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LLMOPS

Keep your AI features fast, cheap, and honest in production.

Shipping the AI feature was the easy half. Now the bill is climbing, latency spikes at peak, a model deprecation is scheduled, and nobody can say whether last week's prompt change made answers better or worse. LLMOps is the discipline that answers those questions — and Dezvo runs it as a service.

See Our Work
What we operate
  • Deployment & version control
  • Tracing & observability (Langfuse etc.)
  • Eval gates on every change
  • Cost dashboards & budgets
  • Security & abuse monitoring
WHAT IS LLMOPS?

DevOps, adapted for language models.

LLMOps is the operational practice of running LLM-powered features in production: versioning prompts and models like code, tracing every request, evaluating quality continuously, controlling token spend, and securing the system against prompt injection and abuse. It's DevOps plus the things that make LLMs different — non-determinism, per-call costs, and quality that drifts silently.

Without it, teams discover problems from customer complaints and invoice shock. With it, a prompt change that hurts accuracy fails an eval gate before deploy, a cost spike pages someone the hour it starts, and a model deprecation is a planned migration instead of an outage.

WHAT WE RUN

The four disciplines of production LLMs.

Deployment & versioning

Prompts, models, and configs versioned in git with eval gates in CI — changes roll out gradually and roll back instantly. Model upgrades become routine.

Monitoring & observability

Full request tracing (Langfuse or equivalent): latency, token counts, error rates, and quality signals per feature — with alerting, not just dashboards.

Cost optimization

Per-feature and per-tenant cost attribution, model routing, prompt and semantic caching, and budgets with alerts. Typical result: 40-70% off the monthly bill.

Security

Prompt-injection monitoring, PII redaction verification, rate limiting, abuse detection, and audit logs — run continuously, not audited annually.

FAQ

Common questions, answered.

If your question isn't here, message us — usually same-day reply.

DevOps assumes deterministic code: tests pass or fail. LLMs are probabilistic — the same input can produce different outputs, quality degrades without any code change, and every request has a marginal cost. LLMOps adds the missing tools: eval suites instead of unit tests alone, tracing of prompts and completions, token-cost accounting, and drift monitoring. MLOps overlaps but centres on training custom models; LLMOps centres on operating API-based and hosted LLMs.

Five things: quality (eval scores on a frozen test set, plus user feedback signals), latency (p95 time-to-first-token), cost (tokens per request, per feature, per tenant), errors (refusals, malformed outputs, provider failures), and security events (injection attempts, PII leaks). Most teams only watch errors — which is why quality and cost problems blindside them.

Almost always by 40-70% within the first month. The usual findings: oversized models on easy tasks (route them down), repeated context sent uncached (prompt caching), identical questions re-answered (semantic caching), and bloated prompts (token diet). Every change passes the eval gate, so cost drops while quality demonstrably holds.

Yes — setup (tracing, evals, dashboards, alerts, CI gates) as a fixed project, then a monthly retainer where we watch the dashboards, handle model migrations and deprecations, tune costs, and report quality trends. From $1,500/month depending on traffic and feature count.
RELATED SERVICES

Bundle the services that work together.

DevOps Services

CI/CD and infrastructure automation underneath.

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AI Infrastructure

GPUs, Kubernetes, and model hosting.

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LLM Customization

The evals and prompts LLMOps keeps honest.

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