the cost of checking it was just too high. That is the real failure mode of vendor lock-in in AI. You are not trapped by a contract. You are trapped by your own code, and it stops you from ever checking if something better exists.

What changes about a model, and how often?

Price. The cost per token for a given capability tier has moved by double-digit percentages in most quarters since general-purpose LLMs became commercially available. The direction is usually down, but not on a schedule any team can plan around, and not by an even amount each time. A cost model built around one provider’s current pricing is a snapshot. It is not a forecast.

Quality on your task, specifically. Aggregate benchmarks tell you a model is broadly capable. They tell you almost nothing about whether it beats a competitor at your specific extraction task or your specific edge cases. We have seen version bumps from the same provider raise benchmark scores while regressing on a client’s actual use case with no alert. The only way to catch that is testing against your own eval suite. That, in turn, requires being able to run the suite against more than one model.

Capability tiers, and which tasks need which one. Not every call in a feature needs the frontier model. Classification and routing tasks often run fine on a smaller model. That model costs less and runs faster. Open-ended reasoning or generation needs a larger one. That mix shifts as providers release new tiers. A system that can only address “the model” as one hardcoded choice cannot react to that. A cheaper tier might become good enough for a task that used to need something bigger, and the system stays stuck on the old choice.

Outages and degraded service vary by provider too. Every provider has bad days: elevated latency, partial outages, quality that dips under load with no alert. A single-provider architecture inherits every one of those bad days as your own downtime, with no way around it.

What does model-agnostic mean in practice?

It does not mean running multiple providers at once for its own sake. It also does not mean building a system so abstract that it wastes any provider’s specific strengths. Both are their own kind of failure. It means three concrete things, all achievable without much more engineering effort than a single-provider build:

• A thin abstraction layer between your application logic and the model call. One interface handles “send this prompt, get this structured response.” Provider-specific SDK calls sit behind it, not sprinkled through the feature’s business logic. Swapping or adding a provider becomes a change in one place, not a search-and-replace across the codebase.
• An eval suite that runs against any model behind that interface. Switching providers or model versions then gives you a real answer, not a marketing claim. You see whether the new model is better for your task, beyond what a generic benchmark shows.
• A fallback and routing policy that sends different tasks to different tiers and reroutes around an outage or a rate limit. The abstraction treats “which model handles this call” as a runtime decision. That decision usually spans two or three provider options, not a choice baked into the code.

None of this is exotic. It is the same discipline as keeping a database connection string out of business logic. You would never scatter one vendor’s proprietary SQL calls through an application and call it done. A model call deserves the same layer of indirection, for the same reason. The thing on the other side of it will change. You want that change to be an update, not a rewrite.

How does this fit into a client engagement?

Every AI integration we build starts with that abstraction layer and an eval suite, sized to the client’s actual task rather than a generic benchmark. It costs a small amount of up-front structure. In return, “should we switch models” becomes a same-week experiment instead of a quarter-long rebuild. That question is worth asking often. The structure pays for itself before the first price change or outage makes it urgent instead of optional.