AWS Cost Optimization and FinOps Review
The AWS or LLM bill is climbing faster than confidence, and nobody knows which usage is worth keeping.
Book a production reviewBest fit
Teams with real traffic, multiple AWS services, and enough spend that cost mistakes now affect roadmap decisions.
Timeline
1-3 weeks
Proof frame
Sanitized examples can be discussed with sanitized or NDA-safe detail.
Symptoms first, architecture second.
The useful work starts by naming what is hurting, what can be measured, and what can be changed safely this week.
What you may be seeing
- Bill spikes are discovered after finance asks
- Idle or oversized resources have unclear owners
- S3, logs, data transfer, NAT, or model calls grow silently
- Savings Plans or commitments feel risky because workload shape is unclear
How I find the cause
- Cost Explorer, CUR, tags, accounts, and workload ownership
- compute, database, storage, logging, NAT, and data-transfer hotspots
- serverless and LLM/token cost per business action
- commitment risk, lifecycle policies, and unit economics
The output has to survive handoff.
The point is not a prettier diagram. The point is a plan that names service boundaries, owners, rollback, cost drivers, and what gets observed.
I will not promise blanket savings without billing and utilization data. The useful answer is what to cut, what to keep, and what to measure next.
What you get
- - cost driver map
- - quick-win reduction list
- - commitment and right-sizing recommendation
- - LLM/token cost guardrails where relevant
- - FinOps operating cadence
What to bring
- - billing access/export
- - service ownership map
- - traffic or usage history
The language this service is meant to own.
These are not keyword decorations. They describe the buying problem this page is built to answer.
Case studies that support this service.
AI-Driven Development
ASTM International · Enterprise engineering enablement
Ad-hoc AI coding → governed Claude Code delivery loop with standards, app KBs, skills, commands, Jira context, and PR review gates.
- Program Model
- 4 steps
- Process Maps
- 7
- Artifact Plan
- 59 rows
GenAI RAG Platform
Enterprise Knowledge Base
30+ min document hunts → sub-10s answers; hybrid RAG + semantic cache cut LLM spend ~60%.
- Documents
- 2M+
- Retrieval
- <1s
- LLM Cost Red.
- 60%
Discovery & Loyalty Platform
Qraved Indonesia · Imaginato
PHP monolith with static feeds → serverless discovery on Lambda, DynamoDB, and GraphQL; 3x throughput, +18% CTR and +22% DAU via Amazon Personalize.
- Throughput
- 3x
- CTR
- +18%
- DAU
- +22%
Questions this service should answer before a call.
Do you guarantee a percentage of AWS savings?
No. The public narrative uses documented/sanitized savings ranges, but each engagement starts from billing and utilization data.
Can GenAI cost be included?
Yes. I inspect cost per request, retrieval design, semantic cache fit, model routing, prompt/context size, and eval-driven quality trade-offs.