Do you need write access to our cloud accounts for a cost review?

No. The review itself needs only read-only access to billing and usage data, with one of your engineers alongside. Changes come afterwards, made with your team and through your own repositories where the setting lives in code.

Do you promise a percentage saving?

No. We report the run-rate change per cost line once it shows on the bill, not a projection. Some reviews find that part of the setup, such as existing savings plans, is already right, and we say so.

Can you reduce our logging and monitoring bill?

Usually. The first step is measuring ingestion per service. After that the fixes are exclusion filters that match what services really write, retention set per environment, and paid features switched off once someone confirms nobody uses them.

Do you work on Kubernetes costs?

Yes, on managed Kubernetes such as EKS and GKE: requests and limits from measured usage, node pool sizing, and autoscaling, including scale to zero for queue-driven workers.

Can a cost review run on its own?

Yes, as a fixed-scope assessment. Cost also comes up during migrations and compliance work, and then the fixes land in the same Terraform as the rest of the project.

Cloud cost optimization for AWS, GCP and Kubernetes

We find what is actually driving your bill, fix the cause in code and configuration, and report what changed, including what did not.

Cloud bills rarely grow for one dramatic reason. More often it is logs nobody reads, capacity sized for a peak that seldom comes, or commitments bought without a usage history. We start by reviewing billing and usage alongside one of your engineers, tie each large cost line to the service or setting behind it, and make the changes in your Terraform and pipelines so they stay made. On one B2B SaaS platform on GKE, a single service produced more than half of the log volume, and four paid monitoring modules nobody used were switched off once the budget owner and the engineer both agreed.

Capabilities

Cost review — A read-only pass over billing, tagging and usage data with your engineer, ending in a short list of cost lines, the cause behind each, and the change that addresses it.
Logging and monitoring spend — Ingestion measured per service, exclusion filters that match what services really write, retention set per environment, and paid monitoring features checked against who uses them.
Right-sizing and scale to zero — Autoscaling on the signal that matters, such as queue depth, scale-in that waits for in-flight work, and test environments that shut down when nobody is using them.
Kubernetes cost — Requests and limits set from measured usage, node pools matched to the workloads they run, and cost visible per namespace.
Commitments and savings plans — Savings plans and committed-use discounts checked against real usage before anyone buys more. Sometimes the answer is that the current commitments are already right.
Reporting finance can check — Run-rate changes per cost line against a stated baseline, with what did not improve reported as plainly as what did.

Technologies

AWS
GCP
Kubernetes
Terraform
AWS Lambda
Amazon SQS

Reducing logging and observability costs

Log bills hide in small details. An exclusion filter that matches info but not INFO lets through logs the team believes it dropped, and one chatty service can outweigh the rest of the platform. We measure ingestion per service first, then fix the filters and retention where the volume actually is.

Kubernetes cost optimization

Teams often set resource requests once and never revisit them, so clusters pay for capacity they do not use. We compare requests with measured usage, resize node pools, and scale queue-driven workers to zero when there is no work, using queue depth rather than CPU where that is the better signal.

AWS cost optimization without breaking production

A saving is not worth a lost job or a missed alert. On a regulated medical-device platform, we made queue workers scale to zero on SQS queue depth, with a check before shutdown so in-flight jobs finished first. Every change goes through your Terraform and pipelines, so it can be reviewed and rolled back like any other.

FinOps reporting that holds up

A cost report is only useful if finance can check it. We measure against a stated baseline, separate one-off effects such as the end of a migration overlap from lasting ones, and say which costs did not come down.

Frequently asked questions

Do you need write access to our cloud accounts for a cost review?: No. The review itself needs only read-only access to billing and usage data, with one of your engineers alongside. Changes come afterwards, made with your team and through your own repositories where the setting lives in code.
Do you promise a percentage saving?: No. We report the run-rate change per cost line once it shows on the bill, not a projection. Some reviews find that part of the setup, such as existing savings plans, is already right, and we say so.
Can you reduce our logging and monitoring bill?: Usually. The first step is measuring ingestion per service. After that the fixes are exclusion filters that match what services really write, retention set per environment, and paid features switched off once someone confirms nobody uses them.
Do you work on Kubernetes costs?: Yes, on managed Kubernetes such as EKS and GKE: requests and limits from measured usage, node pool sizing, and autoscaling, including scale to zero for queue-driven workers.
Can a cost review run on its own?: Yes, as a fixed-scope assessment. Cost also comes up during migrations and compliance work, and then the fixes land in the same Terraform as the rest of the project.

Find out what is driving your cloud bill

Tell us which cost lines worry you. One call is usually enough to decide whether a read-only review is worth doing.

Schedule a free consultation