---
title: "MLOps Consulting Services | Deploy and Run ML and LLMs | Agility"
url: https://agilitytech.ai/mlops-consulting
description: "MLOps and LLMOps consulting: pipelines, model deployment, monitoring and cost control so your models keep working after launch."
publisher: Agility (agilitytech.ai)
---

Service · MLOps

# MLOps consulting

A model that works in a notebook is maybe a third of the job. The rest is getting it deployed, watched and retrained without someone doing it by hand at midnight.

- Senior engineers on every project
- Evaluation before launch, not after
- You own the code and the models

48-hour turnaround · free · no obligation

### Talk to us about MLOps

Tell us what you want to build. A senior engineer sends back a scope and a fixed price within two business days, no obligation.

In short

## The quick answer

We set up the pipelines, monitoring and infrastructure for ML and LLM systems, on your cloud or on-premises.

What we build

## What we build with MLOps

### Training and deployment pipelines

Automated training, testing and deployment, so releasing a model is a routine step and not a project.

### Monitoring

Alerts for data drift, falling accuracy, latency and cost, aimed at the people who can act on them.

### LLMOps

Prompt and model versioning, evaluation on every change, request tracing and spend control for LLM apps.

### Serving infrastructure

GPU serving for open models, autoscaling, and on-premises setups when the cloud is not an option.

Fit

## Is MLOps the right choice?

### When it is a good fit

- Models are deployed by hand and break between releases.
- Nobody notices when a model quietly gets worse.
- LLM costs are rising and nobody can say why.

### When we would suggest something else

- You have one experimental model and no plans to ship it yet.

Process

## How a project usually runs

- 01 ### Audit what exists How models get trained, deployed and checked today, and where it hurts.
- 02 ### Fix the riskiest gap first Often monitoring, because silent failure is the expensive kind.
- 03 ### Automate the pipeline Using tools your team can run: MLflow, Kubernetes, your cloud's native services.
- 04 ### Hand over Runbooks and training so the setup outlives the engagement.

Pitfalls

## What usually goes wrong

We have seen these enough times to plan around them from the start.

- Buying a platform before knowing the process it should support.
- Monitoring uptime but not accuracy.
- No owner for retraining.

Next step

Want to see similar work? Browse our [case studies](https://agilitytech.ai/case-studies) or [tell us what you are working on](https://agilitytech.ai/contact).

FAQ

## MLOps: common questions

[Ask us something else](https://agilitytech.ai/contact)

**What is the difference between MLOps and LLMOps?+**

MLOps covers training, deploying and monitoring models. LLMOps applies the same thinking to language model apps, adding prompt versioning, evaluation sets and token cost tracking.

**Which tools do you use?+**

Whatever fits your stack. Often MLflow, Docker and Kubernetes, plus the native ML services on AWS, Azure or Google Cloud.

**Can you work with our data science team?+**

Yes. Most engagements pair our engineers with your data scientists so the setup matches how they actually work.

**Can models run on our own servers?+**

Yes. We set up on-premises and private cloud serving, including GPU hosting for open models.

Keep reading

## Related pricing and decision guides

[Hire MLOps engineers](https://agilitytech.ai/hire-mlops-engineers)[On-premises AI](https://agilitytech.ai/on-premise-services)[Hire AI developers](https://agilitytech.ai/hire-ai-developers)[AI agent development](https://agilitytech.ai/ai-agent-development)[LLM development services](https://agilitytech.ai/llm-development-services)

## Get your exact number with a free 48-hour audit

Indicative ranges only get you so far. Tell us the specifics and get a scope and a fixed price in two business days.

Get my fixed estimate