Traceloop is live · free plan; paid plans on request · last verified 23 Oct 2026 · viability monitor
Traceloop
LLM reliability platform that pairs OpenTelemetry tracing with built-in and custom evals so production AI apps get caught before they break.
Traceloop earns its place when your tracing already speaks OpenTelemetry and you want evals wired into the same pipeline rather than bolted on afterward. The Apache-2.0 OpenLLMetry SDK means you're never trapped in Traceloop's backend — you can point it at 25+ other observability platforms, useful insurance given the ServiceNow acquisition announced March 2026. The tradeoff is the tier jump: the Free Forever plan gives 50K spans a month, 5 seats, and 24-hour retention, and everything past that — SOC 2, on-prem, custom retention — is a Custom Enterprise conversation. Budget accordingly, and compare against Langfuse or Arize before you commit.
Verdict generatedVerified 7d ago · liveness 62/100 · cite: rightaichoice.com/tools/traceloop
- ML and MLOps engineers debugging LLM failures in production
- Teams that already run OpenTelemetry and want LLM-specific tracing
- Engineering managers enforcing evaluation quality gates in CI/CD
- Product teams tracking model quality drift across releases
- Teams wanting a no-code LLM app builder rather than tracing and evals
- Teams without OpenTelemetry experience who want a fully managed black box
- Buyers who need deep workflow automation more than observability and evals
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Skip Traceloop if you want a no-code LLM app builder or a fully managed black box, or if your primary language is Go or Ruby and you need generally available SDK support rather than a beta.
Every LLM call and every vector database call counts as a span, so heavy retrieval workloads push you past the 50K monthly span allowance faster than the seat count suggests.
Free Forever at $0/mo fits a small team evaluating LLM observability with 50K spans a month, 5 seats, and all features open. Past that volume, or when you need SOC 2, custom retention, or on-prem and air-gapped deployment, you're on Enterprise at a Custom price. Compared with Langfuse's published usage tiers or Arize's self-serve plans, Traceloop's ceiling is more negotiating-room than sticker price — fine if you have procurement, awkward if you don't.
In short
Traceloop — LLM reliability platform that pairs OpenTelemetry tracing with built-in and custom evals so production AI apps get caught before they break. Best for ML and MLOps engineers debugging LLM failures in production, Teams that already run OpenTelemetry and want LLM-specific tracing, Engineering managers enforcing evaluation quality gates in CI/CD. Free to start; it also has paid plans whose price we could not confirm — see the pricing table.
What is Traceloop?
Traceloop — LLM reliability platform that pairs OpenTelemetry tracing with built-in and custom evals so production AI apps get caught before they break. Best for ML and MLOps engineers debugging LLM failures in production, Teams that already run OpenTelemetry and want LLM-specific tracing, Engineering managers enforcing evaluation quality gates in CI/CD. Free to start; it also has paid plans whose price we could not confirm — see the pricing table.
Traceloop is an LLM observability and evaluation platform for teams running AI applications in production. It combines OpenTelemetry-based tracing with quality checks that run on your real traffic, so you see prompts, responses, and latency alongside a read on whether the outputs are actually good. Setup is thin: install the Apache-2.0 OpenLLMetry SDK in Python or TypeScript (Go and Ruby are beta), or route calls through Traceloop Hub, its native OpenTelemetry-based gateway that adds visibility without changing application code. Built-in metrics such as faithfulness, relevance, and safety score automatically on your data with no test authoring; if those don't match your definition of quality, you annotate real examples and train a custom evaluator. Evaluations run on every pull request or in real time, and monitors fire alerts on quality drift. The dashboard covers monitoring, evaluation baselines, prompt management and a prompt registry, datasets, experiments, and guardrails. It supports LLM calls across 20+ providers (OpenAI, Anthropic, Gemini, Bedrock, Ollama), vector databases (Pinecone, Chroma), and frameworks including LangChain, LlamaIndex, and CrewAI. Deployment spans cloud, on-prem, and air-gapped, with SOC 2 and HIPAA compliance at the top end. In March 2026 Traceloop announced it is joining ServiceNow to fold LLM observability into its AIOps platform, which may accelerate development and widen the integration ecosystem. If you already run OpenTelemetry, this is a low-friction route to LLM-specific observability plus a CI feedback loop.
Key features
- One-line OpenLLMetry SDK setup for LLM tracing
- OpenTelemetry-based tracing for LLM and vector DB calls
- OpenLLMetry SDK is Apache-2.0 open source
- Traceloop Hub native OpenTelemetry gateway for LLM calls
- Built-in evals for faithfulness, relevance, and safety
- Custom evaluator training by annotating real examples
- CI/CD integration running evals on every pull request
- Real-time evaluations as your app runs
- Monitoring dashboard for prompts, responses, and latency
- Evaluation dashboard with model quality baselines
- Prompt management and prompt registry
- Datasets and experiments in the dashboard
- Monitors with real-time alerts on quality drift
- Guardrails for production LLM output
- Tracing in Python, TypeScript, Go, and Ruby (Go and Ruby in beta)
Behind the verdict
Traceloop's core argument is that observability and evaluation shouldn't be two systems. Tracing tells you the request path — every LLM call, every vector database lookup, every framework step — and Traceloop layers quality scoring on top of that same span data. The built-in metrics (faithfulness, relevance, safety) apply automatically to live traffic, so a team that has never written an eval gets a baseline on day one. When the stock metrics don't match your definition of quality, you annotate real production examples and train a custom evaluator, then run it either in real time or on every pull request. That CI hook is the part most competing tools handle awkwardly: enforcing quality thresholds as a merge gate is a different job from a dashboard you check when something breaks. Strengths worth naming. One, the OpenTelemetry foundation. Traceloop doesn't invent a proprietary trace format; it emits standard spans, which means the OpenLLMetry SDK is portable to 25+ other backends and your instrumentation work isn't wasted if you leave. Two, language and stack coverage is broad — Python and TypeScript SDKs are generally available, Go and Ruby are beta, and there's support for 20+ providers plus Pinecone, Chroma, LangChain, LlamaIndex, and CrewAI. Three, deployment flexibility is real: cloud, on-prem, Kubernetes, and air-gapped, with SOC 2 and HIPAA at the enterprise end. Four, Hub lets you instrument by proxying LLM calls, which helps teams who can't easily rewrite application code. Weaknesses to weigh. The Free Forever plan is genuinely usable for a small team — 50K spans, 5 seats, all features open — but 24-hour retention limits how far back you can investigate a slow-burning quality regression, and every LLM call plus every vector DB call counts as a span, so volume adds up faster than you'd expect. SOC 2 compliance, on-prem deployment, and custom retention all sit behind the Enterprise tier on a sales-negotiated price, so regulated buyers can't graduate gradually. The docs are candid that Go and Ruby SDKs are beta, so polyglot shops should verify coverage of their primary language before standardizing. And the ServiceNow acquisition, announced March 2026, is an open question: it may accelerate the roadmap and expand integrations, or it may shift emphasis toward AIOps buyers rather than standalone LLM teams. The open-source SDK is your hedge either way. Where it fits: platform and ML engineering teams who already run OpenTelemetry, want evaluation gates in CI/CD, and need prompt management, datasets, and experiments in the same tool as their traces. Where it doesn't: teams looking for a no-code app builder, or teams that want a fully managed black box with no instrumentation work — Traceloop expects you to own the SDK.
Models under the hood
as of 2026-10-25
Limitations
- The Free Forever plan caps you at 50K spans per month with 24-hour data retention and up to 5 seats; every LLM call and every vector database call counts as a span, so volume accumulates quickly.
- Getting SOC 2 compliance, custom data retention, or on-prem deployment means moving to the Enterprise tier, which is priced on a Custom basis through a sales conversation.
- The platform is built on OpenTelemetry, so you must integrate the OpenLLMetry SDK or route through Traceloop Hub to get tracing at all.
- The docs state Python and TypeScript SDKs are generally available while the Go and Ruby SDKs are in beta, so language coverage varies by primary stack.
- Traceloop announced in March 2026 that it is joining ServiceNow, which may affect future roadmap and integrations — the Apache-2.0 SDK is portable to 25+ other backends if that becomes a problem.
as of 2026-10-13
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Is Traceloop still active?
Traceloop is live · free plan; paid plans on request · last verified 23 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is Traceloop? 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 →What do users say about Traceloop?
What people actually say about Traceloop — is it worth it?
Our community sentiment research written Jul 3, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
How we verified this
Every listed fact about Traceloop— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 23 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Traceloop 12 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.
- — changed switching in, hidden costs, limitations, integrations, who should skip it, setup time, our verdict, description, pricing fit, features, summary, switching out
- — changed our verdict, description, who it suits, features, who should skip it, pricing tiers, summary
- — changed limitations
- — changed limitations, hidden costs, switching in, features, switching out, summary, who should skip it, pricing tiers, our verdict, pricing fit, setup time, description, use cases, integrations
- — changed SSL certificate
- — changed SSL certificate
Showing the 6 most recent of 12 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Traceloop cost in 2026?
Traceloop: Free to start; it also has paid plans whose price we could not confirm — see the pricing table. Pricing last verified 23 Oct 2026.
Plans compared
Every published Traceloop tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Free | Free | — | Small teams or solo engineers evaluating LLM observability on a prototype or low-traffic app, with up to 5 seats and up to 50K spans a month.Adds: Free entry point: 50K spans per month, 5 seats, 24-hour data retention, monitoring and evaluation dashboards, CI/CD integration, and prompt management all included. |
| Enterprise | Custom | quote | Production teams past 50K spans a month, or regulated organizations that need SOC 2, custom data retention, on-prem or air-gapped deployment, and unlimited seats.Adds: Adds more than 50K spans per month, unlimited seats, custom data retention, SOC 2 compliance, on-prem deployment, and dedicated Slack support, at a Custom price. |
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.
Where the pricing makes sense
The company stage and team size where Traceloop's pricing actually pencils out — and where peers do it cheaper.
Free Forever at $0/mo fits a small team evaluating LLM observability with 50K spans a month, 5 seats, and all features open. Past that volume, or when you need SOC 2, custom retention, or on-prem and air-gapped deployment, you're on Enterprise at a Custom price. Compared with Langfuse's published usage tiers or Arize's self-serve plans, Traceloop's ceiling is more negotiating-room than sticker price — fine if you have procurement, awkward if you don't.
Has Traceloop's pricing changed?
No plan price for Traceloop has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Traceloop?
Pick Traceloop if you are ML and MLOps engineers debugging LLM failures in production, Teams that already run OpenTelemetry and want LLM-specific tracing, Engineering managers enforcing evaluation quality gates in CI/CD, Product teams tracking model quality drift across releases.
Look elsewhere if you are Teams wanting a no-code LLM app builder rather than tracing and evals, Teams without OpenTelemetry experience who want a fully managed black box, Buyers who need deep workflow automation more than observability and evals.
Skip it if Skip Traceloop if you want a no-code LLM app builder or a fully managed black box, or if your primary language is Go or Ruby and you need generally available SDK support rather than a beta.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Traceloop actually fits — and what changes day-one when you adopt it.
Installs the OpenLLMetry Python SDK in the app, gets live traces of every OpenAI call and Pinecone retrieval within an afternoon, and watches the faithfulness and relevance metrics populate on real traffic without writing a single test.
Outcome: A baseline quality score on production traffic the same day, plus the ability to replay a bad request as a local test case when a user reports a hallucination.
Wires Traceloop evaluations into the CI pipeline so every pull request runs the standard and custom evaluators, with a threshold that fails the build on a quality regression, and configures monitors to alert in Slack when drift appears in production.
Outcome: Regressions get caught before merge, and the team stops discovering quality drops from user complaints.
Adopts OpenLLMetry for instrumentation and deploys the full platform self-hosted on Kubernetes in an air-gapped environment to meet internal data-residency rules, using the evaluation dashboard, datasets, and prompt management in the same environment.
Outcome: LLM tracing and evals run inside the security boundary, without shipping production prompts and responses to a third-party SaaS.
Use cases
- Monitor LLM response quality in production and get alerts on drift or hallucinations.
- Run automated evaluations on every pull request to catch regressions before merging.
- Trace end-to-end requests across LLM calls, retrievals, and framework steps to debug failures.
- Track token usage and cost per user or feature to optimize spending.
- Train a custom evaluator that scores outputs according to your specific quality criteria.
- Reproduce a production issue in your IDE by replaying captured traces as test cases.
- Route LLM calls through Hub to get observability without changing application code.
- Self-host the full platform in a Kubernetes, on-prem, or air-gapped environment.
Setup time & first value
How long it actually takes to get something useful out of Traceloop — broken out by persona, not the marketing-page minute.
With the OpenLLMetry SDK: roughly an afternoon to first trace in Python or TypeScript, since it's a one-line install plus configuration. Routing through Traceloop Hub instead gets you observability without code changes, so first visibility can land within an hour. Go and Ruby SDK users should budget extra time to validate beta behavior. Self-hosted or air-gapped deployment is a Kubernetes project
Switching to or from Traceloop
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From ad-hoc spreadsheets and manual quality scoring: replace the spreadsheet with built-in faithfulness, relevance, and safety metrics that score live traffic automatically.
- →From a generic OpenTelemetry backend: keep your existing instrumentation and add LLM-specific traces, evals, prompt management, and monitors on top.
- →From LangSmith or Langfuse: swap the SDK for OpenLLMetry and point the exporter at Traceloop, keeping the Apache-2.0 instrumentation portable if you move again.
- →From no observability at all: install the OpenLLMetry SDK or proxy calls through Hub to get tracing without rewriting application code.
- ↗To another OpenTelemetry backend: the Apache-2.0 OpenLLMetry SDK exports standard spans, so you can repoint it at any of the 25+ supported observability platforms.
- ↗To Langfuse or Arize: re-instrument with the new vendor's SDK, accepting that custom evaluators and datasets don't transfer automatically.
- ↗To pure OpenTelemetry self-managed infrastructure: keep OpenLLMetry for instrumentation and drop the Traceloop backend, losing the evaluation dashboard and monitors.
Integrations
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “Traceloop”, and we withheld 6: 6 could not be judged, because “Traceloop” 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 Traceloop.
Official links
What are the best Traceloop alternatives?
The closest alternatives to Traceloop we list are Langfuse, LangSmith and Braintrust. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Langfuse | Freemium | Pricing not published | — |
| LangSmith | Freemium | Pricing not published | — |
| Braintrust | Freemium | Pricing not published | — |
Alternatives to Traceloop
View all
Langfuse
Open-source LLM observability: trace agents, manage prompts, and run evals on one MIT-licensed platform.
LangSmith
LangSmith is agent and LLM observability from the LangChain team: trace, monitor, evaluate, and deploy AI agents.
Braintrust
Braintrust traces agent runs, scores them with evals, and surfaces production failure patterns before they ship.
Tools that pair well with Traceloop
Common stack mates teams adopt alongside Traceloop, with the specific reason each pairing earns its keep.
Langfuse
Open-source LLM observability: trace agents, manage prompts, and run evals on one MIT-licensed platform.
LangSmith
LangSmith is agent and LLM observability from the LangChain team: trace, monitor, evaluate, and deploy AI agents.
Braintrust
Braintrust traces agent runs, scores them with evals, and surfaces production failure patterns before they ship.
Traceloop vs its closest rival
Traceloop's closest head-to-head is with Spider Cloud — Choose Traceloop if your priority is monitoring and evaluating LLM outputs in production with built-in quality checks and compliance. Choose Spider Cloud if you need to feed your AI agents with fresh web data via a fast, cheap scraping API. They solve different problems and can complement each other. Verdict generated
Head-to-head comparisons
Traceloop vs Spider Cloud
Choose Traceloop if your priority is monitoring and evaluating LLM outputs in production with built-in quality checks and compliance. Choose Spider Cloud if you need to feed your AI agents with fresh web data via a fast, cheap scraping API. They solve different problems and can complement each other.
GeneratedTraceloop vs Screenplayiq
ScreenplayIQ is a niche tool for feature film writers needing structured script analysis and market predictions, best for those who can pay for Pro/Studio. Traceloop is essential for any team building LLM-based products, offering comprehensive observability and evaluation. Unless you're exclusively in film production, Traceloop serves a broader, high-demand need.
GeneratedTraceloop vs Temporal Ai
Choose Temporal AI if you need durable, fault-tolerant execution for AI agents or long-running workflows and are willing to adopt a workflow-as-code model. Choose Traceloop if your priority is monitoring, evaluating, and debugging LLM outputs in production with minimal setup. They solve different problems — Temporal handles reliability of execution, Traceloop handles reliability of LLM outputs.
GeneratedFrequently asked questions
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