Scale and operate
Understand your cloud bill, then reduce unnecessary spend
We make cloud costs visible by workload, set baselines, and then optimize resources and queries without weakening performance, security or reliability.
In short
Cloud cost optimization starts with visibility: tagging resources and allocating cost to workloads. We then establish a baseline, rightsize compute, manage storage lifecycles, tune queries and track inference costs. Optimization preserves required performance, security and reliability, and benefits are claimed only against a baseline.
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01 The problem
What problem does this solve?
The cloud bill arrives as one large number. It is hard to say which product, team or workload is responsible, so nobody knows what to change first.
Cutting costs blindly can break something that mattered. Resources that look idle may be needed, and savings that weaken security or reliability are not worth having. A baseline has to come first, so any change can be measured.
02 What we build
What do we build?
- Resource tagging and cost allocation by workload
- Baselines such as spend against budget and cost per transaction
- Rightsizing and autoscaling of compute
- Storage lifecycle rules
- Query and data-transfer analysis
- Inference-cost tracking for AI workloads
- Budget monitoring and a backlog of improvements
03 Architecture
What does a typical architecture look like?
04 How it goes
How does the work usually go?
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Make costs visible
We apply resource tagging and allocate cost to workloads, so each part of the bill has an owner and a purpose.
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Set the baseline
We record spend against budget and unit costs, such as cost per transaction, before changing anything.
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Analyze the workloads
We look at compute, storage, data transfer and queries for waste, and rank the options by what they would change and what they put at risk.
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Change and check
We apply the agreed optimizations and check performance, security and reliability afterward, then compare the result with the baseline.
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Keep reviewing
Budget monitoring and a standing improvement backlog keep cost visible as workloads and usage change.
05 Capabilities and stack
Which capabilities and technologies are involved?
Capabilities
- Amazon Web Services
- Microsoft Azure
- Google Cloud
- Terraform
- Prometheus
- Grafana
The final stack follows your requirements and what you already run. How we choose technology
06 A typical situation
What does this usually look like?
A SaaS company's cloud spend has been climbing along with growth, and finance is asking where the increase comes from.
We tag resources, allocate cost by workload, set a baseline, and only then propose changes to be checked against it.
07 Questions
Questions people ask
We do not promise a figure. Savings depend on your workloads, so we set the baseline first and claim a benefit only against it. Forecasts are reported separately from measured results.
Cost optimization should preserve required performance, security and reliability, so each change is checked against them before it is kept.
It can be. If you want regular review, ongoing engineering support can include performance and cost optimization, agreed with its scope and responsibilities first.
If this sounds like you: Cut cloud waste without cutting reliability