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 (19)
- 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: The Open-Source Stack
A layer-by-layer map of the open-source LLMOps stack on GitHub, from serving and gateways to tracing, evaluation and guardrails, plus how to vet a repo.
- 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 Comparison: Throughput, Latency, and EOL Risk
A vLLM vs TGI serving comparison covering PagedAttention, continuous batching, and why TGI's maintenance-mode status now outweighs raw benchmark numbers.
- 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: A TCO Breakdown for 2026
Self hosting LLM vs API cost, broken down: hardware, cloud GPU rental, engineering overhead, and the utilization trap that breaks most breakeven math.
- Serving
Best LLM Serving Frameworks 2026: vLLM, SGLang, and TensorRT-LLM
How vLLM, SGLang, TensorRT-LLM, and Ray Serve stack up on throughput, TTFT, and operational complexity — and which one fits your workload in 2026.
- Platform
Best Vector Database for RAG: A Practical Comparison (2026)
Pinecone, Weaviate, Qdrant, pgvector, Chroma and Milvus compared on the published recall@k, p99 latency, filtered-search and cost figures for each.
- Serving
Semantic Caching for LLM Serving: Hit Rates and Failure Modes
Exact-match caching misses most LLM cache hits — paraphrases tank hit rate. Semantic caching, threshold tuning, and the production failure modes that bite.
- Evaluation
LLM Eval Pipelines in CI/CD: Gates That Actually Catch Things
Running LLM evals in CI is easy to set up and easy to get wrong. How to build quality and red-team gates that block bad prompts before they ever ship.
- 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: Monitoring the Retrieval Layer in Production
When a RAG system gives a bad answer, the retrieval layer is usually to blame — and your LLM monitoring can't see it.
- Evaluation
Guardrails in the Serving Path: Defense in Depth for LLMs
Guardrails are not a single check you bolt on — they're layers in the request path, each catching what the others miss.
- 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
Model Registry Patterns That Actually Work
What the hype skips about model registries, what mature teams actually do, and how to avoid the metadata graveyard most registries become.
- Cost
Token-Cost Observability: What You Measure vs What You Should
Most LLM apps track total spend and call it done. Per-feature cost, per-user attribution and anomaly bands all require deliberate instrumentation.
- Evaluation
Training/Serving Skew: The Silent Killer
How training/serving skew happens, why it's so hard to see, and the specific places to look when your model works in eval and breaks in prod.
- Observability
MLOps Tool Review: Arize vs Evidently
An honest comparison of two ML observability tools—where each fits, where each frustrates, and what neither one solves.
- Observability
Concept Drift Detection in Production: Practical Thresholds
How to actually detect concept drift in live systems, what thresholds matter, and why your monitoring dashboard is probably lying to you.