Caveman vs CodeBurn: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Caveman and CodeBurn — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
C
Caveman
Julius Brussee
Efficiency stack that caches, compresses, and routes AI traffic to cut LLM output tokens by up to 65% with verified savings.
Key features
- Caveman Skill: MIT-licensed Claude Code skill that teaches 30+ agents (Claude Code, Codex, Cursor, and more) to answer in a compressed dialect, cutting output tokens ~65% while keeping code and errors byte-exact.
- Local Proxy Wrap: One command (`caveman claude`) launches your agent with recoverable local context compression — no account required, BYOK, engine stores original bytes before lossy replacement.
- Recoverable Context Compression: Engine recognizes logs, JSON, code, diffs, and tables, then sends smaller eligible context to the model and can restore the originals on demand.
- Agent SDK: `@caveman-ai/agent` TypeScript SDK adds catalog-price guards, per-request token bills, and eval-gated context plans to production agents.
- Cave Score & Ledger: Inferred local savings score and a verified 'causal-cache' ledger on paid tiers so you can prove cut tokens and cut dollars.
- Managed Cloud Gateway: Point traffic at one URL and caching / compression / routing run eval-gated on autopilot, with a synced savings dashboard.
- Browser Extension: Ships for ChatGPT, Claude, and Gemini so end-user chats benefit from the same output compression without any code changes.
- Enterprise & OEM: Same stack self-hosted in your cloud or datacenter with signed savings receipts, zero data retention, and OEM embed options.
Best for
- LLM Bill Reduction: Cap OpenAI, Anthropic, or Google spend without changing model choice by cutting output tokens per response across your agent fleet.
- Coding Agent Efficiency: Install the skill to make Claude Code, Codex, Cursor, and other CLI agents produce terse, byte-exact answers so long tasks fit in context.
- Provider Wrap for Production Agents: Use the SDK to add per-call token bills, catalog-price guards, and eval-gated context plans to LangChain / custom agents.
- Central Cost Gateway: Point every agent in the org at Caveman Cloud so caching and routing are enforced from one URL with a shared dashboard.
- On-Prem or OEM Embed: Ship the Enterprise stack inside a regulated network or embed it in your own AI product with signed savings receipts and zero data retention.
- Chat-App Compression: Install the browser extension for ChatGPT, Claude, or Gemini to keep casual chats short, cheaper, and inside the context window.
C
CodeBurn
AgentSeal
Free, local-first CLI and macOS menubar app that tracks AI coding token usage and cost across 36+ tools like Claude Code, Cursor, Codex, and Gemini CLI.
Key features
- Multi-tool cost tracking: Reads on-disk session files from 36+ AI coding tools including Claude Code, Cursor, Codex, Copilot, Gemini CLI, Kiro, OpenCode, and Goose and unifies them into one dashboard.
- Local-first architecture: No proxy, no wrapper, no API key, and nothing leaves the machine — everything is computed from the session files the tools already write.
- Task classification: Deterministically buckets every AI turn into categories like Coding, Debugging, Feature Development, Refactoring, and Testing without any additional LLM calls.
- Per-project and per-model breakdowns: Slices cost and token counts by project, model, tool, and task so developers can see which repos or models drive the bill.
- `codeburn optimize` grader: Grades the developer's setup A through F and flags duplicate file reads, context bloat, and ghost agents, with one command to apply the fixes and per-change undo.
- TUI and menubar UIs: Ships as both a terminal TUI dashboard for deep dives and a macOS menubar app for at-a-glance daily spend.
Best for
- Solo developer cost visibility: An indie dev running Claude Code and Cursor side by side sees which sessions burned the most tokens and adjusts their workflow.
- Team AI budget attribution: Engineering managers roll up per-project spend to attribute AI costs to specific product areas or clients.
- Debugging runaway sessions: When a coding agent burns thousands of tokens on one task, CodeBurn's classification pinpoints which turn category exploded.
- Optimizing agent setups: Running `codeburn optimize` on a laptop grades the AI setup and removes duplicate context that quietly inflates every prompt.
- Comparing model economics: Developers evaluate whether to move a task category from a frontier model to a cheaper one by looking at CodeBurn's per-model breakdown.