Traceloop
LLM reliability platform with zero-setup evals and OpenTelemetry tracing.
Traceloop stands out for its zero-setup evaluations and OpenTelemetry-native tracing. The free tier is generous, but scaling past 50K spans per month requires a sales call. The ServiceNow acquisition could shift priorities, but for now it's a strong open-source-based option. If you need deep workflow automation or a no-code builder, consider alternatives like Langfuse or Helicone, but for reliability-focused teams, Traceloop is a solid pick.
Verified 15d ago · liveness 65/100 · cite: rightaichoice.com/tools/traceloop
- ML/MLOps engineers debugging LLM failures in production
- Product teams shipping LLM features with confidence
- Engineering managers enforcing quality gates in CI/CD
- Startups building production LLM apps on a budget
- Teams looking for a no-code LLM builder without tracing
- Users who need a free tier with more than 50K spans/month
- Organizations that prefer fully managed SaaS without on-prem options
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Skip Traceloop if you need a free tier larger than 50K spans per month, if you want a no-code LLM builder without tracing, or if you prefer a fully managed SaaS without on-prem options.
Going past 50K spans per month requires upgrading to Enterprise (custom pricing), which involves a sales call and likely a minimum contract.
Traceloop's free tier (50K spans/month) is generous for prototyping, unlike Langfuse's 100K events free tier. Enterprise pricing is custom, but for mid-size teams, it's comparable to competitors like Langfuse (paid tiers from $15/month) or Helicone (usage-based). If you need on-prem, Traceloop's Enterprise tier includes it, which many competitors charge extra for.
In short
Traceloop — LLM reliability platform with zero-setup evals and OpenTelemetry tracing. Best for ML/MLOps engineers debugging LLM failures in production, Product teams shipping LLM features with confidence, Engineering managers enforcing quality gates in CI/CD. Free to use.
What's new in Traceloop
Checked 6 days agoAcross the latest 1 update: 1 news mention.
What people actually say about Traceloop — 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.
3 mentions across 1 source (Hacker News) · researched Jul 3, 2026.
Average across the 1 source that answered — each source counts once, not each post.
- +Built on OpenTelemetry ensures wide compatibility and avoids vendor lock-in.
- +Auto-captures traces, metrics, and quality scores without code changes.
- +Pre-built evaluations for faithfulness, relevance, and safety save setup time.
- +CI/CD integration enables automated regression testing before deployment.
- +On-prem and air-gapped deployment options meet strict compliance needs.
- −Extremely limited community reviews makes it hard to assess real-world performance.
- −Acquisition by ServiceNow may reduce product focus or increase costs.
- −Learning curve for custom evaluator training could be steep for beginners.
- −More powerful for eval-focused teams than simple logging needs.
- −Pricing details beyond freemium are not transparent from community data.
- • Custom evaluator training may require extra compute or engineering time
- • On-prem deployment may incur infrastructure costs not included in subscription
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: September 2026
How we score →Key Features
- One-line code setup for LLM tracing
- OpenTelemetry-based tracing
- Built-in evals for faithfulness, relevance, safety
- Custom evaluator training via annotation
- CI/CD integration for automated evaluations
- Real-time monitoring dashboard
- Evaluation dashboard with baseline insights
- Smart proxy (Hub) for routing and observability
- Open-source OpenLLMetry SDK (Apache-2.0)
- Supports Python, TypeScript, Go, Ruby
- Compatible with 20+ LLM providers
- Support for vector DBs and frameworks
- Deploy on cloud, on-prem, or air-gapped
- SOC 2 and HIPAA compliance
- Prompt management and registry
About Traceloop
Traceloop is an LLM reliability platform for teams shipping AI applications in production. It combines OpenTelemetry-based tracing with automated quality evaluations, turning raw logs into actionable insights. With a one-line code setup using the open-source OpenLLMetry SDK, you get live visibility into prompts, responses, and latency. Built-in checks for faithfulness, relevance, and safety run automatically on your real data, and you can define custom evaluators by annotating examples—no test writing required. Evaluations can run on every pull request or in real time, so quality drift is caught before it reaches users. Traceloop also includes a smart proxy (Hub) for routing and observability, supporting 20+ LLM providers, vector databases, and frameworks like LangChain, LlamaIndex, and CrewAI. Deployment options span cloud, on-prem, and air-gapped environments, with SOC 2 and HIPAA compliance. In March 2026, Traceloop announced it is joining ServiceNow, which may accelerate development and expand its integration ecosystem. If you're already invested in OpenTelemetry, Traceloop offers a low-friction path to LLM-specific observability and evaluations, establishing a continuous feedback loop for your LLM systems.
Behind the Verdict
Traceloop's core strength is its combination of OpenTelemetry-based tracing with built-in quality evaluations. You get a monitoring dashboard and evaluation dashboard out of the box, and you can run evaluations on every pull request to catch regressions early. The ability to train custom evaluators by annotating real examples is a standout feature—it lets you define quality on your terms, not just rely on off-the-shelf metrics. The smart proxy (Hub) simplifies routing and adds observability without code changes. However, the free tier caps at 50K spans per month with only 24 hours of data retention, which is tight for active development. Scaling past that means a sales call, and there's no mid-tier plan. The platform is built on OpenTelemetry, so if you're not using it, there's a learning curve. The ServiceNow acquisition could bring resources, but it also introduces uncertainty about product direction. Traceloop is best for ML/MLOps engineers, product teams shipping LLM features, and engineering managers who want quality gates in CI/CD. It's especially good for teams already using OpenTelemetry. If you need a fully managed SaaS without on-prem options, or if you want a no-code LLM builder, look elsewhere—but for reliability-focused teams, Traceloop is a practical choice.
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Real-world workflow fit
Concrete scenarios for the personas Traceloop actually fits — and what changes day-one when you adopt it.
You integrate OpenLLMetry SDK with one line of code and immediately see traces in the dashboard. You enable built-in evals for faithfulness and relevance, and set up CI/CD integration to run them on every pull request.
Outcome: You catch a regression in prompt performance before merging, and the team ships with confidence.
You deploy Traceloop on-prem to meet compliance. You train a custom evaluator by annotating examples of good and bad responses, and set up real-time monitoring with alerts for quality drift.
Outcome: You get a baseline of model quality and alerts when it slips, reducing user complaints and debugging time.
You want to enforce quality gates in CI. You use Traceloop's CI/CD integration to run evaluations and enforce thresholds before merging.
Outcome: Regressions are caught automatically, and you have a clear feedback loop for improving prompts and models.
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.
Models Under the Hood
as of 2026-09-09
Limitations
- Traceloop's free tier caps at 50K spans per month with 24-hour data retention, and scaling past that requires a sales call.
- The platform is built on OpenTelemetry, so you need to integrate the SDK or Hub to get tracing.
- Custom evaluator training requires manual annotation of real examples.
- On-prem deployment is enterprise-only, and air-gapped setups may need additional configuration.
as of 2026-08-26
Verification history
We have re-verified Traceloop 8 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-checked, vendor evidence unchanged
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- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — 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
Showing the 6 most recent of 8 verification passes.
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 Traceloop 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
Ideal for
Startups and developers exploring LLM observability with up to 50K spans per month and 24-hour retention.
What this tier adds
Starting tier: free forever with 50K spans/month, 5 seats, 24h retention, all core features.
Enterprise
Custom
Ideal for
Enterprise teams needing >50K spans, unlimited seats, custom retention, on-prem deployment, and compliance.
What this tier adds
Adds unlimited seats, custom retention, SOC 2, on-prem deployment, and dedicated Slack support.
Where the pricing makes sense
The company stage and team size where Traceloop's pricing actually pencils out — and where peers do it cheaper.
Traceloop's free tier (50K spans/month) is generous for prototyping, unlike Langfuse's 100K events free tier. Enterprise pricing is custom, but for mid-size teams, it's comparable to competitors like Langfuse (paid tiers from $15/month) or Helicone (usage-based). If you need on-prem, Traceloop's Enterprise tier includes it, which many competitors charge extra for.
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.
For OpenLLMetry SDK, you can start seeing traces in under 10 minutes with one line of code. Hub setup may take 30 minutes to configure routing. Custom evaluators require annotation time—budget a few hours to train effectively.
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 Langfuse: Export traces from Langfuse and import into Traceloop to maintain history; use OpenLLMetry SDK to instrument your app.
- →From custom logging: Replace your manual logging with OpenLLMetry SDK to automatically capture spans and traces.
- →From Helicone: Similar tracing features; switch by updating your endpoint to Traceloop Hub or adding the SDK.
- ↗To Langfuse: Use the OpenTelemetry exporter to send traces to Langfuse's endpoint.
- ↗To Datadog: If using OpenTelemetry, you can export traces to Datadog's APM.
- ↗To self-built: Since OpenLLMetry is open-source, you can fork it and connect to any OTLP-compatible backend.
Integrations
Resources & Guides
- Documentationtraceloop.com
Docs · Traceloop
Full product docs from traceloop.com
- Documentationtraceloop.com
Llms · Traceloop
Full product docs from traceloop.com
- Documentationtraceloop.com
Introduction · Traceloop
Full product docs from traceloop.com
- Resourcetraceloop.com
Blog · Traceloop
Helpful link from traceloop.com
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
Tools that pair well with Traceloop
Common stack mates teams adopt alongside Traceloop, with the specific reason each pairing earns its keep.
Featured 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.
Traceloop 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.
Traceloop 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.
Alternatives to Traceloop
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TruLens
Open-source, OpenTelemetry-native agent evaluation and tracing that finds where your agent fails.
Opik (Comet)
Open-source AI observability for agent tracing, LLM-as-a-judge evals, and coding agent cost tracking
Arize Phoenix
Open-source LLM observability and evals for building reliable agents
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