Visibility and attribution
Every dollar of cloud and AI spend mapped to teams, products, features and customers.
Engineering Excellence
FinOps & AI Cost Management
Attribute every dollar of cloud and AI spend, set budgets that enforce themselves, and engineer the waste out — without slowing teams down.
- 30–50%cloud and AI spend reduction typical
- 100%of spend attributed to teams and features
- <24hfrom anomaly to alert and action
Capabilities
Spend, seen and steered
Visibility, governance and engineering that keep cloud and AI spend aligned with value.
Budgets and guardrails
Limits that enforce themselves — alerts, throttles and approval gates before overruns, not after invoices.
Optimization engineering
Right-sizing, commitments, caching and routing — implemented, not just recommended.
Forecasting and unit economics
Spend forecasts tied to business drivers, and cost-per-action metrics leaders can steer by.
How it works
From mystery bill to managed resource
AI made cloud FinOps harder: token spend, GPU scarcity and per-request costs that scale with success. We bring engineering discipline to all of it.
Attribution
Know what everything costs, per feature
Tagging strategies, showback dashboards and AI-specific metering that attribute compute, storage and token spend to the teams and product features that incur them — ending the peanut-butter allocation.
- Tagging and metering across cloud and AI providers
- Cost-per-feature and cost-per-customer analytics
- Showback and chargeback your teams accept as fair
99.9%Uptime
1.2sp95 latency
$0.021Cost / task
QualityCostLatencyDrift
Control
Budgets that act, not just report
Anomaly detection catches the runaway agent loop at request one hundred; budget guardrails throttle or require approval before overruns; commitments get managed against actual usage curves.
- Real-time anomaly detection on spend velocity
- Automated guardrails: alert, throttle, approve
- Commitment and reservation management against forecasts
✓PII detected & redactedenforced
✓Answer grounded in sourcespassed
✓Role-based data accessenforced
✕Out-of-scope tool callblocked
SOC 2GDPRAudit logHITL
Optimization
Savings that are engineered, then verified
We implement the fixes — right-sizing, storage tiering, model routing, caching — behind quality gates, and track realized savings against the baseline so finance sees the difference land.
- Implementation, not a recommendations deck
- AI-specific levers: routing, caching, model right-sizing
- Realized-savings tracking against locked baselines
−62%p95 latency
Before
3.4s
After
1.3s
Cost / 1k tasks
$41
Optimized
$14
Use cases
Where FinOps engagements land
AI spend crisis
Token and GPU bills audited and cut fast without quality loss.
Cloud cost programs
Sustained 30%+ reductions across compute, storage and data.
Unit economics for AI products
Cost-per-action modeled and engineered against pricing.
Budget governance
Team-level budgets with self-enforcing guardrails.
Commitment strategy
Reservations and savings plans tuned to real usage curves.
M&A cost harmonization
Combined estates rationalized with visibility first.
Delivery
How we build it
We start from the business outcome, then design agents, models, tools and guardrails that can survive production — not just a demo.
- Production-ready architecture
- Secure tool integrations
- Measurable business KPIs
- Operate & improve playbooks
-
1
Discover
Map workflows, data, constraints and ROI.
-
2
Architect
Define models, tools, memory and trust boundaries.
-
3
Build
Ship a production-ready system with evals and observability.
-
4
Scale
Optimize cost, quality and adoption across teams.
We went from a bill nobody could explain to cost-per-feature dashboards the exec team reads weekly. Spend fell 38% — and for the first time, engineering and finance argue from the same numbers.
Works with your stack
Works across your providers
Cloud billing, AI provider metering and your data stack unified into one cost picture.
- AWS
- Azure
- Google Cloud
- Kubernetes
- Kubecost
- CloudZero
- Datadog
- Snowflake
- Anthropic Claude
- OpenAI
- Terraform
- Grafana
FAQ
Common questions
How is AI FinOps different from cloud FinOps?
New meters and new physics: token costs vary per request, GPU capacity is scarce and reserved differently, and spend scales with product success. Traditional tagging still matters, but AI adds routing, caching and model-choice as first-class cost levers.
Do you just report savings opportunities, or implement them?
Implement. Recommendation decks decay; we make the changes behind quality and reliability gates, then track realized savings against the baseline.
Will guardrails slow our developers down?
Good ones don't — budgets alert early, throttle at genuine anomalies and require approval only at thresholds you set. Teams keep autonomy inside visible limits.
What payback should we expect?
Most engagements find 30–50% addressable waste; the first tranche typically pays for the program inside a quarter.
Next step
Make every dollar of compute defensible
One assessment sprint quantifies your addressable waste — then we go get it.