API Versioning
The practice of managing changes to an API over time by maintaining multiple versions simultaneously, ensuring existing clients are not broken when the API evolves.
API versioning lets you evolve your API without breaking existing integrations. When you need to make backward-incompatible changes, you release a new API version while continuing to support the old one for a deprecation period. Common versioning strategies include URL path versioning (v1/users, v2/users), header versioning (Accept: application/vnd.api+v2), and query parameter versioning.
The key challenge is supporting multiple versions simultaneously without duplicating all your backend logic. Strategies include maintaining version-specific request/response transformers that adapt between the external API contract and a single internal representation, or using feature flags to control which behavior is active for each version.
For AI APIs specifically, versioning is critical because model changes can alter output format, quality, and behavior. Pinning consumers to specific model versions while rolling out new versions to new integrations prevents unexpected breaking changes. Semantic versioning helps communicate the nature of changes: patch versions for bug fixes, minor versions for additive features, and major versions for breaking changes.
Related Terms
A/B Testing
A controlled experiment comparing two or more variants to determine which performs better on a defined metric, using statistical methods to ensure reliable results.
Feature Flag
A software mechanism that enables or disables features at runtime without deploying new code, used for gradual rollouts, A/B testing, and targeting specific user segments.
MLOps
The set of practices combining machine learning, DevOps, and data engineering to reliably deploy, monitor, and maintain ML models in production.
Model Serving
The infrastructure and systems that host trained ML models and handle inference requests in production, optimizing for latency, throughput, and cost.
Semantic Search
Search that understands the meaning and intent behind a query rather than just matching keywords, typically powered by embedding-based similarity comparison.
CI/CD (Continuous Integration / Continuous Deployment)
An automated software practice where code changes are continuously integrated into a shared repository, tested, and deployed to production, reducing manual intervention and accelerating delivery cycles.