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Topic guide
LLMOps and cost control
An LLM application is not finished when it answers correctly once. It needs to be observed, evaluated against real questions, and kept inside a budget as usage grows. These articles cover the operating side: what to measure, how to test non-deterministic output, and where the money goes.
The calculators below turn the cost articles into numbers you can plug your own volumes into.
Observe and evaluate
Seeing what the system does in production and checking that its answers stay good.
- LLM Observability in Production: Logs, Traces, and Metrics That MatterWhat to measure, log, and alert on in a production LLM app — latency, token usage, error rates, retrieval quality — and the tools and patterns to get there.4 min read
- Observability for LLM Systems in ProductionHow to instrument, monitor, and alert on LLM apps — distributed tracing, cost dashboards, quality metrics, and incident response for AI systems.4 min read
- You Can't Debug What You Can't See: Observability, Monitoring & Logging in MicroservicesMost microservices teams add logging as an afterthought and call it observability. What separates teams that debug in minutes from teams that debug in days.9 min read
- Testing AI Applications: From Prompts to ProductionA complete testing strategy for LLM apps — unit-testing prompts, building eval pipelines, regression-testing quality, and load-testing AI endpoints.3 min read
- Evaluating RAG Systems: Metrics That Catch Real FailuresYou can't improve a RAG system you can't measure. The metrics that matter — faithfulness, relevance, context precision/recall — and how to build an eval loop.3 min read
Control tokens and spend
Where LLM cost comes from and the levers that reduce it.
- Prompt Caching: Cut Your LLM Costs by 80%A practical guide to Anthropic and OpenAI prompt caching — how it works and how to implement it in Spring AI to cut latency and API costs.4 min read
- LLM Token Budgeting: Sizing Prompts, Context, and CostA practical guide to budgeting tokens in LLM apps — how tokens map to cost and context limits, estimating prompt size, and keeping spend predictable.4 min read
- RAG Chunking Strategies That Actually Improve RetrievalYour RAG quality is capped by how you chunk. A practical comparison of fixed, recursive, semantic, and structural chunking, with sizing and overlap tips.3 min read
- Fine-tuning vs RAG vs Prompting: How to ChooseTeams reach for fine-tuning when they need RAG, or RAG when a better prompt would do. A decision framework for choosing the right approach by problem type.2 min read
Roll out safely and show the value
Releasing changes behind flags, containing failures, and measuring what the work is worth.
- Feature Flags for AI: The Deployment Pattern That Saved Us From 3 Production DisastersWhy LLM features need flags more than anything you've shipped — the four flag patterns that let us swap models, A/B test prompts, and kill broken features fast.8 min read
- Building Fault-Tolerant AI SystemsResilience patterns for production AI — circuit breakers, fallback chains, and graceful degradation so systems survive provider outages and rate limits.3 min read
- AI Coding Tools in Enterprise: What 90 Days of Data Actually ShowsA 90-day study of AI coding tools across a 70-engineer team, measuring PR velocity, defect rates, and ROI. The biggest finding had nothing to do with speed.6 min read
Free tools for this topic
Free calculators and checkers that go with these articles.
- LLM Cost EstimatorEstimate the cost per request and the total for your request volume, from token counts and per-million-token pricing — input and output.
- LLM Token Counter & Context BudgeterPaste any text to estimate its token count and see how much of each model's context window it uses — from 8K to 1M.
- RAG Chunk CalculatorPlan chunk size, overlap, chunk count, and one-time embedding cost for a document corpus.
- AI ROI CalculatorEstimate the annual savings, ROI, and payback period of an AI automation — plug in team size, hours, and costs.
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