AI Operations & Production explicadores.
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LLMOps: MLOps for the LLM Era
LLMOps is the operational discipline of running LLM apps in production — prompts as code, evals on every change, observability, cost, and incident response.
AI Observability: Tracing Every Token in Production
Without traces, every LLM bug is a guess. Capture prompts, tool calls, tokens, costs, and latencies for every request — searchable, filterable, alertable.
AI Cost Optimization: Cutting LLM Bills 80%
Most LLM bills can be cut by 50–90% without quality loss. Caching, model routing, prompt diet, and output caps deliver the bulk of it.
AI Latency: P50, P99, and Why TTFT Matters Most
Users feel TTFT (time to first token), not total time. Optimise for it. P99 hides the customers who actually churn — track it like your job depends on it.
Semantic Caching: Cache LLM Responses That Mean the Same
A normal cache matches exact keys. A semantic cache matches *meanings* — return the cached answer when the new query is close enough by embedding similarity.
LLM Routing: Right Model for Right Task, With Fallbacks
A router classifies each call and sends it to the cheapest model that handles it. Add fallbacks for outages and you get cheaper *and* more reliable than a single-model setup.
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