CodeBurn vs sizeless: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of CodeBurn and sizeless — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
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.
sizeless
sizeless
Turns a smartphone video of an open trench into a centimetre-accurate 3D point cloud, CAD as-built plan and GIS-ready digital twin of buried utilities.
Key features
- Smartphone capture: Field crews record an open trench with a standard iPhone Pro — no specialist scanning hardware and no separate surveying appointment
- Centimetre-accurate point clouds: Reconstruction algorithms developed at ETH Zurich build a high-resolution 3D point cloud of the excavation from the video alone
- Standards-compliant CAD output: Generates as-built plans in DWG and DXF, with couplings and pipe runs identified and measurements simplified
- 3D digital twin and GIS export: Produces a model of the pipe route including building entries that drops into existing GIS systems
- Works without GPS: Captures basement sections and building entry points where GNSS-based surveying fails
- Immediate backfilling: Because capture takes minutes, trenches close right after filming instead of waiting on a survey crew
- Documentation in about 72 hours: Complete records arrive weeks earlier than conventional surveying, enabling prompt connection billing
- Third-party utility capture: Records crossing utilities and as-laid geometry as unbroken 3D evidence, replacing hand sketches
Best for
- A utility network operator documenting residential service connections without booking a surveyor for every site
- A contractor closing a trench the same day instead of leaving it open pending a survey appointment
- Capturing a building entry point in a basement where GPS-based surveying cannot get a fix
- A district heating project producing as-built DWG plans for regulatory sign-off
- Spotting a laying error in the 3D point cloud before backfilling, while the fix is still cheap
- Feeding as-built pipe geometry into a GIS system for long-term network maintenance planning