Ship LLMs that actually work.
Production engineering for LLM systems. Evaluation pipelines, online observability, cost and latency tradeoffs, prompt-version drift, A/B on real traffic, and the cases where the LLM-stack hype crashes into the operational reality.
Prompt Injection Detection in Production: What to Alert On
read →// start here
The reference set
The feed above is ordered by date. These are the pieces worth reading first regardless of when they were published, plus the calculator that turns the cost arguments into a number for your own traffic.
- LLMOps Best Practices: From Prototype to Production
The operating habits that separate a demo from a system.
- LLMOps Tools on GitHub: The Open-Source Stack
Every layer of the open-source stack, and who occupies it.
- vLLM vs TGI Serving Comparison: Throughput, Latency, and EOL Risk
Throughput, tail latency, and the maintenance-mode problem.
- Self Hosting LLM vs API Cost: A TCO Breakdown for 2026
Where the build-versus-buy line actually falls.
- Token-Cost Observability: What You Measure vs What You Should
Attributing spend to features, tenants, and users.
- LLM Eval Pipelines in CI/CD: Gates That Actually Catch Things
Making a bad prompt fail the build instead of the user.
- Model Registry Patterns That Actually Work
Knowing which artifact version is live, and who approved it.
- Concept Drift Detection in Production: Practical Thresholds
Catching quality decay before the support queue does.
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LLM Cost & Latency Estimator
Monthly spend, p50/p95 latency and cost per user, computed in your browser.
Independent, specialist, and free to read
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