Operations
LLMOps: Operating Models in Production
Best practices for deploying, monitoring, and maintaining large language models at scale.
What is LLMOps?
LLMOps (Large Language Model Operations) is the practice of deploying, monitoring, and maintaining LLMs in production. It extends MLOps principles to address unique challenges of generative AI: prompt management, token cost optimization, output quality, and safety controls.
Key Components
- Model Deployment: Serving infrastructure with auto-scaling, load balancing, and versioning.
- Prompt Management: Version control for prompts, A/B testing, and optimization.
- Monitoring & Observability: Track latency, token usage, error rates, and quality metrics.
- Cost Optimization: Caching, batching, model routing, and quantization to reduce token spend.
- Safety & Guardrails: Content filtering, PII detection, and output validation.
- Evaluation & Testing: Automated evals for accuracy, relevance, and safety.
Common Challenges
- Latency: LLMs are slower than traditional APIs; requires streaming and async patterns.
- Cost: Token-based pricing can scale unpredictably with traffic.
- Quality: Output variability and hallucinations require continuous monitoring.
- Safety: Risk of harmful, biased, or non-compliant outputs.
Best Practices
Effective LLMOps requires observability platforms (Arize, Phoenix, W&B), automated evaluation pipelines, cost tracking dashboards, and incident response playbooks. We help clients build production-grade LLM infrastructure with SLAs, alerting, and continuous improvement loops.
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