Depthdata vs sizeless: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Depthdata and sizeless — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
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Depthdata
Depthdata
Read-only AI spend and adoption intelligence — unify ChatGPT, Claude, Copilot, and Gemini admin data into one governed view of usage, cost, and outcomes.
Key features
- Unified AI Data Model: Normalizes usage, seats, and spend from ChatGPT, Claude, Copilot, Gemini, Cursor, Perplexity, and 16+ other tools into one schema.
- Workspace Health Score: A single 0–100 score rolls up adoption, depth, and spend across every AI tool your company uses.
- Cost per Outcome: Reports cost per active user, dormant-seat waste, and model mix using finance-grade math rather than vendor-reported figures.
- Coaching & Nudges: Detects what power users do differently, packages the patterns into playbooks, and routes nudges to the teams leaving value on the table.
- Depth-Based Leaderboards: Scores chained workflows, tool breadth, and retry patterns instead of raw prompt counts — the only way to game it is to get better at AI.
- Confidence-Labelled Analytics: Every KPI carries a confidence label and open methodology so board-deck numbers hold up under follow-up questions.
- Read-Only Admin OAuth: Connects via official admin APIs — no agents on laptops, no browser extensions, and prompts are never read.
- BI & Board Export: Push metrics to your BI stack or export board-ready reports with methodology attached.
Best for
- AI FinOps: CFOs and finance ops track cost per active user, idle-seat waste, and model mix across the entire AI stack in one ledger.
- Executive Reporting: Present the CEO or board a defensible workspace health score with trends and confidence labels instead of vendor screenshots.
- Enablement & Coaching: L&D and team leads target nudges at dormant seats using patterns learned from the workspace's own power users.
- License Right-Sizing: IT identifies which seats belong on premium tiers and which are already optimal, using measured depth-of-use.
- Cross-Tool Governance: Central IT gets one governed view of adoption and spend across ChatGPT, Claude, Copilot, Gemini, and future AI tools as they're added.
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