01
Cloud spend
Resource usage, environments, service mix, idle capacity, over-provisioning signals and ownership gaps.
Line 02 · Technical cost review intake
Request an AI & Cloud Cost Review
We review cloud, GPU, LLM, AI inference, and infrastructure cost concerns to identify where waste may exist — without touching production or making changes without approval.
Built for CTOs, CFOs, founders, engineering leaders, SaaS, AI startups, fintech, healthtech and cloud-heavy teams.
What we check
The cost signals that technical teams usually need to see together.
The review is designed to connect infrastructure, AI workload and finance visibility instead of treating each bill as a separate problem.
02
GPU / AI workload usage
GPU workload patterns, utilization questions, scheduling issues, batch opportunities and workload-to-cost visibility.
03
LLM / API cost
Model usage, token patterns, inference calls, embeddings, agent runs, vector storage and caching opportunities.
04
Storage, logs and backups
Storage growth, retention policy, monitoring/logging cost, backups, snapshots and duplicated data stores.
05
Cost visibility
Whether founders, finance and engineering can see top drivers, trends, product/team owners and cost allocation clearly.
06
Budget alerts
Budget thresholds, alert routing, anomaly review, escalation ownership and monthly review rhythm.
What we need from you
Enough context to understand whether a review is worth doing.
You can start with business and billing context. Production access is not required for the first conversation.
Company and technical context
- Company type and role
- Cloud provider
- Monthly spend range
- AI/LLM tools and vendors
- GPU usage, if any
Review context
- Biggest cost concern
- Whether reports or screenshots can be shared
- Preferred contact method
- Any timing pressure or budget review date
- Notes on performance or reliability constraints
What you receive
An honest, practical view of where to look next.
The output is a cost concern review, not a blind promise. If there is no obvious saving opportunity, we will say that.
A
Cost concern summary
A plain-English summary of the cost issue, likely drivers and the areas that need closer inspection.
B
Likely waste areas
Cloud, GPU, LLM, API, storage, logs, backups or visibility gaps that may deserve a deeper review.
C
Risk / effort view
Each suggested direction is framed by engineering risk, effort, approval need and reliability sensitivity.
D
Suggested next steps
Practical next actions for founders, finance and engineering teams to review together.
E
Honest recommendation
We recommend a deeper diagnostic only when the concern appears real enough to justify attention.
F
Approval-aware path
Any implementation work is separated from review and requires explicit written approval.
Safety promises
Cost control should not create production risk.
✓
Read-only review first
We begin with context, screenshots, reports or read-only information wherever possible.
✓
No production access required first
The first review can start without direct access to production systems.
✓
No changes without written approval
Any change recommendation is separated from implementation and needs approval before action.
✓
No guaranteed savings claims
We do not claim guaranteed savings, fixed percentages or fake benchmarks.
✓
Performance protected
Cost actions are reviewed against reliability, latency, security and customer impact.
✓
Customer owns data
Your reports, screenshots, architecture notes and billing context remain your data.
Start request
Submit cost review request
Share the minimum context needed to decide whether an AI & Cloud Cost Review makes sense. Avoid sending passwords, API keys, secrets or production credentials.