LLMOps for production AI and RAG applications
WZ-IT monitors the agreed AI application above the infrastructure layer: traces, errors, latency, cost, evaluations, and changes to models, prompts and retrieval. Server and GPU operations remain a separately combinable service.
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Measure the behaviour of the AI application, not only availability
A reachable endpoint does not show whether answers deteriorate, retrieval fails or a model change alters cost and latency. Managed LLMOps provides a documented review and response process for these changes.
Traces, errors and usage
Track agreed calls, error patterns, latency, token or model cost and technical failures.
Quality and regression
Use representative evaluations to make relevant changes in answer and retrieval quality visible.
Controlled changes
Document and assess prompt, model, provider and retrieval changes and introduce them with a fallback path.
Understand Langfuse, tracing and evaluation as the technical basis for traceable LLM operations.
Operations control for the AI application, separate from server operations and code maintenance
The monitoring and review boundary is defined against the application, model access, retrieval, criticality and existing telemetry.
Assess observability
Review existing traces, metrics, logs and data-protection boundaries and identify gaps.
Define quality indicators
Agree representative test cases, expected sources, error classes and relevant operating metrics.
Monitor continuously
Review errors, latency, cost, usage and agreed quality indicators on a fixed schedule.
Evaluate changes
Compare model, prompt, provider and retrieval changes before or after controlled introduction.
Assess security advisories
Review relevant advisories for the agreed AI application stack for exposure and action.
Document findings
Record deviations, decisions, accepted risks and recommended follow-up actions.
Service boundary
LLMOps does not replace other operations modules
- Server, GPU, Kubernetes or database operations without an additional managed AI or infrastructure service
- Code fixes, feature development or unlimited prompt and RAG optimisation
- 24/7 incident response without an agreed service level
- A guarantee of correct answers, complete security or regulatory compliance
Establish a baseline, then control change
Before the retainer starts, the application, telemetry, test cases and current state are documented. Later deviations can then be assessed against a confirmed baseline.
- 01
Scope application and risk
Capture the use case, data paths, models, retrieval, users and critical failure modes.
- 02
Build the baseline
Document telemetry, evaluations, metrics, versions and current behaviour.
- 03
Review continuously
Assess operating values, quality, changes and security advisories on the agreed schedule.
- 04
Control follow-up work
Prioritise findings and prepare implementation by your team or a separately commissioned WZ-IT service.
Baseline and retainer
A documented starting point before ongoing LLMOps
The baseline establishes application, telemetry and review criteria. The monthly price then depends on applications, traces, models, evaluation scope and the required response path.
LLMOps Baseline
from €1,490
excluding VAT, one-time
For one clearly scoped AI application with existing or feasible technical telemetry.
- Capture application, model and data paths
- Assess telemetry and observability status
- Define quality indicators and test cases
- Document baseline, gaps and operations scope
Managed LLMOps
from €990 / month
excluding VAT, monthly
For ongoing control of a previously assessed AI or RAG application.
- Traces, errors, latency and cost within the agreed scope
- Regular evaluations and regression checks
- Assessment of relevant model, prompt and retrieval changes
- Prioritised findings and a monthly technical report
Telemetry tools, model and API costs, additional infrastructure, code changes and service levels are quoted separately.
All prices are net and exclude statutory VAT. The offers are addressed to businesses.
Suitable for production AI applications that change over time
- An AI, agent or RAG application is already in production or used by real users
- Models, prompts, knowledge sources or providers change regularly
- Quality, cost and latency should be visible before users complain
- Technical findings and changes need a documented ownership path
What we need from you
- A description of the AI application and its most important user paths
- An overview of models, providers, prompts, retrieval and data flows
- Access to existing telemetry or the ability to add it under control
- Business contacts and representative test or acceptance cases
Combine LLMOps with infrastructure, code maintenance and the business application
Each module remains separately available. This makes it clear whether a finding belongs to the AI application, the code or the underlying infrastructure.
Managed AI Server
Operate GPU, operating system, drivers, containers, inference runtime, monitoring and infrastructure.
View operationsfrom €990 excl. VAT / monthAI Code Review Retainer
Have code, authentication, data, dependency and release risks reviewed by a human on an ongoing basis.
View review servicefrom €699.90 excl. VAT / monthSoftware Maintenance
Have confirmed technical changes, dependency care and controlled releases implemented on an ongoing basis.
View maintenanceDiscuss LLMOps
Which AI application should be monitored?
Tell us the use case, models, retrieval, operating location and existing telemetry. We will assess the baseline and retainer scope.
Frequently asked questions about Managed LLMOps
Boundaries, tracing, evaluation, changes and ongoing operations
LLMOps covers the controlled delivery, observation and change of applications using large language models. Depending on scope, this includes tracing, evaluations, prompt and model versions, retrieval, cost, latency, error patterns and documented response processes.
Managed LLMOps looks at AI application behaviour: traces, quality, retrieval, models, prompts, cost and latency. Managed AI Server covers the underlying infrastructure, including GPU, operating system, drivers, containers and inference runtime.
No. Langfuse is one possible open-source foundation for tracing and evaluation. The telemetry used or retained depends on the existing stack and requirements for data storage and integration.
Managed LLMOps prioritises and documents findings. Code changes, infrastructure work or extensive RAG optimisation are handled by your team or through a separately agreed WZ-IT service.
No. Representative evaluations and monitoring can reveal quality changes and defined error classes, but they cannot provide a general guarantee that every answer to an unknown case will be correct.
Relevant factors include the number and criticality of applications, call volume, models and providers, retrieval, existing telemetry, evaluation scope, change frequency, reporting and the required response process.
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Annota - healthtech startup with an AI documentation assistant.
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Golem.de - leading German IT news outlet.
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Project Manager, Stadtwerke Brühl GmbH
Stadtwerke Brühl - municipal energy and infrastructure utility.
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Data Manager, ARGE
ARGE Neue Medien - master data & digital standards for plumbing and building technology.
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CTO, EVA Real Estate
EVA Real Estate - leading real-estate agency in Dubai.
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Pascal Hakkers
Aphy AG
Aphy AG - AI platform for hotel back-office.
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John Hellmerichs
Managing Director, AInergy GmbH
AInergy - consultancy for energy and facility management.
“Timo and Robin from WZ-IT set up a RocketChat server for us - and I couldn't be more satisfied! From the initial consultation to the final implementation, everything was absolutely professional, efficient, and to my complete satisfaction. I particularly appreciate the clear communication, transparent pricing, and the comprehensive expertise that both bring to the table. Even after the setup, they take care of the maintenance, which frees up my time enormously and allows me to focus on other important areas of my business - with the good feeling that our IT is in the best hands. I can recommend WZ-IT without reservation and look forward to continuing our collaboration! (translated)”
Sebastian Maier
Managing Director, Yonju GmbH
Yonju - business coaching and AI products.
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Gabriel Sanz Señor
Managing Director, Odiseo Solutions
Odiseo Solutions - software engineering for the space industry.
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