CCAR-F Domain 2: Latency Optimization & Message Batches API
Architecting for high-throughput and low-latency SLAs: Time-To-First-Token (TTFT) reduction, token pruning, and asynchronous workload processing with the Message Batches API.
1. Anatomy of LLM Latency
End-to-end response time is determined by two distinct phases:
- Time-to-First-Token (TTFT) / Pre-fill Phase: Time required to ingest and process all input context tokens. Prompt Caching slashes TTFT by up to 85% by reusing pre-computed KV-cache states.
- Generation Phase / Inter-Token Latency: Time required to autoregressively output tokens one by one. Output length is the single biggest driver of total generation time.
2. Latency Optimization Architectural Strategies
Key Techniques to Shrink Latency
1. Prune Output Verbosity: Instruct the model to generate concise JSON schemas rather than conversational paragraphs.
2. Model Tiering: Route simple queries to Claude 3.5 Haiku (up to 3x faster generation speed) and reserve Claude 3.5 Sonnet for complex synthesis.
3. Parallel Tool Calling: Execute independent tool queries concurrently in your backend code rather than sequentially.
3. The Message Batches API for Asynchronous Workloads
For tasks that do not require immediate sub-second human responses (such as nightly document classification, bulk evaluations, or content summarization), Anthropic provides the Message Batches API:
| Feature | Standard Messages API | Message Batches API |
|---|---|---|
| Delivery SLA | Real-time (synchronous streaming) | Asynchronous within 24 hours |
| Pricing Discount | Standard pricing | 50% Flat Discount across all tokens |
| Ideal Workloads | Interactive chatbots, live user tools | Nightly batch jobs, dataset labeling, evaluation benchmarks |