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  • Kubernetes
  • Platform engineering
  • SRE
  • AI integrations

Kubernetes platforms your teams can ship on, kept running 24/7.

InfraNode designs, builds and operates Kubernetes on EKS, AKS, GKE and on-premises. We add the platform layer developers need, the SRE practice that keeps it reliable, and AI where it saves real time: in day-to-day operations and in running your own models.

Talk to an engineer See the AI part
  • EKS · AKS · GKEand on-premises
  • GitOpsevery change in Git
  • 24/7on-call support
Changes flow from Git through Argo CD into a Kubernetes cluster with two general nodes and a GPU node, observed by Prometheus, Grafana and OpenTelemetry Git main · 3f9c1e2 Argo CD cluster: prod-eu · EKS · v1.33 node-1 4 vCPU · 16 GiB node-2 4 vCPU · 16 GiB gpu-node 1 GPU · vLLM Prometheus · Grafana · OTel SLO 99.9% · budget 71% left
AI

p95 latency on checkout rose 38% after deploy 3f9c1e2. Suggested action: roll back in Argo CD. Waiting for on-call engineer approval.

Services

Everything between your code and production

We build the platform once, describe it in code, and keep it healthy while your teams focus on the product.

Kubernetes clusters

Managed or self-hosted, sized and secured for production.

  • EKS, AKS and GKE design and build
  • On-premises clusters on bare metal or VMware
  • Version upgrades without downtime
  • Autoscaling with Karpenter and KEDA

Infrastructure as Code

Every network, cluster and database described in reviewed code.

  • Terraform and OpenTofu modules
  • Remote state, plans in pull requests
  • Policy checks before apply
  • Drift detection

GitOps delivery

Git is the source of truth. What is merged is what runs.

  • Argo CD for all environments
  • Helm and Kustomize packaging
  • Canary and blue-green with Argo Rollouts
  • Promotion from dev to production by pull request

Internal developer platform

Self-service for developers, without a ticket for every change.

  • Backstage portal and service catalogue
  • Golden-path templates for new services
  • Preview environments per pull request
  • Cloud resources on request with Crossplane

Observability

Metrics, logs and traces in one place, with alerts people trust.

  • Prometheus and Grafana dashboards
  • OpenTelemetry instrumentation and collectors
  • Logs and traces with Loki and Tempo
  • Alerts based on SLOs, not on noise

SRE & reliability

Reliability as an engineering practice with clear targets.

  • SLOs and error budgets with product owners
  • Incident response and blameless postmortems
  • Capacity planning and cost reviews
  • Backup, restore and disaster recovery drills
AI integrations

AI where it saves time, with engineers in control

We work in two directions: AI that helps run your platform, and a platform that runs your AI workloads.

AI for operations

Less time collecting context, more time fixing the problem.

  • Alert triage and incident summariesRelated alerts are grouped, and recent deploys, logs and metrics are summarised in the incident channel.
  • On-call assistantAnswers questions over your runbooks, dashboards and read-only cluster data through MCP servers and k8sgpt.
  • Review of infrastructure changesTerraform plans and Helm diffs get an automated review that flags risky changes before merge.
  • Postmortem draftsA timeline built from chat, alerts and deploys. An engineer edits and approves it.

Platform for AI workloads

Your own models and AI features run on the same reliable platform.

  • GPU node poolsNVIDIA GPU Operator, GPU sharing and scale-to-zero, so expensive nodes run only when needed.
  • Model servingOpen models served with vLLM or KServe, autoscaled on request queue length.
  • LLM gatewayOne entry point to Bedrock, Azure OpenAI, Vertex AI and self-hosted models, with keys, quotas and cost per team.
  • RAG infrastructure and LLM observabilityVector databases such as pgvector or Qdrant, and tracing of prompts, latency and cost with OpenTelemetry and Langfuse.
  • Read-only by defaultAI tools get read access. Changes go through pull requests.
  • Human approvalRollbacks and scaling are suggested; an engineer decides.
  • Your data stays yoursSelf-hosted models or model services in your own cloud account.
  • MeasuredCost per request and answer quality are tracked like any other service.

Start with an AI pilot: one use case, a few weeks, and a result you can measure, for example time to triage an incident.

Discuss a pilot
Process

From the first review to stable operations

Typical durations for a mid-sized platform. We agree the exact plan after the assessment.

  1. Assess

    1–2 weeks

    Review of clusters, pipelines, monitoring, recent incidents and cloud costs.

  2. Design

    1–2 weeks

    Target platform, tooling choices and migration order, written down as decisions.

  3. Build & migrate

    4–12 weeks

    IaC, GitOps and observability first, then services move over one by one.

  4. Operate & improve

    ongoing

    On-call, SLO reviews, upgrades and a monthly report on reliability and cost.

24/7 support

Support with clear rules

With a support plan, our engineers are on call around the clock for your clusters. Response times are written into the contract.

  • Monitoring we set up and watchAlerts route to our on-call rotation and to your team if you want.
  • Runbooks for known issuesEvery repeated incident ends with a runbook or an automated fix.
  • Upgrades on a schedule you approveKubernetes, add-ons and node images stay within supported versions.
  • Monthly reportIncidents, SLOs, capacity and cost, with recommendations.
Example response targets
PriorityCoverageFirst response
P1Production down24/715 min
P2Major degradation24/730 min
P3Minor issueBusiness hours4 hours
P4Request or questionBusiness hours1 business day

Final values are agreed in your support contract.

Stack

Open, well-known tooling

Mostly CNCF and open-source projects, so your team can hire for it and keep running it without us.

Kubernetes

  • EKS
  • AKS
  • GKE
  • OpenShift
  • RKE2
  • Talos
  • k3s

Infrastructure as Code

  • Terraform
  • OpenTofu
  • Crossplane
  • Ansible
  • Kyverno

Delivery

  • Argo CD
  • Argo Rollouts
  • Flux
  • Helm
  • Kustomize
  • GitHub Actions
  • GitLab CI

Observability

  • Prometheus
  • Grafana
  • OpenTelemetry
  • Loki
  • Tempo
  • Alertmanager

Platform

  • Backstage
  • Karpenter
  • KEDA
  • Cilium
  • cert-manager
  • External Secrets

AI

  • vLLM
  • KServe
  • GPU Operator
  • LiteLLM
  • Langfuse
  • pgvector
  • Qdrant
  • k8sgpt
  • MCP
Engagement

Three ways to work with us

Start small with an audit, or hand us the whole platform.

Fixed scope

Platform audit

A clear view of what to fix first, and why.

  • Clusters, IaC, CI/CD and monitoring review
  • Security and cost findings
  • AI use cases worth trying
  • Written report with a prioritised plan
Project

Platform build

A new platform, or a rebuild of the one you have.

  • Clusters and cloud resources as code
  • GitOps delivery and observability
  • Service migration with your developers
  • Documentation and team training
Ongoing

SRE & 24/7 support

We run the platform with you, or for you.

  • On-call with agreed response times
  • Upgrades and patching
  • SLO reviews and monthly reports
  • Continuous cost optimisation

Your accounts. Built in clouds you own. Your Git. All code in your repositories. No lock-in. Open-source tools, full hand-over.

FAQ

Questions we hear often

Do we need Kubernetes at all?

Not always. For a handful of services, managed container services such as ECS, Cloud Run or Azure Container Apps can be simpler and cheaper. If that is your case, we will say so in the audit.

Can you take over an existing cluster?

Yes. We start with an audit and an onboarding period: monitoring, access, runbooks and backups are checked before we go on call for it.

How does 24/7 support work?

Alerts from your platform go to our on-call rotation. An engineer acknowledges and works on the incident within the agreed response time and keeps your contacts informed until it is resolved.

Will AI tools change things in our clusters?

Not by default. AI tools get read-only access. Any change they propose goes through a pull request or an explicit approval by an engineer.

Where does our data go when we use AI features?

You choose: self-hosted open models inside your cluster, or the model service of your cloud provider (Bedrock, Azure OpenAI, Vertex AI) in your own account. We do not send your data to other services without your approval.

Do you work with on-premises infrastructure?

Yes. We build and run clusters on bare metal and VMware, including environments without direct internet access.

Do you work with companies outside Ukraine?

Yes. We work remotely in English and Ukrainian, and 24/7 support covers all time zones.

Contact

Tell us about your platform

A few lines about your setup and goals are enough to start. An engineer will reply.

Write an e-mail

Useful to include

  1. Where your workloads run today: cloud, on-premises, both.
  2. Number of services and teams.
  3. Main pain: incidents, slow releases, cost, upgrades.
  4. AI use cases you are considering, if any.
  5. Support needs: 24/7 or business hours.