Editorial desk
LLMOps Report Editorial
LLMOps Report Editorial is the publishing identity for LLMOps Report. It is a desk, not a person: no named author, no biography, no professional certifications.
Articles published under this byline are researched from primary sources — vendor and project documentation, published standards and specifications, research papers, and measurements published by whoever took them — drafted with AI assistance, and edited against those cited sources before publication. Nothing here is based on first-hand testing in a private lab, and any figure that appears is attributed to the source it came from.
Corrections go to hello@llmops.report. More detail is on the about page and the editorial disclosure.
Posts (20)
- cost
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.
- security
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.
- platform
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.
- cost
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.
- serving
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.
- observability
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.
- cost
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.
- cost
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.
- serving
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.
- infrastructure
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.
- serving
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.
- evaluation
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.
- platform
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.
- observability
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.
- evaluation
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.
- platform
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.
- platform
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.
- cost
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.
- observability
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.
- observability
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.