Find your cloud savings in under 5 minutes
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Unified view for all your AI/LLM costs - models, licenses, workloads and the cloud infrastructure.
No credit card | No commitments | Read-only access
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Enter your work email.
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Explore free tools, practical guides, and expert sessions to help you choose the right models, optimize AI costs, and scale AI with confidence.

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CloudKeeper is the only outcome-driven AI & Cloud Cost Optimisation partner, delivering guaranteed, continuous savings across cloud and AI environments. Combining deep FinOps expertise, proprietary technology, and hands-on execution, we help organisations reduce spend by an average of 20%—without upfront costs or long-term commitments.

Certified AWS AI
Services Provider
Authorised Reseller of
Anthropic's Claude AI Models.
Claude Select Services Partner
Since no credit card is required, you won't be charged. Before the trial ends, our team will walk you through what you found, including your AI cost savings analysis, and help you decide whether to continue. If you choose not to, your access is revoked, with no commitment and no hidden terms.
Yes, we only need read-only access. The platform is built to be secure, compliant and enterprise-ready. CloudKeeper holds ISO 27001 certification and a SOC 2 report, so your data and cloud access are managed to globally recognized security standards. Our team also guides you through a secure onboarding to set up access the right way.
Onboarding is simple and takes about 5 minutes with no complex setup or engineering effort needed. To track usage and spend for AI tools (such as employee AI licenses), you'll need to connect those tools separately. This is a quick, one-time step.
Very little. Onboarding takes about 5 minutes with no complex setup. After that, the platform runs continuous optimization, and Forward Deployed Engineers can do the hands-on optimization work in your environment, so your team can stay focused on building.
Yes. Full-Stack FinOps for AI is multi-cloud by design and covers AWS, Azure, and GCP in a single dashboard.
Most tools cover one slice of AI spend, such as cloud infrastructure or LLM API bills. Full-stack means covering all of it in one place: employee AI licenses, model and API usage, inference, GPUs, and the underlying cloud infrastructure, so you see the true cost of running AI, not fragments of it.