Cloud cost optimization services
Cut cloud waste without cutting performance. Cloud bills creep up until someone finally checks them. You’re struggling to scale. Or do you just need a reliable cloud cost optimization partner? We right-size resources, tune autoscaling, and cut idle spend across AWS, Azure, and Firebase.
Industry leaders we work with
Challenges we solve
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Our cloud cost optimization methodology
T.R.I.M
framework
Track (0-30 days)
We pull usage and billing data across every environment first. AWS, Azure, and Firebase spend gets mapped to real workloads, not guesses.
Rightsize (30-60 days)
Oversized instances get matched to actual traffic patterns. We adjust compute, storage, and reserved capacity against demand curves you've already generated.
Implement (60-90 days)
Recommendations turn into autoscaling rules, tagging policies, and automated guardrails. Changes roll out in stages, so nothing breaks production.
Monitor (continuous)
Dashboards track spend against thresholds you set, with alerts before overages happen. This is where cloud cost management stops being a project and starts being a habit.
Our cloud cost optimization framework
Cloud infrastructure review
We pull raw billing exports straight from your provider consoles. Every instance, bucket, and reserved plan gets logged in one sheet. No sampling, no estimates. We look at everything currently running.
Resource utilization analysis
CPU, memory, and I/O metrics get pulled over a 30-day window. We flag anything sitting below 20 percent utilization. Those numbers become the evidence behind every recommendation that follows.
Architecture assessment
We trace how services actually talk to each other. Redundant paths, orphaned load balancers, and stale endpoints surface here. For AWS cloud cost optimization services, we also check reserved instance coverage against real usage patterns.
Cost allocation review
Tags get checked against actual ownership records. Untagged resources get traced back through deployment logs. This step turns a vague bill into a spreadsheet your finance team can actually read.
Optimization opportunities
Every finding gets scored by savings size and rollout risk. Low-risk fixes go first. For Google Cloud cost optimization, for instance, that often means committed use discounts before anything more disruptive.
Governance assessment
We check who can provision what, and under which limits. Missing budget alerts and approval gates get documented. Cloud cost optimization for enterprises usually breaks down here, at the policy layer, not the infrastructure.
What you'll receive with our cloud FinOps services
How our cloud cost optimization framework works
We map every resource across your cloud accounts — compute, storage, networking, and reserved capacity. You get a complete inventory of what's actually running, not just what's on the bill.
We compare spend against real utilization over a 30-day window, provider by provider. You get a clear picture of where money is going versus where it's actually needed.
We flag idle resources, oversized instances, and coverage gaps in reserved or committed plans. You get a concrete list of what's driving waste, not vague warnings.
Each issue gets scored by potential savings and rollout risk before anything is recommended. You get a prioritized view of what to fix first versus what can wait.
Findings are compiled into a single document your team and finance can both read. You get clear numbers, owners, and next steps, no raw exports to decode.
We walk through the report with you and confirm which fixes move forward. You get alignment on the plan before any changes touch production.
Our tech stack
Amazon EC2
Amazon S3
AWS Cost Explorer
AWS Trusted Advisor
Azure Cost Management
Google Cloud Billing
Kubernetes
Terraform
AWS CDK
CloudHealth
Datadog
AWS Compute Optimizer
Not just FinOps consulting services. We fix what’s costing.
Why costs grow
What we’ll do
Over-provisioned instances. EC2, VMs, and Compute Engine sit sized for peak load that rarely hits.
We right-size against actual usage data, not projected capacity.
Idle resources. Unused volumes, orphaned IPs, and forgotten snapshots continue to be billed.
We audit every resource and cut what's not being used.
Unoptimized commitments. Savings Plans and Reserved Instances don't match real workload patterns.
We realign commitments to usage, not guesswork.
Underused Kubernetes clusters. Pods run well below allocated capacity.
We tune requests, limits, and autoscaling against real load.
Always-on dev and test. Non-production environments run 24/7 for no reason.
We schedule automatic shutdowns outside working hours.
Idle AI and GPU clusters. Expensive compute sits active outside training windows.
We scope GPU usage to actual training and inference jobs.
No cost ownership. Spend isn't tied to any team or feature.
We fix tagging so every dollar traces back to an owner.
Trusted by our clients
Dan Brooks
President, Krytter
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Bohdan Zabawskyj
Co-founder and CPO at Fortay.ai
Neil Winkworth
ex-CTPO, Location Live
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Joseph Heenan
CEO, Proteineer
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Mykola Bronitskyy
Co-founder, GrandBus
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Our related services
FAQ
Most clients see line-item changes within the first billing cycle after rightsizing ships. Idle resources get cut immediately; autoscaling and reserved-instance realignment take a few weeks longer to fully register. For one logistics client, EKS rightsizing alone cut idle spend before the second invoice arrived. Full savings, including governance and monitoring, usually stabilize within 60 to 90 days.
Azure and AWS need different tooling, but the underlying approach doesn't change. We pull data from Azure Cost Management instead of Cost Explorer, then apply the same rightsizing, tagging, and autoscaling logic. Reserved Instances become Azure Reservations; the savings math shifts slightly. Multi-cloud clients often run both playbooks in parallel, with one shared dashboard tracking spend across providers.
An audit is a snapshot; ongoing services keep working after the report lands. Our engagement includes continuous monitoring, alerting, and quarterly reviews, not a one-time PDF. That's the difference between cloud cost and performance optimization and a static assessment. Drift happens the moment nobody's watching, so dashboards stay live, and thresholds trigger automatically as usage shifts.
Hiring dedicated FinOps engineers takes months and adds permanent headcount for a problem that ebbs after the first quarter. Consulting gets you rightsizing, tagging, and governance built fast, then hands off a system your existing team runs. We've taken engagements from discovery to deployed automation in under 90 days, without adding a single full-time hire.
It's not always custom software. Most engagements wrap existing tools, like Cost Explorer, Kubecost, and Terraform, into one automated workflow tuned to your environment. Custom tooling only gets built when off-the-shelf options can't handle your specific stack, like multi-jurisdiction billing or unusual reservation structures. Either way, you end up with dashboards and alerts your team actually uses daily.
GPU and AI clusters are usually the biggest single line item, and the easiest to waste. We scope compute to actual training and inference windows, not always-on availability. For one client, idle GPU time outside scheduled training runs accounted for nearly a third of monthly spend. Scheduling, autoscaling, and spot-instance strategies typically cut that waste within weeks.
No. We work with read-only access the entire time, so nothing in production changes during the review itself. You get a full report: usage patterns, waste, and prioritized fixes, before any change gets made. Implementation only starts once you approve the roadmap, and even then, changes roll out in stages with rollback plans ready at every step.
What we’ll do next?
1
Contact you within 24 hours
2
Clarify your expectations, business objectives, and project requirements
3
Develop and accept a proposal
4
After that, we can start our partnership
