Documentation
Learn how to optimize your AI costs and performance with step-by-step implementation guides.
Implementation Guides
Practical, step-by-step guides to help you implement cost-saving and performance-optimizing strategies for your AI applications.
Edge Proxy
Learn how to implement an edge proxy for AI APIs: route traffic, balance load, enforce policies, and cut latency. This Onaro™ guide covers architecture patterns, provider configuration, and safe rollout for high-volume OpenAI and Anthropic workloads.
Circuit Breakers
Add circuit breakers around LLM calls to stop cascading failures, shed load during outages, and avoid runaway spend when APIs degrade. Step-by-step patterns for retries, fallbacks, and observability in production AI systems.
Semantic Caching
Implement semantic caching so similar prompts hit a cache instead of the model—often cutting API cost dramatically. Covers embeddings, similarity thresholds, invalidation, and when caching is safe for your use case.
Model Switching
Route tasks to the right model tier: cheap models for simple work, premium models where quality matters. Practical routing rules, evaluation tips, and examples to lower spend without surprising regressions.
Prompt Compression
Compress prompts and context to cut token usage 30–50% while preserving answer quality: summarization, structured extraction, trimming policies, and measurement so savings show up in your real traffic.
Response Streaming
Stream model responses to users for snappier UX without raising token cost. Covers SSE patterns, client handling, backpressure, and provider-specific streaming options for chat and agent interfaces.
Batch Processing
Batch LLM and embedding jobs to unlock provider batch discounts and simpler rate limits. When to batch, how to chunk inputs, idempotency, and monitoring so throughput goes up and per-token cost goes down.