Your data science team has built a model.
Now you need to deploy it in a production environment, maintain 99.9% uptime, ensure scalability to handle fluctuating traffic, and monitor the quality of predictions 24/7. That’s exactly what MLOps is for – and what we’re here for.
Hostersi is a certified partner that has been designing infrastructure for the most demanding IT environments in Poland for over 20 years. Since 2023, we have specialised in infrastructure for AI workloads – from the implementation of classic machine learning models, through scalable LLM and generative AI environments, to fully automated MLOps and LLMOps pipelines on the AWS, Azure and GCP clouds.
We do not sell courses or ‘paper’ consultancy. We implement and maintain production-grade AI infrastructure – just as we do for e-commerce platforms serving millions of users, financial institutions and technology companies from the FT1000 list.
What is MLOps and why is the model alone not enough?
An AI model trained in a Jupyter notebook and an AI model running reliably in production are two different worlds. According to market data, over 85% of ML projects never make it to a production environment – precisely because they lack the necessary infrastructure and operational processes.
- MLOps (Machine Learning Operations) is an engineering discipline that combines DevOps practices, data engineering and machine learning to ensure the reliable deployment and maintenance of AI models in production. It encompasses the automation of training, model and data versioning, continuous integration and delivery (CI/CD for ML), model drift monitoring, and management of the entire AI system lifecycle – from experimentation to decommissioning
- LLMOps is an extension of MLOps designed specifically for large language models (LLMs) and generative AI. This brings with it a number of specific challenges: prompt management, hallucination monitoring, handling pre-model API gateways (e.g. Azure OpenAI, AWS Bedrock), scaling GPU/TPU infrastructure, and compliance with the AI Act.
Without a robust MLOps/LLMOps infrastructure, even the best model is nothing more than an experiment – costly, one-off and unauditable. With it, it becomes a business asset.
What we do specifically – our MLOps and AI infrastructure services
Designing and building AI infrastructure
We design AI infrastructure from scratch or adapt existing environments. We work on AWS SageMaker, Azure Machine Learning (AML) and Azure AI Foundry, Google Vertex AI, as well as on Kubernetes clusters (EKS, AKS, GKE) with dedicated GPU/CPU nodes for training and serving models.
The scope of design includes:
- training environment architecture – selection of GPU instances (e.g. p3, p4, g5 on AWS; NC/ND on Azure) for a specific model type and budget
- model serving environment – Kubernetes with auto-scaling (KEDA, HPA), load balancing, canary deployments and blue-green releases for ML models
- RAG and vector database infrastructure – deployment and management of Pinecone, Weaviate, pgvector (RDS/Aurora), and OpenSearch as a vector store for LLM-based systems
- API gateway in front of LLM models – configuration of Azure API Management or AWS API Gateway as a security, rate-limiting and monitoring layer in front of Azure OpenAI / AWS Bedrock / self-hosted models
- Infrastructure as Code – the entire setup described using Terraform and Helm, versioned in Git, deployed via a CI/CD pipeline
Automation of ML/LLM pipelines (CI/CD for AI)
We build fully automated pipelines covering the entire model lifecycle: from data collection and validation, through training and model quality testing (unit tests, regression tests, A/B testing), to automatic deployment to staging and production environments. We use tools such as MLflow, Kubeflow Pipelines, AWS Step Functions, Azure ML Pipelines, GitHub Actions and GitLab CI.
Continuous Training (CT) – automatic model retraining upon detection of data drift or a deterioration in metrics – is an increasingly required standard in production environments. We implement it alongside alerting and a rollback mechanism to the previous model version.
Model monitoring and AI observability
Deploying a model is only half the battle. We monitor:
- data drift and model drift – automatic detection of changes in the distribution of input data and degradation in prediction quality (Evidently AI, WhyLabs, built-in SageMaker/AML tools)
- LLM monitoring – tracking latency, tokens, costs (per model, per application), hallucinations and content safety (Azure AI Content Safety, AWS Guardrails for Bedrock)
- infrastructure observability – Prometheus, Grafana, CloudWatch, Azure Monitor with dashboards dedicated to AI workloads
- alerts and on-call – integration with PagerDuty, OpsGenie or the client’s system; Hosters engineers available 24/7 via a dedicated helpline
AI Infrastructure Cost Management (FinOps for AI)
GPUs are expensive. AI infrastructure can generate cloud bills that surprise even experienced engineering teams. As a certified AWS and Microsoft partner, we help optimise the costs of AI environments:
- selection of Spot Instances / Spot VMs for training (savings of up to 70% compared to on-demand instances)
- automatic shutdown of training environments upon job completion
- GPU node rightsizing – selecting the optimal instance size for a specific model
- expense monitoring broken down by AI projects, models and users
- eligibility for the AWS ML Credits and Microsoft AI Skilling Credits programmes – we assist with applications and expedite the process
Security and Compliance (AI Act, NIS2, GDPR)
AI infrastructure processes data that is often sensitive. We implement security standards appropriate to the sector in which the client operates:
- isolation of training environments from production environments (VPC/VNET with private endpoints, no access via the public internet)
- encryption of training data and models at rest and in transit
- privileged access management (IAM, RBAC) with the principle of least privilege
- auditability and lineage – tracking which data a given model was trained on (AI Act requirement for high-risk systems)
- GDPR compliance in the processing of personal data in ML pipelines
Migrating existing AI environments to the cloud
Whether your models run on on-premises servers, in a VMware environment or on an unmanaged Kubernetes cluster, we can help you plan and execute the migration to a managed cloud infrastructure. We develop a migration roadmap, migrate data and models without downtime, and train your team on the new environment.
Who is this service for
Our MLOps and AI infrastructure services are designed for companies that:We work with both tech start-ups and large enterprise organisations. Our collaboration model is flexible – we can act as an external MLOps team, provide support to an existing IT department, or carry out a one-off infrastructure deployment and hand over management to the client’s internal team.
- have ML/AI models in the experimental phase and wish to deploy them into production, but lack in-house MLOps expertise or infrastructure resources
- have launched their first production models and are facing challenges: lack of monitoring, manual retraining, uncontrolled GPU costs, lack of rollback
- are building products based on LLMs (chatbots, assistants, RAG, AI agents) and need a reliable, scalable infrastructure with cost control and data security
- operate in a regulated sector (finance, healthcare, e-commerce) and need AI infrastructure compliant with the AI Act, NIS2 or DORA
- want to migrate from on-premises GPUs or an unmanaged cluster to a cloud environment managed by an experienced team
Technologies we work with
- Cloud platforms: AWS, Microsoft Azure, Google Cloud Platform
- Model training and management: AWS SageMaker, Azure Machine Learning, Azure AI Foundry, Google Vertex AI, MLflow, Kubeflow, DVC
- Model serving: KServe, Seldon Core, TorchServe, TensorFlow Serving, FastAPI, Triton Inference Server (NVIDIA)
- Orchestration and containerisation: Kubernetes (EKS, AKS, GKE), Docker, Helm, Argo Workflows, Argo CD
- LLM and generative AI: AWS Bedrock, Azure OpenAI, Hugging Face on SageMaker/AKS, self-hosted models (Llama, Mistral) on GPU, LangChain, LlamaIndex
- Infrastructure as Code: Terraform, Ansible, GitLab CI/CD, GitHub Actions
- Monitoring and observability: Prometheus, Grafana, Evidently AI, WhyLabs, AWS CloudWatch, Azure Monitor
- Vector databases: Pinecone, Weaviate, pgvector (RDS/Aurora Postgres), Amazon OpenSearch
Why Hostersi, rather than a dedicated in-house team?
Building an in-house MLOps team is an investment that pays off in the long term. An MLOps engineer with experience in AWS SageMaker and Kubernetes will cost PLN 20,000–35,000 gross per month on the Polish market in 2026 – and even then, a single person will not be able to provide 24/7 coverage or a broad range of skills (security, FinOps, architecture, monitoring).
Hostersi provides access to a whole team of engineers covering a full range of specialisms — in a model that scales in line with the project’s needs. As an AWS Premier Partner and Microsoft Solution Partner, we have direct access to the vendors’ technical support, which reduces incident resolution times and speeds up access to new features.
In addition, we assist with securing funding from vendors (AWS ML Credits, Microsoft AI Skilling Credits), which can significantly reduce implementation and infrastructure costs in the first year.
How we work
Step 1 — Free technical consultation (60 mins) We discuss the current state of your AI environment, your business objectives and the challenges you face. Based on this, we prepare an initial architectural recommendation.
Step 2 — Audit and roadmap We carry out a detailed audit of the existing infrastructure (or an analysis of requirements for a new project) and provide an implementation roadmap with a schedule and cost estimates.
Step 3 — Implementation We implement the infrastructure and pipelines in agreed sprints. Regular contact with a dedicated lead engineer.
Step 4 — 24/7 maintenance and monitoring Following implementation, we take over administrative support: monitoring, alerts, updates, cost optimisation, and round-the-clock incident response.
FAQ
DevOps manages the application code lifecycle. MLOps extends these practices to address the specific challenges of AI: data and model versioning, automatic retraining, drift monitoring and auditability – elements not covered by a standard CI/CD pipeline.
Yes, if that model is running in production and influencing business decisions. Without monitoring, you won’t know when the model has started performing poorly. Without version control, you won’t be able to revert to a previous version following a failed retraining. Even a simple production model requires the basics: monitoring, backups and a rollback plan.
Yes. We deploy both self-hosted models on GPU infrastructure (EKS with GPU nodes, AKS with GPUs) and manage the infrastructure for commercial APIs (Azure OpenAI, AWS Bedrock). In many projects, we take a hybrid approach.
We usually set up the basic environment (an ML CI/CD pipeline, monitoring, and model deployment on Kubernetes) within 2–4 weeks. The timeframe depends on the complexity of the existing infrastructure and the number of models to be supported.
Yes. We implement the auditability and data lineage mechanisms required by the AI Act for high-risk systems, and for the financial sector we assist with meeting DORA requirements regarding AI infrastructure.
