BearDrive vs CodeBurn: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of BearDrive and CodeBurn — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
B
BearDrive
Runbear
Open-source shared folder that syncs the files your team's AI agents create, with versions, authorship, and shareable links — no server required.
Key features
- Filesystem-native sharing: The shared surface is your local filesystem, so any AI agent that writes files can participate without a special SDK.
- Second-scale sync: Files sync to teammates within seconds of being written, so an agent's output is immediately available to another teammate's agent.
- Automatic versioning: Every synced change becomes a version that is kept, attributed to a user or agent, and restorable.
- Shareable web view: Every file gets a link you can drop into Slack; teammates open a web view of the file with no install required.
- Team history and authorship: See who — or which agent — produced each file and when, across the whole team.
- Open source, self-hostable: AGPL-3.0 with the server, teams, history, and links code publicly available on GitHub.
Best for
- Agent-produced research sharing: An analyst's research agent drops long-form HTML reports into a shared folder; a teammate's agent reads them the next minute.
- Design/data handoff: A design or data agent writes assets or CSVs at a stable path so downstream agents pick them up without brittle Slack uploads.
- Cross-agent workflows: One agent's output is another agent's input — BearDrive keeps the artifacts and their versions instead of scattering copies.
- Team memory for AI work: Teams keep a browsable history of everything their agents have produced, restorable and attributable.
- Self-hosted deployments: Regulated teams host BearDrive on their own infra to keep AI-produced files inside their perimeter.
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.