AI load balancing distributes LLM requests across multiple providers or endpoints to improve reliability, reduce latency, and avoid rate limits. This is essential for high-traffic production applications.
Load Balancing Strategies
- Round-robin: Rotate between providers
- Weighted: Prefer faster/cheaper providers
- Least-connections: Route to least busy
- Geographic: Route to nearest region
Multi-Provider Benefits
- No single point of failure
- Aggregate rate limits across providers
- Optimize cost by routing to cheapest available
- A/B test different models in production
Practical example
Requests are distributed across two model deployments with different capacity.
What to check
Verify health checks, retries, request deduplication, and how a deployment is removed during failure.
Limits and tradeoffs
Two deployments can expose different model versions; distributing traffic can also change answer quality.
Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.