All articles

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.

• Cost to Host Private LLM: Monthly GPU Math by Model Size

  What it costs to host a private LLM on rented GPUs: VRAM sizing for 8B, 70B and 405B models, monthly bills at list prices, and the metric to watch.

  October 7, 2026

• Prompt Injection Detection in Production: What to Alert On

  Where injection detection sits in the serving path, which metric to tune (recall at a fixed false-positive rate), and how to shadow-test it.

  August 24, 2026

• LLMOps Tools on GitHub: Repositories by Stack Layer

  Find LLMOps tools on GitHub by serving, gateway, retrieval, prompts, and release layers, with official repositories and practical adoption checks.

  August 18, 2026

• Serverless vs Dedicated GPU for LLM Hosting

  Serverless GPU containers scale to zero but pay for it in cold starts and lost batching. Here is the duty-cycle math that decides which one is cheaper.

  August 18, 2026

• vLLM vs TGI: Serving, Metrics and Migration

  Compare vLLM vs TGI on documented serving features, maintenance status, latency metrics, and migration checks for production LLM workloads.

  August 3, 2026

• Best LLM Observability Tools for Serving and GPU Cost

  Compare LLM observability tools for serving latency, queue pressure, token costs, and GPU signals, with documented capabilities and selection checks.

  July 20, 2026

• LLM GPU Cost Optimization Techniques That Move the Needle

  Four LLM GPU cost optimization techniques that pay off: quantization, KV cache management, continuous batching, and parallelism, and when each fits.

  July 19, 2026

• Self Hosting LLM vs API Cost: Private Hosting TCO

  Estimate the cost to host a private LLM versus an API, including GPU utilization, retrieval, operating time, and clearly labeled break-even examples.

  June 22, 2026

• Best LLM Serving Frameworks for 2026

  This 2026 guide compares vLLM, SGLang, Ray Serve, and TGI on throughput, TTFT, prefix caching, hardware fit, and operational complexity.

  June 21, 2026

• Best Vector Database for RAG: Filters, Cost and Scale

  Compare six vector databases for RAG on filtered retrieval, hybrid search, deployment choices, and capacity planning using official documentation.

  June 13, 2026

• Semantic Caching for LLMs: Hit Rates and Failure Modes

  Semantic caching uses embedding similarity to improve LLM cache hit rates while requiring careful threshold, tenant scope, TTL, and invalidation controls.

  May 30, 2026

• LLM Eval Pipelines in CI/CD: Quality and Security Gates

  LLM evaluation pipelines use calibrated quality and red-team gates to catch prompt regressions, security risks, and non-deterministic failures in CI/CD.

  May 16, 2026

• Prompt Versioning and Deployment: The Operational Workflow

  Versioning prompts is the easy part. The hard parts are decoupling prompt releases from code deploys and keeping staging and production labels honest.

  May 15, 2026

• RAG Observability: Retrieval Metrics and Production Tracing

  RAG observability uses context precision, context recall, faithfulness, and full-chain tracing to isolate retrieval and generation failures in production.

  May 14, 2026

• LLM Serving Guardrails: Placement and Failure Policy

  LLM serving guardrails as a policy matrix: placement, failure handling, streaming release, telemetry, and the model calls and latency each rail adds.

  May 12, 2026

• LLMOps Best Practices: From Prototype to Production

  LLMOps best practices that separate fragile demos from reliable production systems: prompt versioning, observability, evaluation and cost control.

  May 8, 2026

• LLM Model Registry Patterns: Prompts and Releases

  Design an LLM and prompt registry around immutable versions, deployment aliases, evaluation evidence, and rollback bundles for production releases.

  May 8, 2026

• Token Cost Observability: LLM Token and GPU Economics

  Track LLM token and GPU costs by feature, release, and completed task, with retry accounting, cache savings, rate cards, and idle-capacity allocation.

  May 7, 2026

• Arize vs Evidently for LLM Serving Incidents

  Compare Arize Phoenix and Evidently for LLM serving incidents: request traces, cohort reports, release evidence, and the GPU signals to add separately.

  May 1, 2026

• Concept Drift Detection: Thresholds for LLM Production

  Set concept drift investigation thresholds for LLM production using task cohorts, delayed feedback, versioned releases, and separate serving signals.

  April 28, 2026