Topics
Browse everything LLMOps Report publishes by category and tag: every topic covered here, with the most recent guides listed under each one.
Tags
- #llm-serving 6
- #llmops 6
- #production-llm 5
- #vllm 5
- #cost-optimization 4
- #gpu 3
- #inference 3
- #llm-inference 3
- #observability 3
- #self-hosted-llm 3
- #deployment 2
- #gpu-optimization 2
- #guardrails 2
- #infrastructure 2
- #latency 2
- #llm-security 2
- #prompt-injection 2
- #prompt-management 2
- #prompt-versioning 2
- #rag 2
- #token-cost 2
- #arize 1
- #benchmark 1
- #best-practices 1
- #ci-cd 1
- #concept-drift 1
- #cost-allocation 1
- #cost-reduction 1
- #deepeval 1
- #evidently 1
- #github 1
- #gpu-cost 1
- #gpu-metrics 1
- #incident-analysis 1
- #langfuse 1
- #llm-eval 1
- #llm-observability 1
- #llm-production 1
- #llm-registry 1
- #llmops-tools 1
- #nemo-guardrails 1
- #open-source 1
- #prompt-registry 1
- #promptfoo 1
- #quality-thresholds 1
- #quantization 1
- #ragas 1
- #ray-serve 1
- #release-monitoring 1
- #retrieval 1
- #retrieval-augmented-generation 1
- #semantic-caching 1
- #sglang 1
- #tensorrt-llm 1
- #testing 1
- #tgi 1
- #vector-database 1
Categories
cost 5 posts
- Cost to Host Private LLM: Monthly GPU Math by Model SizeWhat 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.
- Serverless vs Dedicated GPU for LLM HostingServerless 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.
- LLM GPU Cost Optimization Techniques That Move the NeedleFour LLM GPU cost optimization techniques that pay off: quantization, KV cache management, continuous batching, and parallelism, and when each fits.
- Self Hosting LLM vs API Cost: Private Hosting TCOEstimate the cost to host a private LLM versus an API, including GPU utilization, retrieval, operating time, and clearly labeled break-even examples.
- Token Cost Observability: LLM Token and GPU EconomicsTrack LLM token and GPU costs by feature, release, and completed task, with retry accounting, cache savings, rate cards, and idle-capacity allocation.
observability 4 posts
- Best LLM Observability Tools for Serving and GPU CostCompare LLM observability tools for serving latency, queue pressure, token costs, and GPU signals, with documented capabilities and selection checks.
- RAG Observability: Retrieval Metrics and Production TracingRAG observability uses context precision, context recall, faithfulness, and full-chain tracing to isolate retrieval and generation failures in production.
- Arize vs Evidently for LLM Serving IncidentsCompare Arize Phoenix and Evidently for LLM serving incidents: request traces, cohort reports, release evidence, and the GPU signals to add separately.
- Concept Drift Detection: Thresholds for LLM ProductionSet concept drift investigation thresholds for LLM production using task cohorts, delayed feedback, versioned releases, and separate serving signals.
platform 4 posts
- LLMOps Tools on GitHub: Repositories by Stack LayerFind LLMOps tools on GitHub by serving, gateway, retrieval, prompts, and release layers, with official repositories and practical adoption checks.
- Prompt Versioning and Deployment: The Operational WorkflowVersioning prompts is the easy part. The hard parts are decoupling prompt releases from code deploys and keeping staging and production labels honest.
- LLMOps Best Practices: From Prototype to ProductionLLMOps best practices that separate fragile demos from reliable production systems: prompt versioning, observability, evaluation and cost control.
- LLM Model Registry Patterns: Prompts and ReleasesDesign an LLM and prompt registry around immutable versions, deployment aliases, evaluation evidence, and rollback bundles for production releases.
serving 3 posts
- vLLM vs TGI: Serving, Metrics and MigrationCompare vLLM vs TGI on documented serving features, maintenance status, latency metrics, and migration checks for production LLM workloads.
- Best LLM Serving Frameworks for 2026This 2026 guide compares vLLM, SGLang, Ray Serve, and TGI on throughput, TTFT, prefix caching, hardware fit, and operational complexity.
- Semantic Caching for LLMs: Hit Rates and Failure ModesSemantic caching uses embedding similarity to improve LLM cache hit rates while requiring careful threshold, tenant scope, TTL, and invalidation controls.
evaluation 2 posts
- LLM Eval Pipelines in CI/CD: Quality and Security GatesLLM evaluation pipelines use calibrated quality and red-team gates to catch prompt regressions, security risks, and non-deterministic failures in CI/CD.
- LLM Serving Guardrails: Placement and Failure PolicyLLM serving guardrails as a policy matrix: placement, failure handling, streaming release, telemetry, and the model calls and latency each rail adds.