AI coding cost audit

Reduce token-heavy coding-agent spend without slowing engineers.

Vorp Labs helps engineering teams make Claude Code, Cursor, Codex, Copilot, and custom coding agents cheaper and more reliable by fixing context, memory, prompts, MCP tooling, and model routing.

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Audit focusv1

Claude Code / Cursor / Codex workflows

Recall, project memory, and prompt reuse

MCP token overhead and tool routing

Open-source and cheaper-model substitution

No diagnosis required

You do not need to know where tokens are being wasted.

Most teams only know the symptoms: usage is rising, sessions feel long, engineers repeat context, MCP tools feel noisy, or leadership wants to understand what the spend is buying.

The audit maps the actual drivers: repeated context, prompt drift, tool overhead, missing memory, and workflows that should move to retrieval, deterministic scripts, smaller models, or reusable commands.

Where spend leaks

Coding agents get expensive when context is treated as disposable.

Repeated context rebuilds

Agents rediscover architecture, conventions, prior decisions, and debugging history every session instead of retrieving stable project memory.

Bloated tool surfaces

MCP servers, commands, and tool descriptions load too much schema or irrelevant capability into the context window.

Frontier model overuse

Every task routes to the most expensive model even when retrieval, deterministic code, embeddings, or open-source models would be enough.

Unmanaged prompt drift

Teams repeat long prompts by hand, lose effective patterns, and have no shared way to compare agent workflows.

What we inspect

A practical audit of the whole agentic coding loop.

The goal is not to tell teams to use fewer tokens. It is to make the right context available once, route work to the cheapest reliable path, and preserve what the team learns.

Usage and spend baseline

Map the current coding-agent stack, token-heavy workflows, cost centers, repeated tasks, and where cached context is or is not being used.

Context architecture

Review repo instructions, agent briefs, project docs, session-start patterns, compaction behavior, and recurring context that should become durable memory.

Recall and knowledge base

Design a practical memory layer for engineering teams: prompt library, prior decisions, debugging lessons, reusable workflows, and retrieval rules.

MCP and tool efficiency

Inspect MCP servers, command catalogs, schemas, tool descriptions, and routing so agents see the right tools without carrying every possible tool.

Model routing plan

Separate work that needs frontier models from work better handled by cheaper API models, open-source models, embeddings, search, or scripts.

Workflow playbook

Turn effective prompting and agent usage into repeatable team practice: task briefs, review loops, handoffs, eval cases, and reusable commands.

Packages

Start fixed-scope, expand only if implementation help is useful.

Fast diagnosis1 week

Audit package

A fixed-scope review of coding-agent usage, repeated context, prompt habits, MCP/tooling overhead, and model routing opportunities.

Best when the immediate need is a clear savings roadmap before changing team workflows.

Hands-on2-3 weeks

Audit + setup

The audit plus implementation help for repo instructions, prompt storage, recall/knowledge base setup, MCP cleanup, and routing changes.

Best when the team wants the first wave of changes implemented instead of handed over as recommendations.

Deeper build4-6 weeks

Agent workbench sprint

A more complete engineering-agent operating system for teams with heavy usage, multiple repos, custom tools, or internal platform needs.

Best when coding agents are already becoming part of the engineering platform.

Pricing posture

The audit is scoped as a fixed-fee package after a short usage review. Implementation work is scoped separately so the audit can stay honest about what is worth doing.

The target is payback from lower token waste, faster agent sessions, better reuse of team knowledge, and fewer repeated debugging loops.

Deliverables

AI coding spend map by workflow and tool class.
Context and memory architecture recommendations.
Prompt library and reusable agent workflow structure.
MCP/tooling cleanup plan with token-overhead notes.
Model routing matrix across frontier, cheaper API, open-source, retrieval, and deterministic paths.
Quick-win backlog for immediate savings and reliability improvements.

Good fit

Best for teams already feeling the token curve.

The audit is most useful when engineers are already using coding agents heavily, costs are starting to matter, or the same prompts, context, and debugging lessons keep getting recreated across sessions.

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