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Your cloud bill is the second-biggest line item after payroll. We make it the third.
A FinOps practice for teams whose cloud spend has outgrown their visibility. Per-workload cost attribution, Spot-and-Reserved discipline, right-sizing automation, and the operating model that keeps spend from creeping back. Works across AWS, Azure, GCP, DigitalOcean, and Hetzner.
How we deliver this: AI handles the routine analysis (audits, IaC drafts, runbook scaffolds, alert triage). A senior engineer reviews every change before it touches your production. Consultancy speed at consultancy quality.
Read more →When you need this
Your bill grew faster than revenue
The CFO is asking questions engineering can't answer. Per-team and per-workload cost are invisible. Right-sizing is a manual chore nobody owns. We turn cost into a metric engineers see every day, not a quarterly surprise.
You tried Spot once and it broke
Most teams give up on Spot after one bad outage. The fix isn't avoiding Spot — it's wiring graceful interruption handling and putting Spot behind a Reserved baseload. Done right, Spot saves 60–80% without SLO regression.
Reserved Instances and Savings Plans look like a 1-year prison
Procurement-friendly commitments terrify engineering teams afraid of locking in the wrong instance family. We model commitment shapes against your actual usage curve and right-size the commit, not the workload.
How it works
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Phase 01
Cost visibility (Week 1–2)
Tagging schema, per-team / per-workload / per-customer cost dashboards, anomaly alerts. Engineering can see what they spent before the bill arrives.
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Phase 02
Quick wins (Week 2–6)
Right-sizing, unused-resource cleanup, storage tier transitions, idle-environment shutdown, NAT-gateway alternatives. Average client recoups 20–35% in this phase alone.
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Phase 03
Structural change (Week 4–12)
Spot adoption with graceful-interruption handling, Reserved/Savings-Plan modelling against real usage, autoscaling rewrites, multi-cloud arbitrage where it fits. Another 15–30% on top.
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Phase 04
Continuous practice (Month 3+)
FinOps as a culture, not a project. Cost-per-deploy metrics in CI, per-team chargeback, monthly review cadence, anomaly response playbook. Spend stays controlled after we leave.
What you get
- → Per-workload cost dashboard (Grafana / CloudHealth / native cloud cost tools)
- → Tagging schema enforced via policy-as-code (we install the policy guardrails)
- → Right-sizing recommendation engine wired to your usage data
- → Commitment plan (Reserved Instances / Savings Plans / Compute commits) modelled against real curves
- → Spot adoption runbook with graceful-interruption handling
- → Monthly FinOps review template and on-call rotation guide
What changes for you
30–60% cost reduction, typical range
Most clients see 30–60% reduction within 90 days. Best engagement to date — a Series B SaaS — hit 64% in 12 weeks ($180k/mo → $65k/mo).
Cost as an engineering metric
Engineers see cost per deploy, per workload, per customer. Cost becomes a thing engineers improve, not a thing finance complains about.
Multi-cloud where it actually saves money
We don't push multi-cloud religion. But when one workload runs 70% cheaper on Hetzner or DigitalOcean, we'll tell you — and ship the move if you decide it's worth the operational cost.
SLOs preserved
We don't cut cost by cutting quality. Spot adoption is paired with graceful-interruption handling; right-sizing is paired with autoscale headroom; commitments are modelled against your actual traffic shape, not aspirational forecasts.
A practice, not a project
When we leave, your team owns FinOps. Dashboards, runbooks, review cadence, ownership model — all transferred.
AI workload cost optimization, included
GPU spend is the new cloud cost frontier. Spot GPUs, model-serving right-sizing, prompt caching, KV-cache reuse — all in scope. We've cut AI inference spend 40–60% on multiple engagements.
What clients say
"CloudWizz rebuilt our delivery pipeline in eight weeks. Deploys went from a Friday-night ritual to a non-event we ship four times a day."
Director of Engineering
Fintech, Series C · 2025-11
"They turned a CFO emergency into a board-ready story in 12 weeks. The dashboards alone changed how engineering thinks about cost."
VP Engineering
Series B SaaS · 2026-01
Frequently asked questions
What does a typical engagement look like? +
8–14 weeks. Two-week assessment + six-to-twelve-week implementation. Many clients retain us at one or two days a week of advisory after the main engagement to keep the practice alive.
Which clouds do you cover? +
AWS, Azure, GCP (the big three) plus DigitalOcean and Hetzner (where we maintain open-source Terraform module libraries). Multi-cloud arbitrage is part of the engagement when it makes sense.
How is this different from your Infrastructure Audit? +
Infrastructure Audit is a two-week broad assessment that surfaces cost issues among other things. Cloud Cost Optimization is the implementation engagement — actually shipping the changes, with a continuous FinOps practice as the outcome. Many clients start with the audit and graduate to this.
Do you cover AI / GPU spend? +
Yes — GPU and inference cost optimization is a major part of recent engagements. Spot GPU adoption, model-serving runtime tuning (vLLM, TGI, Triton), prompt caching, KV-cache reuse. Typical AI workload savings 40–60% versus a stock setup.
How do you model Reserved Instances and Savings Plans? +
We pull 90 days of usage, fit a baseload curve, simulate commitment shapes (1y vs 3y, no-upfront vs partial, EC2 vs Compute Savings Plans), and pick the shape that minimises 12-month TCO while leaving headroom for growth. Re-modelled quarterly.
What about FinOps tooling — CloudHealth, Vantage, Apptio? +
We're vendor-agnostic. For most teams, native cloud cost tools (AWS Cost Explorer, GCP Billing, Azure Cost Management) plus Grafana dashboards are enough. We deploy commercial FinOps tools when there's a clear ROI — usually at $1M+ annual cloud spend.
Can you do chargeback / showback to internal teams? +
Yes. Tagging schema + per-team rollups + monthly cost report template. Cultural change matters more than tooling here — we help with both.
Will Spot adoption break our SLOs? +
Not if implemented right. Spot lives behind a reserved baseload. Workloads handle interruptions gracefully (drain, retry, checkpoint). For latency-critical user-facing traffic, Spot is overflow, not primary. We've shipped Spot-heavy stacks at 99.99%+ availability.
How do you handle data egress costs? +
Often the hidden cost. We map egress patterns (cross-AZ, cross-region, internet egress), recommend architecture changes (VPC endpoints, CloudFront, peering), and price out CDN strategies. Egress optimization alone can save $10k–100k/mo for data-heavy workloads.
What if our team is small and we can't own this long-term? +
We offer an ongoing FinOps-as-a-Service retainer — one or two days a week, monthly cost review, anomaly response, quarterly commitment re-modelling. Most clients keep this for at least a year after the main engagement.
How does the AI-driven, human-reviewed model apply to FinOps? +
FinOps is one of the cleanest fits for our model. AI parses cost reports, identifies waste patterns, drafts right-sizing recommendations, and surfaces anomalies — work that previously took a senior engineer days. A senior engineer then reviews every recommendation against your actual workload context (SLO requirements, traffic shape, team constraints) before anything ships. Total engagement is typically 30–50% faster than a body-shop FinOps consultancy, with the same — or better — quality of judgment.