Traccia
Traccia traces, costs, and governs AI agents from one control plane — enforcing policies before a call runs, not after.
The rare agent platform that acts before a call instead of filing a report after it. Preventive policies, Tool Guards, and Gateway enforcement for native OpenAI, Anthropic, and Gemini clients are the reason to pick Traccia over an observability-only tool. Just budget for Business if compliance evidence is the actual goal, since audit log, evidence packs, and the EU AI Act and HIPAA modules don't appear until $799/mo.
Verified 1d ago · liveness 81/100 · cite: rightaichoice.com/tools/traccia
- Platform teams running multi-framework agents that need one registry, one dashboard, and one place to set policy
- AI engineers who must block or reshape an unsafe LLM or tool call before it executes, not after
- Compliance leads who need export-ready EU AI Act Art. 12/14/50 or HIPAA evidence packs from a single hub
- FinOps and platform leads attributing AI spend per agent, model, trace, or workspace
- Solo builders testing a chatbot prototype — the Free tier's 50K events and 7-day retention is a trial run, not a debugging archive
- Teams with no agents in staging or production; the registry has nothing to discover
- Buyers who want a no-code agent builder — Traccia observes and constrains agents you already run
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Skip Traccia if you only need prompt-level debugging traces and don't intend to enforce runtime policy or produce compliance evidence, since guardrail alerts start at $299/mo and hard enforcement starts at $799/mo.
Exceeding the 500K events included on Observe costs $12 per 100K events, which adds up fast for multi-agent loops that fire many spans per request.
Fits platform and AI engineering teams in the 5–200 person range: Observe at $99/mo covers one production agent stack, Govern at $299/mo suits a team that needs policy alerts and 90-day retention, and Scale at $799/mo is where enforcement and hard spend caps arrive. There are no per-seat charges on any paid plan, which makes it cheaper than seat-priced observability suites at larger headcounts, but the event-overage ladder plus Enterprise-gated SSO and audit logs can push mid-size
In short
Traccia — Traccia traces, costs, and governs AI agents from one control plane — enforcing policies before a call runs, not after. Best for Platform teams running multi-framework agents that need one registry, one dashboard, and one place to set policy, AI engineers who must block or reshape an unsafe LLM or tool call before it executes, not after, Compliance leads who need export-ready EU AI Act Art. 12/14/50 or HIPAA evidence packs from a single hub. Free to start; paid plans from $299/mo.
What's new in Traccia
Checked 2 days agoAcross the latest 6 updates: 6 news mentions.
Traccia publishes Top 5 AI Agent Control Planes comparison for 2026
Buyer's guide compares five AI agent control planes across identity, observability, evaluation, governance, audit, and multi-model/framework/cloud support.
Using Jev for AI Evals in Traccia
Guide covers configuring a Jev Decision scorer in Traccia Evals, running dataset experiments, and invoking the scorer from Python or TypeScript.
AI Agent Evaluation: From Transcripts to Execution-Based Evidence
Research post argues agent evals need an oracle: observable state proving task completion within policy and cost bounds, not just final responses.
LLM Evaluation: Why It's the Foundation of Trustworthy AI Agents
Post argues reliable evaluation for autonomous agents requires measuring the full trajectory, not only the final response.
AI Agent Guardrails vs Policies: The Difference That Could Prevent Your Next Security Incident
Distinguishes guardrails (guide agent judgement) from policies (constrain agent authority), warning that conflating them lets investigative agents alter production.
Establishing Control Boundaries for AI Agents: Prompts vs Policy Enforcement
Post frames Traccia as an agent control plane for when system prompts fail, covering enforcement boundaries once agents gain real capability.
What people actually say about Traccia — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
41 mentions across 5 sources (Hacker News, YouTube, Product Hunt, GitHub, Lemmy) · researched Aug 28, 2026.
Average across the 5 sources that answered — each source counts once, not each post.
- +Vendor-neutral control plane works across OpenAI, LangGraph, CrewAI.
- +Open-source SDK lowers barrier to adoption and builds trust.
- +Centralized observability, logging, and audit for all agents.
- +Runtime policy enforcement and governance address rogue agent risks.
- +Concept of a 'crash dump for AI agents' resonates with developers.
- −Documentation gaps, e.g., missing dev dependency instructions.
- −Sparse community data; few in-depth technical discussions.
- −No pricing transparency, only 'contact' — worries small teams.
- −Support model unclear; no dedicated support channels or SLAs.
- −Early-stage with limited proof of production reliability.
- • No transparent pricing; enterprise costs likely high for scale
- • Potential additional costs for support, training, or integrations
Viability Score
How well maintained and how widely used is Traccia? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Unified agent registry syncing agents from LangChain, CrewAI, OpenAI Agents SDK, AutoGen, and LlamaIndex
- OpenTelemetry-native tracing of LLM calls, tool calls, and agent decision steps
- Export traces to Jaeger, Grafana Tempo, Zipkin, or any OpenTelemetry backend
- Cost and token attribution by agent, model, trace, and workspace across 2,500+ models
- Cost totals computed locally at span-end that stay accurate at 10% trace sampling
- Three-tier guardrail detection from explicit annotations, provider-native signals, and heuristics
- Preventive policies (Spend Cap, Model Boundary, Loop Cap, Refund Guard) that block mid-execution
- Traccia Gateway enforcing Spend Cap and Model Boundary on native OpenAI, Anthropic, or Gemini clients
- PII and sensitive data detection with redact_pii masking before spans are exported
- Prompt registry with immutable versions, protected production label, and SDK load_prompt
- Prompt playground comparing candidate vs production on latency, tokens, and cost
- Datasets, built-in checks, LLM-as-judge, code scorers, and Jev Decision scorers
- Experiment reports comparing baseline vs candidate with evidence attached for promotion
- Compliance Hub with audit log, AI system registry, and one-click EU AI Act and HIPAA evidence packs
- Claude Code and Cursor coding-agent sessions traced with per-turn cost
About Traccia
Traccia is an AI agent control plane — a single console where you observe, evaluate, govern, and audit agents you already have running. One traccia.init() call connects an agent to a registry and starts exporting traces; the registry tracks version, health, environment, and owner across LangChain, CrewAI, OpenAI Agents SDK, AutoGen, and LlamaIndex agents with no code changes. Agent dashboards show active agents, traces per minute, errors, policy violations, and token spend side by side, so platform teams stop juggling LangSmith, custom Grafana boards, and spreadsheets. Governance is the differentiator. Three-tier guardrail detection reads explicit annotations, provider-native signals from OpenAI, Anthropic, and Google, plus heuristics, and policies like Spend Cap, Refund Guard, and Loop Cap block hard violations mid-execution rather than alerting afterward. Spend Cap and Model Boundary also apply when native OpenAI, Anthropic, or Gemini clients point at the Gateway, no SDK needed. PII detection with redact_pii=True masks sensitive data before spans leave your process. The eval side covers prompts, datasets, scorers, and experiments: named prompts with immutable versions and a protected production label, side-by-side playground comparisons on latency, tokens, and cost, built-in checks, LLM-as-judge, code scorers, and Jev Decision scorers, plus immutable experiment reports so a promotion ships with evidence attached. Compliance lives in the Compliance Hub — an AI system registry, reviews, incidents, an audit log, and one-click evidence packs mapped to EU AI Act Art. 12, 14, and 50, with an opt-in HIPAA module for PHI inventory and labeled exports. Pricing is flat: no per-seat charges, and enforcement keeps running even if you exceed your event quota. Where LangSmith centers on tracing and evals and Azure AI Foundry on model lifecycle, Traccia is built for runtime enforcement of agents in production.
Behind the Verdict
Most agent observability tools answer "what did my agent do?" Traccia's pitch is narrower and more useful: stop it from doing the wrong thing in the first place. A Spend Cap or Loop Cap that hard-blocks mid-execution, a Refund Guard that denies one tool call, and a Gateway that enforces Spend Cap and Model Boundary for native OpenAI, Anthropic, or Gemini clients without the SDK — that's a control plane, not a dashboard. We'd reach for it when several agents across different frameworks are already in staging or production and nobody can say which one burned the budget or touched customer data. The unified registry and per-agent, per-model, per-workspace cost attribution solve that on day one, and cost totals computed locally at span-end stay accurate even at 10% trace sampling, which is better than most sampling-aware competitors manage. Evaluation being bundled on every plan is a quiet advantage. Prompts, datasets, scorers, and experiments come free, and the playground and LLM judges run on your own provider keys, so there's no token markup on top. The Jev Decision scorer work from October 2026 slots into that same experiment flow. Where it bites is the compliance cliff. If the reason you're shopping is an evidence pack for EU AI Act Art. 12, 14, and 50, the Compliance Hub, audit log, and HIPAA module all sit at Business ($799/mo). Team's 3 active policies and 90-day retention are fine for a pilot, thin for a regulated rollout. The closest alternative is LangSmith when you only need traces plus evals and already live in the LangChain ecosystem. Traccia earns its place once you need the block button. Teams that want a no-code agent builder should look elsewhere — this constrains agents you've built, it doesn't build them. One caveat we'd flag before purchase: the
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Real-world workflow fit
Concrete scenarios for the personas Traccia actually fits — and what changes day-one when you adopt it.
Runs pip install traccia and a single traccia.init() call over the staging and production services, then opens the registry to see all agents with environment, owner, and health attached.
Outcome: One inventory replaces the spreadsheet of agent owners, and cost attribution immediately shows which agent is driving LLM spend, computed at span-end so sampled traces don't distort the number.
Wraps the refund tool call with @govern() and sets a cost guard as a hard block, so the call is denied or reshaped before it executes rather than logged afterward.
Outcome: Unsafe actions stop mid-execution; the same trace that recorded the attempt supplies the evidence for the incident review.
Enables the HIPAA controls module for PHI inventory, confirms PII detection is masking patient names before spans leave the process, and exports an evidence pack from the Governance Hub.
Outcome: Art. 12 governance event spans, Art. 14 human review, and Art. 50 disclosure evidence are exported from traces the team already collected, without a separate compliance pipeline.
Use Cases
- See every AI agent you run, across frameworks and environments, in one registry with owner and health attached
- Attribute LLM token spend per agent, workflow, department, or customer instead of a single monthly total
- Stop an unsafe agent action at runtime with @govern() rather than reading about it in a log the next morning
- Enforce hard spend caps and restricted-model allowlists across OpenAI, Anthropic, and Gemini traffic through the Gateway
- Compare a candidate prompt version against production on latency, tokens, and cost before you promote it
- Catch patient names or credentials in traces and mask them before spans are written to logs
- Generate audit-ready EU AI Act or HIPAA evidence from traces you already collect
- Trace OpenAI Agents SDK runs including tool invocations and turn boundaries with one init() call
Models Under the Hood
as of 2026-10-18
Limitations
- Pricing is metered by stored events rather than seats: Free is $0/month with 50K events, 7-day retention, and 1 active policy; Team is $299/month with 2M events, 90-day retention, and 3 policies; Business is $799/month with 10M events, 1-year retention, and 15 policies; Enterprise is custom with unlimited policies and custom retention with legal hold.
- Only the Free plan limits users to 3, while every paid plan has unlimited users and workspaces.
- Several governance capabilities — Compliance Hub, audit log, evidence packs, EU AI Act and HIPAA modules, and the SDK-less Gateway — appear as unavailable on Free, Team, and Business, reserving them for Enterprise.
- Policy email alert recipients are limited to 1 on Free, 5 on Team, and 20 on Business.
as of 2026-09-28
Verification history
We have re-verified Traccia 5 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Traccia tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Team
$299/mo
Business
$799/mo
Enterprise
Custom
Ideal for
Regulated enterprises that need SSO, SCIM, audit logs, and a multi-year retention commitment on custom terms
What this tier adds
Adds SSO (Google Workspace, Okta, Azure AD, SAML), SCIM provisioning, audit logs, ten-year retention, volume-based overage pricing, and a 99.9% uptime SLA.
Where the pricing makes sense
The company stage and team size where Traccia's pricing actually pencils out — and where peers do it cheaper.
Fits platform and AI engineering teams in the 5–200 person range: Observe at $99/mo covers one production agent stack, Govern at $299/mo suits a team that needs policy alerts and 90-day retention, and Scale at $799/mo is where enforcement and hard spend caps arrive. There are no per-seat charges on any paid plan, which makes it cheaper than seat-priced observability suites at larger headcounts, but the event-overage ladder plus Enterprise-gated SSO and audit logs can push mid-size
Setup time & first value
How long it actually takes to get something useful out of Traccia — broken out by persona, not the marketing-page minute.
For an engineer with agents already running: SDK install plus traccia.init() is a same-day task, first traces in minutes, and the registry populates as agents are exercised. Add a few days to wire @govern() policies and spend caps on the paths that matter. Full Governance Hub setup — reviews, incidents, and evidence exports — is a multi-week effort scoped to your compliance deadline rather than
Switching to or from Traccia
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From LangSmith: keep your existing tracing calls and send spans to Traccia via the OpenTelemetry SDK, then compare dashboards before you retire the old workspace.
- →From Grafana Tempo or Jaeger: use the OTLP exporter already in Traccia's open-source SDK, since it writes to any OpenTelemetry-compatible backend.
- →From a spreadsheet of token spend: point traccia.init() at each service and let the registry auto-sync agents, replacing manual cost rollups with per-agent attribution.
- →From a homegrown gateway proxy: move model allowlists and hard spend caps to Traccia's Gateway so native OpenAI, Anthropic, and Gemini clients are covered.
- ↗To a self-hosted OpenTelemetry stack: the Traccia SDK is open source and exports to Jaeger, Grafana Tempo, Zipkin, or any OTLP collector, so traces keep flowing without the hosted platform.
- ↗To LangSmith: your trace data stays in the OTLP format, but you lose runtime policy enforcement and the EU AI Act evidence exports that Traccia generates.
- ↗To Azure AI Foundry: Traccia itself frames Foundry as model lifecycle and evals, so plan for a separate runtime-control layer if enforcement is a requirement.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Traccia”, and we withheld 6: 6 could not be judged, because “Traccia” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Traccia.
Official links
Featured Head-to-Head Comparisons
Traccia vs Mindgard
If your problem is coordinating many AI agents across vendors without losing control, Traccia is your control plane. If your problem is attackers probing those agents and models, Mindgard is your automated red team. Buy Traccia when you need orchestration and governance; buy Mindgard when you need continuous security testing and compliance evidence — they’re complementary, not substitutes.
Traccia vs Arize Phoenix
For AI platform teams that need multi-vendor orchestration and policy governance, Traccia is the control plane you'll want — but it's not something you'll run without an enterprise sales cycle. If you're an AI engineer debugging agent output and iterating on prompts, Phoenix's free, open-source observability and evaluation loop is immediately actionable — and its acquisition by Dynatrace signals deep enterprise backing. Pick based on your primary pain: controlling agents vs. understanding them.
Traccia vs Temporal Ai
If your pain is reliability—agents dying mid-task, lost state, manual retries—Temporal is the mature, battle-tested choice with a free tier and deep SDK coverage. If your pain is coordinating agents across multiple vendors and enforcing governance, Traccia's control-plane approach is intriguing but unproven (no pricing, no version details). For most teams, start with Temporal; revisit Traccia once it matures.
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