LLMOps

LLMOps is the set of practices, tools, and infrastructure for deploying, monitoring, evaluating, and governing large language models in production. It builds on the principles of MLOps but adapts them for the runtime-centric demands of LLM applications. It is the operational discipline that sits between "the model works in a notebook" and "the model works reliably for 10,000 users."

How LLMOps differs from MLOps

MLOps grew out of the need to manage training pipelines, model registries, and batch inference for traditional machine learning. LLMOps addresses a different set of problems. Most teams using LLMs are not training models from scratch -- they are calling APIs from OpenAI, Anthropic, or Google, and building applications on top of those APIs. The operational concerns shift from training reproducibility to prompt management, retrieval pipeline configuration, model version tracking, and output quality assurance.

Where MLOps focuses on data pipelines and model retraining, LLMOps focuses on what happens after you pick a model: how you version prompts, how you evaluate output quality at scale, how you handle provider outages and model deprecations, and how you maintain audit trails for compliance.

Core LLMOps practices

Prompt versioning and management. Prompts are code in LLM applications. They need version control, diff tracking, and rollback capabilities. A prompt change can affect every query in production, so treating prompts with the same rigor as application code is table stakes.

Evaluation. LLM evaluation measures output quality across dimensions like accuracy, faithfulness, relevance, and safety. Evaluation happens pre-deploy (to gate releases) and post-deploy (to catch drift). Without evaluation, you have no signal on whether your system is working correctly.

Observability. LLM observability gives you visibility into what your LLM application is doing in production -- traces, latency, token usage, error rates, and quality scores per request. When something breaks, observability tells you where and why.

Cost management. LLM API costs scale with usage and model selection. LLMOps includes monitoring spend per pipeline, per model, and per customer, plus strategies for cost optimization like caching, model routing, and prompt compression.

Governance and compliance. For regulated industries, LLMOps includes safety filtering, PII detection, output logging, and audit trails that satisfy regulatory requirements and internal policies. The NIST AI Risk Management Framework provides a widely referenced structure for these controls.

The LLMOps tooling stack

The tooling market has organized into three layers:

1. Development tools -- prompt playgrounds, evaluation frameworks, dataset management
2. Deployment infrastructure -- LLM gateways, model routers, caching layers, CI/CD pipelines
3. Production operations -- observability dashboards, alerting, cost analytics, compliance monitoring

Some platforms try to cover all three layers. Others specialize in one. The LLMOps tools pricing comparison breaks down the major platforms and where they fit.

When you need LLMOps

If you are running a single LLM-powered feature with a handful of users, you can probably get by with manual testing and ad-hoc monitoring. LLMOps becomes necessary when:

• Multiple people are changing prompts and configs in the same system
• You need to know whether a model update improved or degraded quality
• Your LLM spend is material and needs tracking
• Regulatory requirements demand audit trails or safety filters
• Production incidents need root-cause analysis faster than "check the logs"

The complete LLMOps guide covers the full picture -- what the tools do, how the market is evolving, and where to start. For platform comparisons, see our LangSmith alternative and Humanloop alternative pages.