Visibility and allocation
Establish where the money actually goes before proposing anything, at a granularity someone can own.
- Tagging standards and enforcement
- Account and subscription structure
- Showback and chargeback reporting
- Anomaly detection
FinOps & Cloud Cost
The biggest savings are architectural, not contractual.
Make cloud spend attributable and then reduce it: tagging that survives, cost per team and per feature, architectural savings rather than only commitment discounts, and a review cadence that holds.
Inside the delivery
Turn cloud spend into an engineering decision
A useful cost review connects billing to the workload, tests a change and checks whether the normalized cost improved without degrading the service.
- 01
Attribute spend
Map usage and shared costs to workloads and accountable owners.
Output
A cost baseline with known gaps
- 02
Assess candidates
Inspect utilization, architecture and commitments against performance needs.
Output
Ranked optimization candidates
- 03
Validate changes
Test an approved change with performance, reliability and recovery checks.
Output
An evidence-backed implementation
- 04
Measure & review
Compare like-for-like usage and assign a cadence for continued review.
Output
Verified costs and follow-up actions
Controls across the workflow
- Workload ownership
- Usage normalization
- Performance safeguards
- Commitment review
Decisions that shape the scope
- Unit cost or total bill?
- A growing workload can spend more while becoming more efficient. Select a meaningful denominator such as requests, jobs or active users before comparing periods.
- Commitment discount or architectural change?
- Commitments depend on forecast confidence. Architecture changes depend on engineering effort and workload behavior. Evaluate both without assuming a savings percentage.
- What data is needed?
- Billing exports, resource ownership and utilization help establish the baseline. Missing allocation data may make attribution the first deliverable.
Before you commit
Is this the right engagement?
Cloud costs are hard to attribute or architectural waste is preventing predictable spending.
- What we need from you
- Billing exports, workload owners, utilization trends, commitments and performance requirements.
- How you accept the work
- Compare normalized workload costs before and after an approved change and verify that performance and reliability remain within agreed bounds.
- Scope & alternatives
- Savings depend on workload behavior, commitments and engineering effort. We estimate candidates after assessment and report actual results separately.
Overview
Reserved instances are the easy half
Commitment discounts can reduce unit rates when usage forecasts and commitment terms justify them. They also lock in whatever architecture you already have. The savings that compound come from the design: the always-on cluster serving spiky traffic, the data being egressed twice, the environment nobody switched off, the retention policy set to forever. Those need engineers reading the architecture, not only a finance dashboard.
- 01Tagging, allocation, and showback
- 02Architectural cost reduction
- 03Commitment and rate optimization
- 04Forecasting and ongoing governance
Capabilities
Engineering scope and deliverables
Select the work that addresses your constraint. Responsibilities and acceptance criteria are agreed before delivery.
Architectural optimization
Find the spend that design decisions created, which is usually where the largest durable savings sit.
- Compute right-sizing and scheduling
- Storage tiering and retention
- Data transfer and egress patterns
- Managed service selection
Rate and commitment management
Take the pricing discounts available without over-committing to a shape you are about to change.
- Reserved and savings plan modeling
- Spot and interruptible workloads
- Licence and support review
- Renewal planning
Governance and cadence
Make cost a standing engineering concern rather than a quarterly panic.
- Budgets and alerting
- Cost in the design review
- Forecasting model
- Regular optimization cycle
Pricing
Engagement options and pricing factors.
A proposal follows discovery and identifies the deliverables, access assumptions, review responsibilities and milestones. Third-party platform and model charges are identified separately where relevant.
Discovery and scope
A growing workload can spend more while becoming more efficient. Select a meaningful denominator such as requests, jobs or active users before comparing periods.
Implementation
Deliver an agreed increment with the review and acceptance evidence described on this page.
Ongoing engineering
Agree a separate scope for maintenance, operational work or further development, including coverage and ownership.
Integrations
Selected for your environment
We select tools around your existing systems, data requirements and operating constraints.
AWS
Microsoft Azure
Kubernetes
Terraform and infrastructure tooling
Observability tools
Business intelligence tools
Frequently asked questions
Questions to resolve before starting
01How much can we expect to save?
How much can we expect to save?
We will not quote a percentage before seeing the environment, and anyone who does is guessing. The assessment gives a per-item estimate with the effort against it, so you can decide which savings are worth the engineering time.
02Is this a tool or a service?
Is this a tool or a service?
A service. Tools are useful for visibility and we will help you use the ones you have, but a dashboard does not resize a cluster or change a retention policy. The savings come from engineering changes made against what the dashboard shows.
03Will cost work slow our teams down?
Will cost work slow our teams down?
Not if cost visibility sits close to the teams making the decisions. What slows delivery is a central approval process; what works is engineers seeing what their service costs at design time.
04We already bought reserved instances. Is there more?
We already bought reserved instances. Is there more?
Usually a lot. Commitments reduce the rate for the architecture you have. The remaining savings are in what is running, how it scales, where data moves, and what is retained, but their value depends on your workload and the engineering effort required.
05Does this cover GPU and AI workload spend?
Does this cover GPU and AI workload spend?
Yes, and it is increasingly where the surprises are. Inference and training spend behaves differently from steady application load, and needs its own attribution, scheduling, and right-sizing approach.
Start a conversation
Plan your FinOps and cloud cost engagement.
Tell us the current environment, the constraint you need to remove, and the outcome you need to reach. We will map the technical path from there.