Core service
Monitoring & Drift Detection
Dashboards and alerts for accuracy, data drift, latency and errors, so problems are caught before customers notice.
MLOps & LLMOps Services
Deploy, monitor and evaluate AI models with confidence
Most AI projects stall between a promising demo and a reliable product. We build the pipelines, monitoring and evaluation that keep machine learning models and LLM applications accurate, safe and affordable in production.
The production AI loop
- 1
Train & version
Data, models and prompts tracked in one registry
- 2
Test & deploy
Automated checks, staged rollout, one-click rollback
- 3
Monitor & evaluate
Accuracy, drift, safety, latency and cost
- 4
Improve & retrain
Fixes ship safely, and the loop starts again
The problem
Why AI Models Fail After the Demo
A model that works in a notebook is only the start. In production, data changes, users ask unexpected questions, costs grow with usage and nobody notices when accuracy slips.
MLOps and LLMOps are the engineering practices that fix this: repeatable pipelines, automated tests, monitoring and a safe way to ship improvements.
- 01
Silent accuracy drift
Real-world data moves away from the training data, and predictions quietly get worse.
- 02
Manual, risky releases
Models are copied to servers by hand, with no versioning, tests or easy rollback.
- 03
No way to measure LLM quality
Prompt or model changes go live without anyone checking whether answers got better or worse.
- 04
Costs that creep up
GPU bills and API token spend rise with usage, with no visibility into where the money goes.
What we offer
Our MLOps & LLMOps Services
From the first automated pipeline to full observability, we set up everything your team needs to run AI in production, and train your engineers to own it.
MLOps Assessment & Roadmap
We review how your models are built, deployed and monitored today, and plan the practical steps to production-grade AI.
Learn more about MLOps Assessment & RoadmapCI/CD for Machine Learning
Automated pipelines that retrain, test and version models and data, so every release is repeatable and easy to roll back.
Model Deployment & Serving
Models served as fast, scalable APIs on Kubernetes or managed cloud services, including open-source LLMs on your own GPUs.
LLM Evaluation
Test sets built from real questions, automated scoring (including LLM-as-a-judge) and regression checks before every prompt or model change.
LLM Observability
Tracing of every prompt, retrieval step and tool call, with prompt versioning and token cost tracking per feature and user.
Guardrails & Safety
Filters for sensitive data, prompt-injection defenses and policy checks, with audit logs for compliance.
Cost & Performance Optimization
Caching, model routing, quantization and GPU autoscaling that cut inference costs without hurting quality.
MLOps vs LLMOps
What's the Difference Between MLOps and LLMOps?
LLMOps builds on MLOps. Both need pipelines, monitoring and governance, but language models bring new challenges. We cover both, so your predictive models and LLM apps run on one platform.
| MLOps: for predictive models | LLMOps: for LLM applications | |
|---|---|---|
| Core work | Data and feature pipelines, training and retraining | Prompt, model and retrieval versioning |
| Tracking | Model registry, versioning and experiment tracking | Tracing of RAG and agent steps, token cost tracking |
| Quality checks | Accuracy and data-drift monitoring | Evaluation of answer quality, safety and hallucinations |
| Best for | Forecasting, scoring, vision and classification models | Chatbots, RAG assistants, copilots and AI agents |
Process
How We Set Up MLOps & LLMOps
Assess
We map your models, data and deployment process, and agree on the quality, speed and cost targets that matter.
Build Pipelines
We automate training, testing and packaging, with versioned data, models and prompts.
Deploy
Models go live behind scalable APIs, with staged rollouts, A/B tests and one-click rollback.
Monitor & Evaluate
Dashboards, alerts and automated evaluations track accuracy, drift, latency, safety and cost.
Improve & Hand Over
We retrain and tune based on real usage, document everything and train your team to run it.
Technology
MLOps & LLMOps Tools We Use
We work with open, widely used tools and your existing cloud, so there's no lock-in.
- Pipelines & Tracking
- MLflowKubeflowAirflowDVCWeights & Biases
- Model Serving
- vLLMKServeBentoMLNVIDIA TritonRay Serve
- LLM Evaluation & Observability
- LangfuseLangSmithArize PhoenixRagas & DeepEvalPromptfoo
- Cloud & Infrastructure
- Kubernetes & DockerTerraformAWS SageMaker & BedrockAzure Machine LearningGoogle Vertex AI
What you get
What You Get at the End
Everything is set up in your own cloud accounts and repositories, and documented for your team.
- Automated training & deployment pipelines
- Model & prompt registry with versioning
- Monitoring dashboards & alerts
- LLM evaluation suite & quality reports
- Cost & usage tracking
- Runbooks & team training
Why Infilon
Why Choose Infilon for MLOps & LLMOps?
We've spent more than 16 years building and running production software, with DevOps, cloud and data engineering teams under one roof. That's exactly the mix MLOps and LLMOps need.
We build AI models ourselves too, so we know what breaks in production and set things up to be simple for your team to own.
How we work
- Built in your cloud, with no lock-in to our tools
- Start small: one model or LLM app, then scale
- Clear metrics for quality, speed and cost
- Hand-over and training, or ongoing support if you prefer
FAQ
MLOps & LLMOps FAQs
What is MLOps?
MLOps (machine learning operations) is a set of practices and tools for deploying, monitoring and maintaining ML models in production. It brings DevOps ideas like automation, versioning, testing and monitoring to machine learning.
What is LLMOps?
LLMOps is MLOps for applications built on large language models. It adds prompt and model versioning, evaluation of answer quality and safety, tracing of RAG and agent steps, and tracking of token costs.
Do we need MLOps if we only have one or two models?
Yes, a lightweight version. Even one model needs automated deployment, monitoring and a way to retrain safely. We scale the setup to your size, so you don't pay for a platform you don't need.
How do you evaluate LLM applications?
We build a test set from real user questions, score answers automatically for correctness, relevance, safety and hallucinations (often with an LLM as a judge, checked against human review) and run it before every change to catch regressions.
Can you work with the cloud and tools we already use?
Yes. We work in AWS, Azure or Google Cloud and fit around your existing CI/CD, data platform and monitoring tools, adding only what's missing.
How do you reduce LLM and GPU costs?
We track spend per feature, cache repeated requests, route simple tasks to smaller models, shorten prompts, quantize self-hosted models and autoscale GPUs so you only pay for what you use.
How long does it take to set up MLOps or LLMOps?
A first production pipeline with monitoring for one model or LLM app usually takes four to eight weeks. We then extend it to more models and teams step by step.
Ready to Run AI in Production With Confidence?
Tell us about the models or LLM apps you're running, or planning. We'll show you what to automate first and how to measure it.
Talk to an MLOps Expert