LLMOps Roadmap for SRE & DevOps Engineers

What LLMOps means from an SRE perspective

LLMOps is the operational discipline around production language-model systems. It overlaps with SRE, but adds model-specific concerns such as token usage, evaluation quality, context length, GPU memory, provider limits and model routing.

Client
  ↓
AI Gateway
  ↓
Application / RAG / Agent
  ↓
Model Serving
  ↓
GPU / Provider

Across every layer:
metrics • traces • evals • security • cost

1. Start with inference and serving

Learn Inference, Model Serving, Batching, Continuous Batching and Autoscaling. These concepts explain how requests become model work and why throughput is not the same thing as user latency.

2. Learn the scarce resources

The most important capacity concepts are GPU, VRAM, KV Cache, Quantization and GPU Out of Memory (GPU OOM). In many self-hosted LLM systems, GPU memory is a hard capacity boundary. Long contexts and high concurrency can consume KV cache even when the model weights fit comfortably.

3. Measure user-perceived performance

Track Time to First Token (TTFT), Time per Output Token (TPOT), Tokens per Second and Throughput. TTFT tells you how long users wait before output starts; TPOT and tokens/sec describe generation speed; throughput tells you how much work the system can sustain.

4. Put a control plane in front

An AI Gateway can centralize authentication, rate limits, routing, retries, policy and observability. Combine it with Model Routing, Rate Limit, Circuit Breaker and fallbacks to reduce provider or model-specific blast radius.

5. Add AI-specific reliability signals

HTTP availability is not enough. Pair Observability with Evaluation (Eval), Hallucination Rate, token/cost telemetry and tool-call traces. Your SLOs can then represent both system reliability and the quality of the user experience.