We Let 40 Engineers Loose With Coding Agents. The Bill Was Brutal.

Your engineering team adopted Claude Code last month. Productivity went up. Then the bill came in.

340% increase.

Nobody budgeted for this. Nobody even knew who spent what.

The math that kills budgets

A single coding agent session makes 50-200 API calls. Claude Sonnet 4 processes 100K+ context windows on every call. One developer running sessions all day burns $50-100.

Scale that to 40 engineers and you hit $20K/month in unexpected AI spend.

The root cause: raw API keys. No per-developer budgets. No team caps. No visibility. You find out about the problem when the invoice arrives.

What we did

We put recently. Fair comparison:

• Still in beta. LiteLLM has been in production since 2023.


• 7 providers vs 100+.


• Managed only. LiteLLM is self-hosted, your code stays in your VPC.


• Locked to LangSmith ecosystem. LiteLLM works with any observability stack.

For coding agents processing proprietary source code, the self-hosted part matters a lot.

Getting started

# Start proxy

litellm --config config.yaml



# Create team

curl -X POST 'http://localhost:4000/team/new' \

  -H 'Authorization: Bearer sk-master' \

  -d '{"team_alias": "engineering", "max_budget": 5000, "budget_duration": "1mo"}'



# Generate developer key

curl -X POST 'http://localhost:4000/key/generate' \

  -H 'Authorization: Bearer sk-master' \

  -d '{"team_id": "TEAM_ID", "key_alias": "dev-key", "max_budget": 100, "budget_duration": "1mo"}'



# Developer sets env var

export ANTHROPIC_BASE_URL=http://litellm-proxy:4000

export ANTHROPIC_API_KEY=sk-generated-key

15 minutes. Every coding agent call gets tracked, budget-checked, and attributed.

Full walkthrough with screenshots: docs.litellm.ai/blog/coding-agent-spend-control

Ran into similar agent cost problems? Curious what approaches other teams are using.