Portable AI Architecture: Change Models Without Rebuilding Your SaaS

Define requests in product terms: tenant, session, messages, media, allowed tools, response format, deadline, and policy version. Define responses with normalized text, tool calls, usage, finish reason, and safe error categories. Keep provider-specific objects behind an adapter.

Avoid a lowest-common-denominator interface that hides necessary capabilities. Portability means stable application code plus explicit capability discovery, not identical behavior. A workflow requiring vision or structured output should fail configuration validation if the selected model lacks it.

Your graph or workflow engine should manage state, tool permissions, retries, checkpoints, and handoff. The provider adapter handles request translation and response normalization. This division lets business behavior survive an SDK change and keeps provider quirks from spreading across the product.

Store immutable deployment configuration including model alias, parameters, prompt version, tool set, and knowledge version. Resolve aliases server-side so a tenant can use a stable name while operators change the underlying provider through a controlled release.

Classify authentication, quota, rate limit, timeout, safety refusal, invalid input, unavailable model, and provider outage separately. Retry only appropriate failures and respect deadlines. A generic inference error prevents intelligent fallback and makes incident response slow.

Record latency, normalized usage, selected provider, model, workflow, tenant, outcome, and correlation identifier without logging secrets or unnecessary prompts. Cost and token semantics differ, so preserve raw provider usage privately while exposing normalized product metrics cautiously.

Fallback is safe only when the alternate model supports required tools, media, context, region, policy, and output format. Never retry a consequential tool action blindly after an ambiguous timeout. The workflow must know whether inference failed before or after an external side effect.

Choose fallback per workflow and risk level. A FAQ can use another qualified model; a regulated or payment flow may pause and escalate. Test degraded behavior before an outage instead of discovering semantic differences during customer traffic.

Maintain evaluations from real anonymized tasks covering accuracy, tool selection, groundedness, tone, latency, and escalation. Compare candidates on the workflows they will actually run. A model leaderboard cannot represent your knowledge, policies, languages, or integration behavior.

Shadow or canary new deployments for limited tenants and observe quality alongside cost and reliability. Preserve instant rollback to the prior immutable version. Portability is achieved when switching is a measured deployment decision rather than an emergency rewrite.