MLOPS / 04

MLOps, LLMOps & AI Infrastructure

We manage model, prompt, retrieval, and agent versions under one production discipline with evaluation, tracing, CI/CD, security, and cost visibility built in.

Discuss your project

Runtime flowMLOPS / 04

01Change
02Evaluation
03Staged rollout
04Monitoring
05Rollback path

The problem

We define the problem before the solution.

An AI system does not end on launch day; that is when its real life starts. Model versions change, prompts are edited, the retrieval corpus grows, provider pricing shifts, and data distributions move. If you cannot measure which of those changes lowered quality, you are not operating the system; you are hoping it still works.

We build the MLOps and LLMOps layer as one production discipline: model, prompt, retrieval, and agent versions are tracked together; evaluation sets are wired into CI/CD; tracing makes the latency and cost of every request visible; and the rollback path is defined before an incident, not during one. The goal is not more tooling, but seeing the effect of a change before it ships.

Use cases

Where this system usually shows up.

Model and prompt versioning

One record that tracks which version is live, what changed when, and which metric each change moved.

Evaluation and regression testing

An automated check that runs every change against a fixed question set before release and catches quality regressions at pull-request time.

Cost and latency visibility

A monitoring layer that breaks token use, latency, and error rate down per endpoint and per step, naming the expensive one.

Incident response and rollback

An operations plan that defines in advance which version to return to, who does it, and with whose approval when quality drops or a service fails.

What does the system deliver?

Repeatable, controlled releases

Visibility into quality, drift, latency, and cost

Fast rollback and safe version management

Delivery scope

We deliver an operable product, not just a model.

01
Infrastructure and risk assessment
02
Model/prompt registry and versioning
03
Evaluation, tracing, and monitoring dashboards
04
CI/CD, security, and incident response plan

How we build it

We design technical components together with the operating workflow.

Create a baseline

We define starting metrics for quality, latency, cost, and failure classes.

Manage versions

We track model, prompt, data, and retrieval changes together.

Automate releases

We connect testing, approval, canary, and rollback to CI/CD.

Observe production

We bring drift, quality loss, cost, and incidents into one operations view.

Technical approach

The layers and tools we work with.

Versioning: Model and prompt registry, dataset and eval-set versioning
Release: Docker, CI/CD, staged rollout and rollback steps
Observability: Langfuse tracing, quality, drift, and cost dashboards
Infrastructure: A serving layer that fits AWS, GCP, or your existing cloud setup

Frequently asked questions

What to know before making a decision.

01Is MLOps only for custom models?

No. Applications using hosted LLMs still need prompt, retrieval, evaluation, tracing, cost, and security operations.

02Will it work with our current cloud stack?

Yes. We can integrate with your existing CI/CD, observability, and security stack without changing cloud providers.

03How do you build the evaluation set?

From the requests the system actually receives, not synthetic questions. We sample real user queries, define expected behaviour together with a domain expert, and add known failure cases to the set. It grows as new failure types arrive from production; a small real set is more trustworthy than a large artificial one.

04Can our team take the system over?

The delivery scope is designed for it. Architecture decisions, runbooks, dashboards, and incident steps are documented, and a handover is planned with your team. The aim is to leave a system your team can operate, not a dependency.

05When is investing in MLOps too early?

For a single prototype with no live users, where accuracy is not yet tied to a business outcome, a heavy MLOps setup is premature. At that stage a simple evaluation set, version tagging, and basic logging are enough; the rest is added once the system sees real use.