AI coding assistant usage and cost analysis workspace

For

Engineering leaders and platform teams tracking AI coding assistant usage, cost and session activity

Solves

AI coding assistant spend and session activity sit across several vendor dashboards and local logs, so cost per task, model, project or pull request and the workflow signals behind it cannot be reviewed in one place.

Delivers

Reviewed usage and cost analysis linked to named owners

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$14,000 for the MVP, $47,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce manual reconciliation of AI coding assistant usage and cost while keeping session data under the buyer's control.

1. Aggregate token counts, model mix and session details from permitted AI coding sessions.
2. Break down spend by task, model, project, vendor, employee and pull request.
3. Process session data locally with no account or upload required.
4. Run as open-source software the buyer can inspect and self-host.
5. Browse session titles, timing and costing to find specific work sessions.
6. Show correction frequency, time to first edit and files reworked.
7. Expose the same data through CLI, desktop app, menu bar widget, self-hosted web dashboard and panel extension.
8. Identify waste such as cache bloat or retry tax, apply fixes and track savings.
9. Map AI spend to repository metrics such as pull request volume, code revisions and code rework.
10. Connect AI vendors, repository and employee data into a custom dashboard.
11. Answer follow-up questions in natural language to drill into spend and usage patterns.
12. Share dashboards with named colleagues under permissioning.
13. Track spend over time with views such as spend by vendor over time.
14. Show sub-agent activity, tool calls and session timelines in real time.
15. Trace full context for chained agent interactions, including what each agent saw and decided.
16. Install with minimal configuration against the existing setup.
17. Assign usage archetypes from observed session patterns.
18. Highlight sessions that fail or produce low-quality outputs.
19. Present a compact, shareable summary that makes patterns easy to scan.
20. Compare the reviewed result with the recorded baseline and value assumptions.
21. Capture corrections and named-owner approval before consequential use.
22. Export a versioned reviewed usage and cost analysis linked to named owners with source references and unresolved questions.

Everything these tools do, in one app

• Session usage analytics Aggregates token counts, model mix, and session details from AI coding sessions.Found in CodeBurn, Claude Code & Codex Usage Trading Cards by Rudel
• Cost breakdown by dimension Breaks down AI spend by task, model, project, vendor, employee, or pull request.Found in CodeBurn, AI Spend Console by Rippling, ClawMetry for OpenClaw
• Local data processing Runs entirely on the user's machine with no account or uploads required.Found in CodeBurn, Claude Code & Codex Usage Trading Cards by Rudel
• Open source Distributed as free, open-source software.Found in CodeBurn, ClawMetry for OpenClaw, Claude Code & Codex Usage Trading Cards by Rudel
• Session browser Displays session titles, timing, and costing information to find specific work sessions.Found in CodeBurn
• Workflow insights Shows how often the AI is corrected, time to first edit, and files reworked.Found in CodeBurn
• Multiple interfaces Provides CLI, desktop apps, menu bar widget, self-hosted web dashboard, and GNOME panel extension.Found in CodeBurn
• Waste optimization Identifies waste such as cache bloat or retry tax, applies fixes, and tracks savings.Found in CodeBurn
• GitHub output mapping Maps AI spend to GitHub metrics like pull request volume, code revisions, and code rework.Found in AI Spend Console by Rippling
• Custom dashboard Connects AI vendors, GitHub, and employee data to generate a custom dashboard.Found in AI Spend Console by Rippling
• Natural language queries Allows asking follow-up questions in natural language to drill into spend and usage patterns.Found in AI Spend Console by Rippling
• Shareable dashboards Enables sharing dashboards with anyone in the company with permissioning.Found in AI Spend Console by Rippling
• Time-based spend tracking Tracks AI spend over time with views like 'AI spend by vendor over time'.Found in AI Spend Console by Rippling
• Real-time agent dashboard Shows sub-agent activity, tool calls, and session timelines in real time.Found in ClawMetry for OpenClaw
• Context tracing Provides full context tracing for chained agent interactions, including logs of what each agent saw and decided.Found in ClawMetry for OpenClaw
• Zero-configuration install Quick integration with existing setup with minimal configuration.Found in ClawMetry for OpenClaw
• Behavioral classifier Assigns archetypes based on usage patterns from 20k+ sessions.Found in Claude Code & Codex Usage Trading Cards by Rudel
• Error and quality signals Highlights where sessions fail or produce low-quality outputs.Found in Claude Code & Codex Usage Trading Cards by Rudel
• Trading-card presentation Provides a compact, shareable summary that makes patterns easy to scan.Found in Claude Code & Codex Usage Trading Cards by Rudel

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted session logs, vendor usage records, repository metadata and team mappings

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted session logs

4. 3

   Vendor usage records

5. 4

   Repository metadata and team mappings

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed usage and cost analysis linked to named owners

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Session data stays on the buyer's machine; cost attribution and quality judgments remain with the named owner. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Data sources and permissions, Editable analysis workspace, Client report and delivery. Use a thumbnail gallery for reporting periods, a large central analysis canvas, and a right-hand panel for sources, constraints and comments. Let users compare periods and dimensions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant chart or session. Make the task-specific outcome reviewed usage and cost analysis linked to named owners visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, client comments, approval states, usage allowances, revision limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Buyer-owned session logs, vendor usage exports, repository metadata and team directories. Cloud storage, repository import/export and reporting destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: aggregate token counts, model mix and session details; break down spend by task, model, project, vendor, employee and pull request. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will engineering leaders and platform teams tracking AI coding assistant usage, cost and session activity use it to solve "AI coding assistant spend and session activity sit across several vendor dashboards and local logs, so cost per task, model, project or pull request and the workflow signals behind it cannot be reviewed in one place"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Reconciled spend per accepted pull request and reviewer hours per reporting cycle.
4. Measure, then decide. Track reconciled spend per accepted pull request and reviewer hours per reporting cycle; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One repository and one AI vendor; cost attribution and quality judgments remain with the named owner. Implement one approved input format, a bounded representative case set and the first two task modules: aggregate token counts, model mix and session details; break down spend by task, model, project, vendor, employee and pull request. Support the third module with operator review: show correction frequency, time to first edit and files reworked. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed usage and cost analysis linked to named owners. Retain the explicit scope boundary: One repository and one AI vendor; cost attribution and quality judgments remain with the named owner.

What the build depends on. Source upload and preview, asynchronous processing jobs, editable version history, reviewer access and tested export formats. High-fidelity attribution requires specialist engineering-operations QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One repository and one AI vendor; cost attribution and quality judgments remain with the named owner.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined repository and vendor scope. Offer a monthly production allowance after repeat demand. Quote complex multi-vendor or multi-organization rollouts separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed usage and cost analysis linked to named owners. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce manual reconciliation of AI coding assistant usage and cost while keeping session data under the buyer's control. Demonstrate a concrete reviewed usage and cost analysis linked to named owners using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering leaders and platform teams tracking AI coding assistant usage, cost and session activity professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed usage and cost analysis linked to named owners from a small authorized input set, with a transparent calculation of reconciled spend per accepted pull request and reviewer hours per reporting cycle and no promised savings.

The first 30 days

1. Week 1: interview five engineering leaders and platform teams tracking AI coding assistant usage, cost and session activity and inspect a recent example of AI coding assistant spend and session activity sitting across several vendor dashboards and local logs.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure reconciled spend per accepted pull request and reviewer hours per reporting cycle, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Reconciled spend per accepted pull request and reviewer hours per reporting cycle. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Reconciled spend per accepted pull request and reviewer hours per reporting cycle; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed usage and cost analysis linked to named owners. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved attribution rules, reporting constraints and review examples, together with reliable delivery for a narrow engineering-operations niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for engineering leaders and platform teams tracking AI coding assistant usage, cost and session activity. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

CodeBurn, AI Spend Console by Rippling, ClawMetry for OpenClaw and Claude Code & Codex Usage Trading Cards by Rudel are what buyers use today, alongside spreadsheets and vendor dashboards. Compare this product with the buyer's present method on reconciled spend per accepted pull request and reviewer hours per reporting cycle. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, log processing, storage, reviewer hours, client revision rounds and licensed source data. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed usage and cost analysis linked to named owners. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, session privacy and usage permissions. Named owners approve attribution rules and reporting scope. One repository and one AI vendor; cost attribution and quality judgments remain with the named owner. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.