sizeless vs Traccia: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of sizeless and Traccia — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
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
Traccia
Traccia
OpenTelemetry-native observability for AI agents that also enforces policy at runtime, with cost attribution and EU AI Act evidence export.
Key features
- Runtime Policy Enforcement: Tool call limits, restricted model lists and cost guards run as soft or hard blocks that stop an agent mid-execution, not just alert after the fact.
- Unified Agent Registry: One source of truth listing every agent across frameworks and environments with version, health, ownership and sync status, with zero code changes per agent.
- OpenTelemetry-Native Tracing: End-to-end spans over every LLM call, tool use and agent decision, exportable to the OTel tooling you already run.
- Cost and Token Attribution: Input, output and embedding tokens priced and computed locally at span end, rolled up per agent and per task to show top spenders.
- PII and Sensitive Data Detection: Detects exposure of customer data in agent traffic and masks it before export, with init(redact_pii=True) as the switch.
- Prompt Registry and Evals: Version prompts, grade candidates against datasets and scorers, compare pass rates against production and promote with evidence attached.
- Compliance Evidence Export: One command produces export-ready packs for EU AI Act Art. 12 governance, Art. 14 human review and Art. 50 disclosure, plus HIPAA PHI inventory and labeled exports.
- Single-Call Instrumentation: pip install traccia and one traccia.init() connects the registry and starts exporting traces across LangChain, CrewAI, OpenAI Agents SDK, AutoGen and LlamaIndex.
Best for
- Agent Fleet Visibility: Replace a mix of LangSmith, custom Grafana dashboards and spreadsheets with one view of every agent regardless of framework.
- LLM Spend Control: Find which agents and tasks are driving cost, then set hard spend caps that stop runaway loops before the invoice does.
- Regulated Deployment: Produce audit-ready EU AI Act or HIPAA evidence packs on demand instead of reconstructing them under deadline.
- Data Leak Prevention: Catch and mask PII in agent traces before it leaves the system, with critical violations surfaced as alerts.
- Prompt Change Management: Prove a candidate prompt beats production on a scored dataset before promoting it, with the experiment kept as evidence.
- Model Access Governance: Block agents from calling restricted or unapproved models at runtime across an entire workspace or organisation.