Cloud Cost Optimization
Finds and removes cloud waste using a FinOps operating model so spend maps to real workload value and finance engineering and product make cost decisions on the same numbers.
Everything included under this practice line.
Spend baseline and unit economics per service tenant or feature
Rightsizing across compute storage and managed services based on real utilization
Commitment strategy across Reserved Instances Savings Plans and Committed Use Discounts
Storage lifecycle policy for object block and snapshot tiers
Idle and orphaned resource cleanup with guardrails so it does not grow back
Tagging and allocation model so every dollar has an owner
Kubernetes cost visibility per namespace workload and team
FinOps operating cadence with engineering finance and product on shared dashboards
The stack we reach for.
What the business gets, measured.
- Meaningful reduction in cloud run-rate spend without capping product velocity
- Predictable cost forecasts finance can defend
- Clear per-team accountability replacing a single lump bill
- Higher engineering awareness of cost as a design input
- Commitment coverage that matches real usage instead of hopeful forecasts
The specialists behind this practice line.
FinOps practitioners and cloud economists run the analysis with cloud architects engaged on rightsizing and architecture-level changes and data engineers pulled in on billing pipeline work. Engineering managers and finance partners join the review cadence so decisions get made in the same room instead of relayed by ticket.
Compose several capabilities into one engagement.
Cloud Consulting & Migration (AWS, Azure, GCP)
We plan the target landing zone then move workloads across in waves so nothing goes dark. Lift-and-shift where it makes sense replatform where the payoff is real.
DevOps Consulting
We audit how your team ships today find the actual bottleneck and fix it. Usually it's not the tooling it's the handoff between dev and ops.
CI/CD Pipeline Automation
Pipelines that build test scan and deploy on every merge without a human in the loop. Same pipeline for every service so nobody has to relearn it.
Infrastructure as Code (Terraform)
Every resource in a repo reviewed like application code. No click-ops no drift and no server that only one person knows how to rebuild.
Kubernetes & Container Orchestration
EKS AKS or GKE clusters set up so day-two doesn't become a fire drill. Sane defaults for networking RBAC upgrades and workload isolation.
Platform Engineering
Internal developer platform so product teams ship services without filing tickets. Golden paths for the common cases escape hatches for the rest.
Site Reliability Engineering (SRE)
SLOs tied to what users actually feel error budgets that gate risky changes and on-call rotations that don't burn people out. Postmortems that fix causes not symptoms.
Disaster Recovery & Business Continuity
RTO and RPO targets you can actually meet backed by restores we test on a schedule. Backups nobody has restored are not backups.
Let's talk
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