Traccia vs Visiby: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Traccia and Visiby — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Traccia

Freemium

OpenTelemetry-native observability for AI agents that also enforces policy at runtime, with cost attribution and EU AI Act evidence export.

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.

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.

FNA Technology

Paid

AI visibility platform that tracks how ChatGPT, Perplexity, Claude, Gemini and AI Overviews cite your brand, and ships fixes.

AI Citation Tracking: Continuously samples roughly 50,000 prompts per week across ChatGPT, Claude, Perplexity, Gemini and Google AI Overviews to record where and how a brand is cited.
Per-Engine Visibility Scoring: Reports a composite AI Visibility score plus share of voice and prompts won or lost, broken out engine by engine so declines can be traced to a specific model.
Prompts & Citations Explorer: Lets teams open any tracked prompt and read the actual model answer to see which competitor was named and why.
Brand Entity Analysis: Maps the adjectives each engine associates with your brand versus competitors and suggests reframing plays to change that portrait.
Competitor Intelligence: Tracks rival citation share on comparison and 'alternatives to' prompts, highlighting categories where a competitor dominates.
Prioritized Action Plan: Converts findings into P0/P1 recommendations such as schema additions or comparison pages, each with a time estimate and projected score gain.
Site Audit for AI Parseability: Audits pages for missing entity definitions, structured Q&A data and other signals that prevent models from citing the site correctly.
White-Label Reporting and API: Higher tiers add white-label client reports, SSO/SAML and API access for agencies managing multiple brands.

AI Search Monitoring: Marketing teams track whether ChatGPT and Perplexity recommend their product or a competitor on high-intent category prompts.
Competitive Benchmarking: Brands quantify how much citation share a named rival is capturing on 'alternatives to' and 'best of' queries.
Content Prioritization: Content teams decide which pages to write or refresh based on which prompts are currently missed rather than on keyword volume alone.
Technical AEO Audits: SEO specialists find pages lacking FAQ schema or entity markers that keep answer engines from parsing them.
Agency Client Reporting: Agencies run pooled prompt tracking across multiple client workspaces and deliver white-label AI visibility reports.
Executive Reporting: Operators present a weekly digest showing search clicks alongside AI citation share to explain traffic shifts leadership sees.