Local-first
Local-first control surface
Run AI-assisted infrastructure workflows from a desktop app using your existing credentials.
For DevOps engineers, platform teams, and SREs
Run multi-cloud infrastructure with local-first AI ops
Investigate incidents, review infrastructure plans before execution, and operate across clouds and clusters from one local control surface.
What your infra team needs to move faster
Guardrails
Safer infrastructure changes
Agents generate plans, show the proposed impact, and require explicit approval before resources are created, modified, or destroyed.
Investigate
Faster incident investigation
Ask what is failing, what changed, or where the cost anomaly is coming from and get a grounded summary of the environment state.
Kubernetes
Multi-cloud and Kubernetes visibility
See infrastructure state across AWS, GCP, Azure, Kubernetes, Cloudflare, Hetzner, and DigitalOcean in one place.
FinOps
Cost and optimization context
Track spend across providers and model usage so ops teams can understand infrastructure and AI costs in one workflow.
Extend
Custom agents and enterprise controls
Extend the system with Node.js or Python agents and move toward stronger auditability without abandoning the local-first model.
Connect your clouds, scan live infrastructure, and operate with approval
Lean DevOps teams need one workflow that spans infrastructure management, AI incident response, and multi-cloud operations without first centralizing privileged credentials in another SaaS layer.
Step 1
Connect your environments
Install Clanker Cloud locally and use your existing cloud accounts, cluster credentials, and API keys.
Step 2
Build live context
Scan resources, services, dependencies, topology, health, and cost across the providers you already run.
Step 3
Investigate and act
Inspect incidents, plan changes, optimize costs, and remediate issues with explicit approval before execution.
How lean infra teams use Clanker Cloud day to day
Investigate Kubernetes incidents with live context
Use one control surface for AI Kubernetes troubleshooting across EKS, GKE, and AKS instead of reconstructing cluster state from several consoles and terminal sessions.
Review change plans before touching production
Human-in-the-loop approval makes it easier to move faster without giving automation silent permission to create, modify, or destroy infrastructure.
Connect FinOps signals to operational decisions
Track cloud and model spend next to topology and incident context so teams can understand whether a cost spike is architectural, operational, or model-driven.
What teams use Clanker Cloud for
The strongest fit is a team that already operates across several services, clusters, or providers and wants less console sprawl with tighter review around changes.
AI Kubernetes troubleshooting across EKS, GKE, and AKS.
AWS Lambda and service debugging with execution context.
Safer infrastructure changes with explicit approval loops.
Multi-cloud cost visibility across cloud and AI spend.
Cross-provider operations from one local control surface.
Custom operational agents for your own workflows.
Why local-first beats hosted AI copilots for ops
Local-first instead of hosted credential custody
Clanker Cloud runs on your machine with your existing credentials, which is a better fit for teams that care about access control and minimizing vendor trust assumptions.
Provider-agnostic instead of single-cloud only
The product is built for teams that already span AWS, GCP, Azure, Kubernetes, and edge providers rather than living inside one vendor console.
Full lifecycle instead of point tooling
Provision, inspect, investigate, optimize, and remediate from one agentic infrastructure workflow rather than stitching together five separate tools.
BYOK instead of token markup
Bring your own AI keys and benefit directly from lower model costs instead of paying another layer of token resale margin.
Works with your existing stack
AWSGCPAzureKubernetesCloudflareHetznerDigitalOceanVercelBYOK
What your team gets with Clanker Cloud
If your team is stretched across clouds, clusters, and incidents, Clanker Cloud gives you one local-first workflow to investigate faster and operate with tighter control.
Unified context
Work across AWS, GCP, Azure, Kubernetes, and edge providers from one local control surface instead of losing time to console sprawl.
Faster investigation
Cut through alert noise with environment-aware investigation and plain-English ops workflows.
Less toil
Replace repetitive change work with reviewable plans and explicit approval before anything is created, modified, or destroyed.
Trusted control
Keep credentials local and operate with stronger trust boundaries instead of handing privileged access to a hosted copilot layer.
For DevOps engineers · platform teams · SREs
Operate faster without handing a vendor your credentials.
Install locally, connect existing credentials, and operate multi-cloud infrastructure with faster investigation and explicit review before execution.
One-minute setup for ops teams
Install the desktop app, point it at existing cloud and cluster credentials, and start investigating live infrastructure without provisioning a hosted layer.
macOSWindowsLinuxYour phone soonYour agent soon
Human-approved execution plansKubernetes support across EKS, GKE, AKSUnified cost and topology viewsLocal credentials, no hosted custody
DevOps FAQ
How is this different from a Kubernetes management tool or hosted copilot?
Clanker Cloud combines investigation, change planning, topology, and multi-cloud context in a local-first workflow instead of acting only as a hosted assistant or single-cluster dashboard.
Can a small DevOps team use existing cloud and cluster credentials?
Yes. The product is designed around the credentials and providers a team already uses, including AWS, GCP, Azure, Kubernetes, Cloudflare, Hetzner, DigitalOcean, and Vercel.
Does it help with incident response as well as infrastructure changes?
Yes. Teams can inspect incidents in plain English, understand environment state, and then move into a reviewed action plan when remediation is needed.
Can teams extend it for internal workflows?
Yes. Node.js and Python agents can be used to add environment-specific workflows, internal tooling, and stronger process alignment without giving up the local-first model.
Deep Research
Audit your entire estate before the next incident
Deep Research scans every connected provider in parallel — surfacing cost spikes, misconfigurations, single points of failure, and stale resources across your whole stack. Get a prioritised report with severity, evidence, and concrete actions instead of digging through dashboards.
Explore more
Pick your next path
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