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MLOps agencies in Hamburg compared
Training a model is only half the work. Running it reliably, monitoring it, and scaling it is the other half, and that second half gets underestimated in a lot of AI projects. These agencies build the infrastructure that keeps machine learning and AI systems running in production. Compare them by stack, cloud provider, and verified proof of expertise.
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Guide
Why MLOps is its own discipline
Running machine learning systems brings challenges that classic software development doesn't have. Models need to be versioned like code, training data needs to be documented in a way you can trace back, and the quality of a running model has to be monitored continuously, because it can degrade quietly over time. An MLOps agency builds the infrastructure that handles all of this systematically instead of ad hoc: pipelines for training and deployment, monitoring of model quality in production, and processes for retraining when needed.
What to look for in MLOps projects
Automated deployment is the core of any solid MLOps setup. A new model should reach production in a controlled, traceable way, not through manually copying files onto a server. Ask about the actual pipeline, not just the concept behind it.
Monitoring model quality in production, not just technical uptime, is what separates experienced providers from plain infrastructure vendors. A model can run technically flawlessly while its predictions quietly get worse, and that needs to be caught before it causes real business damage.
Cost transparency on inference, meaning the ongoing cost of running the model, matters a lot at high volume, since these costs can grow out of control if nobody actively tracks them. And ask how reproducible training runs are: can you trace back exactly which data and settings produced a given model, if you need to?
What building MLOps costs
€140 to €210 per hour is typical. Building a basic MLOps pipeline for an existing model costs €25,000 to €70,000. A comprehensive infrastructure for multiple models with full monitoring runs €70,000 to €200,000. Ongoing operations are often billed as a monthly flat fee starting around €2,000.
Typical MLOps projects
Building an automated training and deployment pipeline for an existing model. A monitoring system that catches quality degradation in production models early. Migrating manually operated models to a structured MLOps platform. Scaling an existing AI solution for significantly higher request volume.
Find an MLOps partner
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Frequently asked questions
MLOps agencies: questions and answers
What does building an MLOps infrastructure cost?
A basic pipeline for an existing model costs €25,000 to €70,000, and a comprehensive infrastructure for multiple models runs €70,000 to €200,000. Agencies charge €140 to €210 per hour, and ongoing operations are often billed as a monthly flat fee.
Do I need MLOps for a single model already?
Often not at full scope, if it's a single model that's updated rarely. Once you're running multiple models, retraining regularly, or the model's quality is business-critical, structured MLOps pays off quickly, usually well before an avoidable outage forces the issue.
How do you know a model has degraded in production?
Through ongoing monitoring of actual prediction quality against real outcomes, not just the system's technical uptime. Without that monitoring, companies often only notice the degradation once it has already caused noticeable business damage.
What's the difference between DevOps and MLOps?
DevOps automates testing, building, and deploying classic code. MLOps extends those principles to the specifics of machine learning: versioning training data and models, continuous quality monitoring, and processes for retraining. Many of the concepts overlap, but the actual implementation looks quite different.