MLOps and LLMOps: versioned models and prompts, tests before release, fast rollback
We build a process where every prompt or model change runs automated evals, ships to part of the traffic first and rolls back with a single command if something goes wrong.
What hurts
Someone edited a prompt in production and the agent started answering customers strangely. Nobody knows which version was live before or how to bring it back.
A cheaper model came out from your provider, but you are afraid to switch. There is no fast way to check that hundreds of scenarios will not break.
AI code lives apart from the rest of engineering. It is deployed by hand from a laptop, with no tests and no log of who changed what.
What we build
- A repository for prompts, model configuration and tests
- An automated eval run on every change
- A build and deployment pipeline in Docker
- Gradual release to part of the traffic with metric comparison
- Rollback to the previous version with one command
- A model registry with versions, dates and eval results
- Prometheus metrics and alerts on quality degradation
What you get
- CI/CD for AI components configured in your repository
- A scenario test set with quality thresholds
- A registry of model and prompt versions
- A release and quality dashboard
- Process documentation and training for your team
How we measure the result
- Time from a prompt change to a production release
- Time to roll back to the previous version
- Number of releases stopped by evals before production
- Number of incidents after AI component releases
Systems and stack
Industries
Questions
How much does MLOps implementation cost?
From $8,000. The price depends on the number of models and agents, your existing CI/CD and whether you already have test scenarios. If not, we build them from real conversations and logs. We give a fixed estimate after discovery.
How is LLMOps different from regular DevOps?
LLM output is not deterministic, so ordinary unit tests do not catch degradation. You need scenario sets, answer scoring by another model or by rules, and version comparison on the same data. The rest is familiar: versions, pipelines and rollback.
Does it work with cloud models, not only self-hosted ones?
Yes. The process is the same for OpenAI, Anthropic, Gemini and self-hosted models on vLLM. We run agents on the Happ platform this way ourselves: every change passes a test suite before it ships.
What do you need from us?
Access to the repository and environments, a list of AI components, and sample conversations or requests for tests. Ideally one person on your team who will own the process afterwards.
How long does it take and what comes next?
Usually 6–10 weeks. After that your team runs the process, and we can support it under an SLA or join again when you add new models.
Can we start small?
Yes. Often the first step is a test set and an automated run on every prompt change. That alone catches most regressions. Gradual release, the model registry and dashboards come once the first step works. You see the benefit in the first weeks, not at the end of the project.
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