As Large Language Models (LLMs) move from experimentation into production systems, I’ve noticed a recurring pattern across startups and SMEs:
Teams adopt AI quickly.
Visibility improves.
Then costs and governance risks quietly spiral.
After building an open-source tool focused on making AI data flows visible, I kept running into the same follow-up question from engineering leaders:
“Now that we know where the data goes… how do we control this sustainably?”
This article documents the motivation, design decisions, and lessons learned from building a second open-source tool — the LLM Cost & Risk Optimizer — focused on cost transparency, explainable governance, and auditability for real-world AI systems.
The Problem I Kept Seeing in Practice
As LLMs became embedded into products and internal workflows, several issues appeared repeatedly:
1. Unpredictable Costs
Usage-based pricing makes LLM spend difficult to forecast.
Teams often lacked:
- Visibility by application or team
- Attribution by model or provider
- Early warnings for abnormal usage
Costs were usually discovered after invoices arrived.
2. Growing Regulatory and Operational Risk
At the same time, organisations were increasingly exposed to:
- Cross-border data transfers
- Provider training policies
- Unclear data retention rules
- Incomplete anonymisation
In many cases, no single system documented these risks end-to-end.
3. Limited Governance Infrastructure
Most SMEs rely on:
- Spreadsheets
- Ad-hoc dashboards
- Vendor portals
- Informal documentation
These approaches don’t scale as AI usage expands.
What was missing was not awareness — it was tooling.
Why Existing Approaches Fall Short (for SMEs)
Most commercial governance platforms are designed for large enterprises.
They tend to be:
- Expensive
- Cloud-dependent
- Vendor-centric
- Opaque in their risk scoring
For smaller teams, this creates friction.
Either governance is ignored, or it becomes an external compliance exercise disconnected from engineering reality.
I wanted to explore whether a lightweight, engineering-led approach could work better.
Design Principles
Before building anything, I set several constraints.
1. Transparency Over Automation
The tool should explain why something is risky, not just label it.
No black-box scoring.
2. Deterministic and Auditable
All cost calculations and risk rules should be inspectable.
If a finding exists, the logic should be visible.
3. Local-First by Default
All data stays on the user’s machine.
No telemetry.
No external services.
No cloud dependency.
4. Professional Outputs
Findings should be suitable for:
- Internal reviews
- Technical audits
- Management reporting
Not just developer dashboards.
5. Narrow Scope
This is not a full compliance platform.
It focuses specifically on:
LLM usage, cost, and governance in production systems.
What the Tool Does
At a high level, the LLM Cost & Risk Optimizer provides four core capabilities.
1. Usage and Cost Analytics
It ingests structured usage data and calculates:
- Cost by application
- Cost by team
- Cost by model
- Cost by provider
This enables early identification of inefficient or risky usage patterns.
2. Explainable Risk Engine
A rule-based engine evaluates risks such as:
- Processing personal data with external providers
- Cross-border data flows
- Provider training policies
- Missing anonymisation
- Undefined retention policies
Each finding includes a plain-English explanation and mitigation guidance.
3. Governance Dashboard
A multi-page Streamlit interface enables teams to explore:
- Executive metrics
- Cost trends
- Risk alerts
- Provider profiles
- Compliance summaries
This bridges the gap between technical and non-technical stakeholders.
4. Audit Report Generator
The system generates:
- Structured technical reports
- Executive summaries
- Markdown and PDF artefacts
These are designed for internal governance and external review.
Architecture Overview
The system follows a modular, auditable architecture:
Streamlit UI ↔ FastAPI Backend ↔ SQLite Database
│
Processing & Analytics Engine
│
Cost & Risk Rule Engine
This separation ensures:
- Clear accountability
- Testable components
- Replaceable modules
- Long-term maintainability
The architecture mirrors patterns used in larger governance systems, adapted for SME constraints.
Handling “Unknowns” Explicitly
One important design decision was how to handle incomplete information.
In practice, teams often don’t know:
- Exact provider policies
- Data residency details
- Training data usage
- Retention periods
Instead of failing, the tool treats missing information as a governance signal.
Uncertainty increases risk.
Surfacing that uncertainty is often more valuable than hiding it.
What This Tool Is (and Is Not)
To be explicit, this project is:
It Is
- A governance visibility tool
- A cost control system
- An engineering-led audit framework
- A decision-support platform
It Is Not
- Legal advice
- An automated certification system
- A compliance guarantee
- A replacement for professional review
It supports better decision-making — it does not replace accountability.
Why Open Source
I chose to keep this project open-source for several reasons:
- Governance logic should be inspectable
- Risk rules should be debatable
- Cost models should be extensible
- Trust requires transparency
Open-source enables peer review and collaborative improvement, especially in sensitive domains like AI governance.
Early Learnings
Building and piloting the tool revealed several patterns.
1. Most Teams Underestimate AI Spend
When usage is visualised properly, hidden costs become obvious very quickly.
2. Governance Is an Engineering Problem
Effective AI governance depends more on system design than on documentation.
3. Explainability Builds Trust
Teams engage more when they understand the logic behind findings.
4. Professional Reporting Matters
Audit-ready outputs significantly increase internal adoption.
Governance tools are more effective when they integrate with existing workflows.
From Visibility to Governance
This project builds directly on my earlier work on AI data flow visibility.
The progression reflects a broader pattern:
- Understand where data goes
- Understand what it costs
- Understand what it risks
- Establish sustainable controls
AI adoption fails most often at step four.
Technical systems scale faster than governance processes.
This tool attempts to close that gap.
What’s Next
The project is currently in local MVP stage.
Planned enhancements include:
- Automated optimisation recommendations
- Advanced compliance rule libraries
- Historical anomaly detection
- Role-based access control
- Alerting integrations
The focus remains on keeping the system transparent, explainable, and engineer-friendly.
Repository
The project is available here:
👉 https://github.com/harisraja123/LLM-Cost-Risk-Optimizer
Feedback, issues, and contributions are welcome.
Closing Thought
AI systems are becoming core infrastructure.
We already apply discipline to:
- Cloud platforms
- Data pipelines
- Security systems
LLMs deserve the same treatment.
Sometimes the most valuable innovation isn’t adding more intelligence — it’s making existing systems understandable, auditable, and sustainable.
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