Model-Agnostic by Design

Never hardcode a single model provider into a production AI feature: the model that’s right today rarely is in a year. Providers ship new versions on a cadence measured in months, per-token prices for the same capability tier keep shifting, and 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 suite that runs against any model behind it, and a fallback/routing policy.

We’ve watched the alternative play out across enough client engagements to treat it as a design constraint, not a hypothetical: teams that hardcoded a provider’s SDK directly into their application logic faced a rewrite every time they wanted to test a cheaper or better alternative, and most of them simply didn’t, not because the alternative wasn’t worth evaluating, but because the cost of finding out was too high. That’s the actual failure mode of vendor lock-in in AI: not being trapped by a contract, but being trapped by your own code structure into never checking if something better exists.

What actually 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, generally downward but not uniformly, and not on a schedule any team can plan around. A cost model built around one provider’s current pricing is a snapshot, not a forecast.

Quality on your task, specifically. Aggregate benchmarks tell you a model is broadly capable; they tell you very little about whether it’s better or worse than a competitor at your specific extraction task, your specific tone, your specific edge cases. We’ve seen version bumps from the same provider improve benchmark scores while quietly regressing on a client’s particular use case, and the only way to catch that is testing against your own eval suite, which requires being able to run that suite against more than one model in the first place.

Capability tiers, and which tasks need which one. Not every call in a feature needs the frontier model. Classification, extraction, and routing tasks often run acceptably on a smaller, cheaper, faster model, while open-ended reasoning or generation genuinely benefits from a larger one. That mix shifts as providers release new tiers, and a system that can only address “the model” as a single hardcoded choice can’t take advantage of a cheaper tier becoming good enough for a task that used to need something bigger.

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

What does model-agnostic actually mean in practice?

It doesn’t mean using multiple providers simultaneously for the sake of it, and it doesn’t mean building a system so abstract that it can’t take advantage of any provider’s specific strengths. Both of those are their own kind of failure. It means three concrete things, all achievable without meaningfully more engineering effort than a single-provider build:

• A thin abstraction layer between your application logic and the model call: one interface for “send this prompt, get this structured response,” with the provider-specific SDK calls behind it, not sprinkled through the feature’s business logic. Swapping or adding a provider becomes a change in one place instead of a search-and-replace across the codebase.
• An eval suite that runs against any model behind that interface, so switching providers or model versions produces a real answer to “is this actually better for us” instead of a marketing claim or a benchmark score that may not reflect your task.
• A fallback and routing policy that can send different tasks to different tiers, and reroute around an outage or a rate limit, because the abstraction already treats “which model handles this call” as a decision made at runtime (typically across two or three provider options, not one) rather than baked into the code.

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

How does this fit into a client engagement?

Every AI integration we build goes in with that abstraction layer and an eval suite from the start, sized to the client’s actual task rather than a generic benchmark. It costs a small amount of up-front structure. What it buys is the ability to answer “should we switch models” with a same-week experiment instead of a quarter-long re-architecture — which, given how often that question is worth asking, pays for itself well before the first provider price change or outage makes the question urgent instead of optional.