Compare the four leading LLM observability platforms (Langfuse, LangSmith, Helicone, and Arize Phoenix) with real production patterns for tracing, prompts, and evals.
Circuit breakers, fallback chains, hedging, and bulkheads for LLM apps: patterns that keep OpenAI, Anthropic, and Gemini outages from taking you down.
A production playbook for handling HTTP 429 responses from OpenAI, Anthropic, and Gemini in 2026: retry-after, exponential backoff with jitter, token buckets, circuit breakers, and multi-provider fallback.
OpenAI Batch API and Anthropic Message Batches both cut LLM token costs by 50% in exchange for a 24-hour SLA. Here is when batch wins, working Python for both, and the production pitfalls nobody mentions.