LLMOps

Running LLMs in production is its own discipline. Token costs balloon, latency varies wildly, model providers deprecate APIs, and an unmonitored prompt change can quietly tank quality for days.

LLMOps is the connective tissue: model routing, cost controls, AI-specific observability, eval pipelines that catch regressions, and caching strategies that turn expensive calls into cheap ones.

If “AI engineering” is building the feature, LLMOps is making sure it stays good — and stays affordable — once a thousand users start hitting it.

What’s Included

• Production deployment of AI features
• Model routing across providers and tiers
• Cost optimization — prompt compression, caching, batching
• Latency, throughput, and timeout strategies
• AI observability — traces, prompt logs, quality metrics
• Eval pipelines wired into CI
• Semantic and exact-match caching

Key Benefits

Predictable AI bills
Visibility into per-feature spend and the tools to bring it down.

No silent quality drops
Eval pipelines catch prompt regressions before users do.

Failover when providers wobble
Model routing so an OpenAI outage isn’t your outage.

Faster, cheaper responses
Caching and routing that keep p95 latency in check.