Network latency significantly impacts applications that rely on remote vector stores or LLM APIs, as every request-response cycle adds delays. For example, a user querying a cloud-hosted vector database for semantic search might wait hundreds of milliseconds for network round-trip time alone, even if the database processes the request quickly. Similarly, an LLM API call could take 500ms for the model to generate a response, but network latency might add another 200ms, making the total delay noticeable to users. This directly affects user experience in production and complicates performance evaluation, as latency can mask the true processing time of the service.
To mitigate latency in evaluation, measure network overhead separately from service processing time. For instance, track timestamps before/after network calls using tools like time.time() in Python or OpenTelemetry spans. During load testing, simulate realistic network conditions with tools like tc (Traffic Control) on Linux or cloud-based network emulators to inject delays. For vector stores, benchmark local versus remote performance: if a local vector search takes 50ms but the remote version takes 300ms, the 250ms difference highlights network impact. For LLMs, compare API response times against locally hosted smaller models to isolate network latency from computational delays.
In production, implement three key strategies. First, use caching for frequent queries—cache LLM responses for common prompts using Redis or Memcached, with TTLs to balance freshness. For vector searches, cache frequently accessed embeddings. Second, employ asynchronous processing—use async/await in Python or reactive frameworks to prevent blocking the main thread during API calls. For example, process multiple vector search requests concurrently via batch APIs if supported. Third, optimize network payloads: compress embeddings using techniques like product quantization for vector stores, and use protocol buffers instead of JSON for API payloads. For global user bases, deploy services in multi-region cloud setups (e.g., AWS Global Accelerator) to reduce physical distance. Additionally, set timeout thresholds (e.g., 2 seconds for LLM responses) and fallback mechanisms like returning cached results or simplified responses when latency exceeds limits.