Bring-up
Stand up research infrastructure on preferred providers
Start from the providers, credentials, and clusters your team already trusts.
For frontier labs, research engineers, and applied AI teams
Deploy, train, and run your own AI systems
Use preferred providers, keep keys local, and manage bring-up, runtime inspection, and spend from one local-first operating surface.
What research teams need to deploy, train, and run their own systems
Inspect
Ask infrastructure questions while experiments run
Check what is deployed, what resources are hot, and where failures live from the same control surface.
Guardrails
Review changes before they hit expensive infrastructure
See the execution plan before resources are created, modified, or destroyed.
Visualize
See topology, services, and dependencies together
Understand how training, serving, and support systems fit together without reconstructing the environment by hand.
Cost
Track cloud and model spend in one workflow
Keep infrastructure and model usage visible as work scales.
Extend
Bring your own research agents
Run custom Node.js or Python agents locally for your own tools and deployment logic.
Connect, bring up, and keep systems running
This page is built for labs and research teams that want to deploy, train, and run their own systems without giving up provider choice, key custody, or runtime visibility.
Step 1
Install and connect local credentials
Install Clanker Cloud and connect the providers, clusters, and keys your team already uses.
Step 2
Stand up and inspect the environment
Bring infrastructure online, scan the environment, and keep deployment context attached.
Step 3
Train, deploy, and keep the environment under control
Use one local-first control surface to bring systems up, inspect them while they run, and keep costs visible.
Research workflows that benefit most
Stand up training and inference environments faster
Move from preferred cloud accounts and clusters to a live environment without re-documenting every dependency, service, and support system by hand.
Inspect runtime state while experiments are active
Ask what is deployed, where failures sit, how topology is connected, and which resources are getting hot while workloads are still running.
Keep GPU and cloud spend visible during iteration
Research teams can tie provider choice, infrastructure shape, and model usage back to actual cost instead of discovering the bill after the run is already over.
Use the providers, clusters, and GPU paths you already trust
Bring local-first AI research infrastructure to the providers, clusters, and support services you already use for training, inference, and experimentation.
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Why teams choose local-first research ops
Preferred-provider control instead of forced platform lock-in
Teams can keep using the GPU and cloud providers they trust rather than reshaping research workflows around a single managed environment.
Local credential custody instead of shared hosted access
Clanker Cloud keeps credentials and AI keys under team control, which is a better fit for labs that treat access boundaries and data handling seriously.
Bring-up, runtime, and cost context in one surface
The workflow connects training infrastructure, inference paths, topology, and spend so research teams do not need a separate tool for each stage.
Custom agents for lab-specific workflows
Teams can extend the product with their own Node.js or Python agents when experiments, internal tooling, or compliance requirements need something more specific.
What your research team gets with Clanker Cloud
If your team wants to move faster without giving up provider choice or key custody, Clanker Cloud gives you one local-first workflow for bring-up, training, deployment, and runtime operations.
Preferred providers
Use the cloud, cluster, and GPU paths your team actually wants.
Less bring-up drag
Keep research momentum by bringing environments online faster and inspecting them from the same control surface.
Visible spend
Keep GPU and cloud costs visible while systems run so teams can decide what is worth the bill.
One operating surface
Bring training, deployment, and runtime operations into one local-first workflow built for teams running their own systems.
For frontier labs · applied AI teams · research engineers
Keep provider choice and research velocity in the same workflow.
Keep credentials local, use preferred providers, and deploy, train, and run your own systems without handing control to a hosted platform.
One-minute setup for AI researchers
Connect the clouds, clusters, and keys you already use, then bring infrastructure online fast.
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Preferred providersLocal credentials and keysDeploy, train, and run your own systemsCustom research agents
Research FAQ
Can Clanker Cloud help with GPU workload and model infrastructure management?
Yes. It is designed to help teams bring up infrastructure, inspect runtime state, and manage cloud plus model spend across the providers they already use.
Do research teams have to centralize credentials in a hosted platform?
No. The product is local-first, so credentials and AI keys stay under team control rather than being handed to a hosted SaaS layer.
Is it only for training clusters?
No. It also fits inference infrastructure, supporting services, and the broader environment around deployment and runtime operations.
Can labs build their own research agents and workflows?
Yes. Teams can run custom Node.js or Python agents locally so the system fits their own experiment, deployment, and operating model.
Deep Research
Let AI models audit your infrastructure in parallel
Deep Research dispatches multiple AI models and specialised subagents across your entire cloud estate — covering cost, topology, security, and resilience in a single pass. Each finding comes with severity, evidence sources, and actionable next steps.
Explore more
Pick your next path
Choose the page that matches what you want to deploy or operate next.