Smarter cloud efficiency.Without losing .
CloudKnife helps teams make better cloud decisions, review them clearly, and automate them where policy allows.
€100k in recurring yearly savings identified against a €1.7M annual public cloud footprint in a reference enterprise deployment on Azure. One proof point, not the whole story: the same workflow is built for safe, explainable decisions in context across your environments, not just cost.
Optimisation slows down when safe decisions stay manual
Teams are not short on signals. They are short on time to turn those signals into decisions they can defend. CloudKnife is for platform and DevOps leaders who want review-ready analysis without giving up engineering ownership.
Releases keep landing, dependencies shift, and extra headroom is often the rational default under delivery pressure. The hard question is not whether something could be cheaper. It is whether a change is safe here, for this workload and its owners.
Teams are rarely short on graphs. They are short on time to stitch usage, behaviour, configuration, and risk into a call they would defend in front of the people who run production.
Every serious candidate still needs manual checking: what changed, what depends on it, how prod differs from non-prod, why the metrics look the way they do. That work is honest, slow, and easy to postpone.
CloudKnife exists to carry that burden: structured, explainable recommendations your engineers can review instead of reopening the same threads and spreadsheets each quarter.
Review before action. Less manual digging, same ownership of what goes live.
Prepares the analysis behind safe decisions
CloudKnife prepares the investigation teams normally do by hand, then surfaces explainable recommendations with impact, rationale, and context. Savings are an outcome, not the only lens.
- Explainable, reviewable recommendations
- Context-aware analysis across usage and risk
- Less repetitive optimisation work for engineers
A review queue for evidence, context, and recommendations.
Illustrative portfolio view. In product, priorities and opportunities match your environment.
From signals to safe, reviewable decisions
Most tools stop at signals. CloudKnife prepares the analysis in context, then turns it into explainable recommendations your team can review before any action.
CloudKnife brings together the same evidence a strong engineer would inspect before acting: utilisation, behaviour, configuration, cost, risk, ownership, and environment context. The goal is to reduce repetitive analysis, not to replace judgement.
That analysis becomes concrete suggestions with rationale, impact, and confidence, so you can decide what is safe to change here. Rightsizing, scheduling, and service-fit are examples. No unattended execution in the product today.
CPU stays below the safety buffer, and tail behaviour remains stable in the decision window after review.
Ownership and policy boundaries stay visible for audit and review.
- Evidence and assumptions
- Impact and affected resources
- Safety headroom and risk context
Illustrative review surface. Impact, safety, and comparison stay in one place for a decision.
Built for teams that need efficiency without overhead
Where cloud spend is meaningful and environments change often, but recommendations still need to read like a careful engineer prepared them, not like a raw alert feed.
Built for teams with real usage and real delivery pressure, but limited appetite for endless manual analysis. CloudKnife prepares the work so recommendations are specific, explainable, and reviewable, not another pile of signals to interpret alone.
- Meaningful cloud spend
- Fast-changing environments
- Limited process overhead
- Need for clear, reviewable recommendations
Helps MSPs deliver a clearer optimisation story to customers: explainable recommendations and a repeatable review cadence, without promising unattended changes in their tenants.
- Portfolio-level leverage
- More consistent optimisation
- Better customer reporting
- A stronger efficiency service offer
Built for teams that need control.
Explainable recommendations, read-only onboarding first, and a clear review path. Trust starts with visibility and judgement, not silent automation.
Today the product is about insights and recommendations. Execution is not automatic. Your team decides what to act on.
Rationale, expected impact, affected resources, and confidence stay visible so decisions are inspectable, not opaque.
CloudKnife combines usage, behaviour, configuration, risk, and environment context, not a single metric in isolation.
We connect with read-only access and narrow reader roles where supported, so analysis does not need broad write permissions. Deepest coverage is on Azure today; AWS and GCP patterns are on a quality-led roadmap, and we welcome early adopters who want to shape them.
Recommendations respect different expectations for production and non-production, where that matters for your environment.
A short request starts the conversation. We begin with read-only access where your cloud is supported, with the deepest onboarding path on Azure today. You get insights and reviewable efficiency recommendations in context. Teams stay in control. Review comes first, with policy-governed automation where allowed. On AWS or GCP and want to help shape support? Say so in your note.