LLMOps is more Ops than LLM

Your LLM application just crashed at 2am. Again.

You scroll through logs trying to figure out what happened. Token limits? API timeouts? Hallucinations? The monitoring dashboard shows everything green, but your users are seeing garbage outputs.

Here is what nobody tells you about LLMOps best practices: the problem is rarely the LLM. The issue is that you are treating AI infrastructure like it needs special operational magic when it actually needs the same boring reliability engineering that keeps your database running.

The ops fundamentals that actually matter

Recent industry analysis shows 73% of LLM deployments fail to reach production or fail within the first 90 days. The surviving 27% share one critical trait: they apply traditional operations discipline.

Not special AI operations. Regular operations.

They monitor what matters. Set up proper alerting. Plan capacity. Document runbooks. Test deployments. The stuff that Google’s SRE team has been writing about for years, adapted for systems that happen to call LLM APIs instead of databases.

When I talk to operations teams running reliable LLM applications at Tallyfy, they sound exactly like ops teams running reliable web services. They obsess over latency percentiles. They set error budgets. They run chaos engineering experiments. They treat their LLM infrastructure like infrastructure.

The teams that struggle? They are trying to apply machine learning practices to operations problems. Experimenting with prompts when they should be fixing their deployment pipeline. Tweaking model parameters when their monitoring is fundamentally broken.

Why traditional reliability patterns work

There is a whole book from Google engineers about applying SRE principles to machine learning systems. The core insight: ML systems fail in the same ways traditional systems fail, plus a few new ones.

Your LLM application needs load balancing. It needs circuit breakers. It needs proper retry logic with exponential backoff. These are not AI problems. They are distributed systems problems.

The unique challenges - things like prompt injection, hallucination detection, token usage spikes - get layered on top of this foundation. But if you cannot keep your API calls working reliably, you will never get to the interesting AI-specific challenges.

Microsoft’s LLMOps maturity model describes this progression clearly. Organizations start at the ad hoc stage with no standardization. They advance by implementing the same practices that work for traditional applications: automated testing, CI/CD pipelines, monitoring, incident response.

The optimized organizations are not doing anything exotic. They have just applied proven operational discipline consistently.

Monitoring across the full stack

Here is where LLMOps best practices diverge from traditional monitoring. You need visibility across multiple layers simultaneously.

IBM’s research on AI observability breaks this down into five essential layers:

Application layer - track user interactions, latency, feedback loops. The stuff you would monitor in any web application.

Orchestration layer - trace prompt-response pairs, retries, tool execution timing. This is where things get LLM-specific.

Model layer - monitor token usage, API latency, failure modes like timeouts and errors. Track quality metrics including hallucination rates and accuracy.

Vector database layer - watch embedding quality, retrieval relevance, result set sizes. If you are using RAG, this layer predicts most of your production issues.

Infrastructure layer - GPU utilization, memory consumption, network bandwidth. The traditional ops layer that still matters.

Most teams monitor one or two layers well. The organizations with reliable systems monitor all five, with alerts that understand the relationships between layers. When user latency spikes, they can see whether the issue is the model API, the vector search, or infrastructure constraints.

That visibility does not come from AI-specific tools. It comes from proper instrumentation, structured logging, and distributed tracing. The same observability patterns that work for microservices, adapted for the specific components in your LLM stack.

Building from zero to reliable

The path to operational maturity follows a predictable pattern. Start with something small that works end-to-end. Build basic monitoring. Add evaluation harnesses. Only then start optimizing.

ZenML’s analysis of production LLM systems emphasizes this ruthlessly: get something deployed with basic infrastructure before you worry about perfect performance.

The deployment gap happens when teams build sophisticated LLM applications locally but cannot get them running reliably in production. They skip the boring operational work - proper CI/CD pipelines, automated testing, deployment automation, monitoring dashboards.

When you are building LLMOps best practices into your organization, focus on these operational fundamentals first:

Can you deploy changes without manual intervention? Do you have automated tests that catch regressions? Can you roll back quickly when something breaks? Do you know within minutes when quality degrades?

Once you can answer yes to those questions, you can improve model performance, tune prompts, experiment with different architectures. The foundation lets you move fast without breaking things.

FinOps research on AI workloads shows another critical operational discipline: capacity planning. LLM applications consume resources unpredictably. Token usage spikes do not follow normal traffic patterns. You need predictive capacity planning and automatic scaling, just like you do for any variable workload.

The teams that control costs plan capacity based on business metrics, not just technical metrics. They know their cost per user interaction, per API call, per successful task completion. They set budgets and alerts. They use spot instances for training workloads. Standard cloud operations, applied thoughtfully.

What this means for your team

If you are building LLMOps best practices, hire operations people who understand reliability engineering. They will apply the right patterns faster than ML engineers trying to learn operations.

Your monitoring strategy should look familiar to anyone who runs production services. Your deployment pipeline should look like any modern CI/CD system. Your incident response should follow established SRE practices.

The AI-specific parts - prompt versioning, model evaluation, hallucination detection - get built on top of that operational foundation. Not instead of it.

The organizations succeeding with production LLM systems are not doing anything revolutionary. They are applying operational discipline consistently. Monitoring what matters. Planning capacity properly. Deploying safely. Responding to incidents quickly.

This is the discipline that separates the 27% that make it to production from the 73% that do not.