AI visibility report
Traceloop ranks #9 in LLM Observability Evals & Gateways AI search.
Outside the top three on 16 of the 25 prompts buyers actually ask.
Braintrust is cited on 7 of those losses.
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Start free trial#9 among 11 vendors · still absent from 96% of tracked prompt responses
Top-3 citations across 75 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
Perplexity
Gemini Search
ChatGPTVisible, but narrative can improve. Traceloop ranks #9 on presence but #10 on sentiment. The brand appears relatively often, but competitors may be getting more favorable language when they appear.
Where Traceloop is losing
Prompts where competitors are visible and Traceloop is not.
These prompt-level losses are the first prompts to track and repair.
Where Traceloop is winning2
Which LLM observability tools show token usage, latency, and cost per step in an agent pipeline?
Avg # 1.0 · 1 platform
What platforms support replaying production traces in development for reproducible debugging?
Avg # 3.0 · 1 platform
Where Traceloop is losing5
Which LLM observability tools work with OpenTelemetry so I don't have to add yet another vendor SDK?
Competitors on 3 platforms
Track this promptWhich LLM eval platforms support running automated evaluations on production traces with custom metrics?
Competitors on 3 platforms
Track this promptWhat are the best tools for detecting hallucinations and faithfulness issues in RAG pipelines?
Competitors on 3 platforms
Track this promptWhich AI observability platforms can be self-hosted with one command using Docker Compose?
Competitors on 2 platforms
Track this promptWhat AI eval platforms support on-premise or VPC deployment for regulated industries?
Competitors on 2 platforms
Track this prompt
Track Traceloop daily before the next report refresh.
Track these gapsResearch dossierCapabilities, use cases, sources, reviews, pricing, and FAQ
Overview
Traceloop is an LLM observability and evaluation platform founded in 2022 and headquartered in Tel Aviv, Israel. Built by ML engineers from Google and Fiverr, it helps development teams monitor, debug, and continuously improve LLM-powered applications in production. Its open-source SDK, OpenLLMetry—built on OpenTelemetry—provides one-line-of-code instrumentation and became a widely adopted standard with over 6.8k GitHub stars and 500K monthly installs. The commercial platform adds built-in and custom evaluators, drift detection, CI/CD-integrated quality gates, prompt management, and an experiment framework. Traceloop supports 20+ LLM providers, major vector databases, and AI frameworks. It is SOC 2 and HIPAA compliant with cloud, on-prem, and air-gapped deployment options. In March 2026, Traceloop was acquired by ServiceNow to power its AI Control Tower governance platform.
Traceloop is an LLM reliability and observability platform that turns LLM logs, traces, and evaluations into a continuous feedback loop for production AI applications. Its core is OpenLLMetry, an open-source OpenTelemetry extension that instruments LLM calls, vector DB queries, and agent actions in Python, TypeScript, Go, and Ruby. On top of this telemetry layer, the Traceloop platform provides built-in quality evaluators (faithfulness, relevance, safety, PII/toxicity detection), trainable custom evaluators, real-time drift monitoring, automated CI/CD quality gates, prompt management, and an experiment framework for model and prompt comparisons—all deployable in cloud, on-prem, or air-gapped environments.
Key Facts
- Founded
- 2022
- HQ
- Tel Aviv, Israel
- Founders
- Nir Gazit, Gal Kleinman
- Employees
- 11-50
- Funding
- $6.6M
- Status
- Acquired by ServiceNow (March 2026)
Target users
Key Capabilities10
- OpenTelemetry-based LLM tracing via open-source OpenLLMetry SDK (Apache-2.0)
- Single-line-of-code instrumentation for prompts, responses, latency, and metadata
- Built-in evaluators for faithfulness, relevance, safety, PII detection, toxicity, and JSON/SQL/code validation
- Custom evaluator training using annotated production examples
- Real-time production monitoring with drift detection and quality alerts
- CI/CD integration for automated quality gates on pull requests
- Experiment framework for data-backed model and prompt comparison
- Prompt management registry with version control
- On-premises, air-gapped, and hybrid deployment options
- SOC 2 and HIPAA compliance
Key Use Cases8
- Production monitoring of LLM outputs for quality regressions and drift
- RAG pipeline tracing and debugging
- AI agent observability across complex multi-step workflows
- Automated prompt regression testing in CI/CD pipelines
- Model migration evaluation and A/B comparison
- LLM cost and latency tracking across providers
- Enterprise AI governance and compliance auditing
- Gradual rollout of prompt and model changes with data-backed confidence
Traceloop customer outcomes
IBM integrated OpenLLMetry with its Instana observability platform to monitor the performance of large language models running on Amazon Bedrock and IBM watsonx.ai, helping teams understand how AI applications behave in real-world conditions.
Miro uses Traceloop to gain real-world performance visibility across millions of conversations, flag critical edge cases at scale, and confidently experiment with and migrate to new models in production.
Recent Trend
How AI describes Traceloop3
...ports traces to any OpenTelemetry-compatible backend (Datadog, Grafana, Honeycomb, Jaeger, New Relic, etc.). \[2\] Python example: Python from traceloop.sdk import TraceloopTraceloop.init() After that, existing LLM calls are automatically traced.
What's the fastest way to start tracing my LLM application calls without rewriting my code?
traceloop +2
Which LLM observability tools show token usage, latency, and cost per step in an agent pipeline?
Real-time detection and alerting * Traceloop: Instrument RAG pipelines to emit per-request traces and fidelity signals to observability backends (e.g., OpenTelemetry, Grafana/Prometheus) for instant alerts on anomalous behavior.
What are the best tools for detecting hallucinations and faithfulness issues in RAG pipelines?
Most cited sources3
4Tools to Detect & Reduce Hallucinations in a LangChain ...
traceloop.com·Blog Post
- 3
Granular LLM Monitoring for Tracking Token Usage and Latency per ...
traceloop.com·Blog Post
- 3
Mastering the Maze: Tools for Tracing and Reproducing Non-Deterministic LLM Failures in Production | Traceloop - LLM Application Observability
traceloop.com·Blog Post
Alternatives in LLM Observability Evals & Gateways6
Traceloop positions itself as the open-standards-first LLM observability and evaluation platform, differentiated by its OpenTelemetry-grounded open-source SDK (OpenLLMetry) that became a de facto community standard with 6.8k+ GitHub stars and 500K+ monthly installs.
- Its core pitch is developer-simplicity ('one line of code, full observability') paired with enterprise-grade features (SOC 2, HIPAA, air-gapped deployment).
- Unlike proprietary observability tools, Traceloop avoids vendor lock-in by piping to 25+ existing observability backends.
- It targets enterprise teams needing continuous, automated eval-to-monitor feedback loops rather than ad-hoc spreadsheet-based quality checks.
- Traceloop was recognized as a Gartner Cool Vendor and was acquired by ServiceNow in March 2026 to power its AI Control Tower governance platform, signaling strong enterprise validation.
Reviews
Praised
- One-line-of-code setup and fast time-to-value
- OpenTelemetry open standards with no vendor lock-in
- Wide LLM provider and framework coverage
- Built-in evaluators requiring zero test configuration
- CI/CD integration for automated quality gates
- On-prem and air-gapped deployment flexibility
- Active open-source community and contributor base
- SOC 2 and HIPAA compliance for enterprise use
Criticized
- Narrow scope limited to LLM observability, not full ML lifecycle
- Free tier span and seat limits may be insufficient for production scale
- May overlap with existing APM and logging infrastructure
- Enterprise pricing is opaque and requires sales contact
- Future product roadmap uncertain following ServiceNow acquisition
No verified aggregate numerical review scores for Traceloop were found on G2, Gartner Peer Insights, or comparable platforms at the time of research. Community sentiment inferred from press coverage, investor quotes, and customer testimonials is positive, highlighting ease of integration, open-standards approach, and enterprise reliability. Analyst recognition includes a Gartner Cool Vendor designation. Third-party review aggregators list the product but do not yet surface verified scored reviews.
Pricing
Free tier available at $0/month supporting up to 50,000 spans/month, up to 5 seats, 24-hour data retention, and access to all core features including monitoring, evaluation, CI/CD integration, and prompt management. Enterprise tier is custom-priced (contact sales) and includes more than 50,000 spans/month, unlimited seats, custom data retention, SOC 2 compliance, on-premises deployment, and dedicated Slack support. OpenLLMetry open-source SDK is free under Apache-2.0 license and can connect to 25+ third-party observability platforms at no cost. Traceloop is available for purchase on AWS, GCP, and Azure Marketplaces.
Limitations
- Traceloop is purpose-built for LLM observability and evaluation; it does not cover broader ML lifecycle stages such as data preparation, feature engineering, or model training, requiring complementary tooling for end-to-end MLOps.
- The free tier is restricted to 50,000 spans per month and 5 seats with only 24-hour data retention, which may be insufficient for high-volume production workloads.
- Enterprise pricing is custom and not publicly disclosed.
- The platform may overlap with existing APM and logging infrastructure, requiring integration decisions.
- As of March 2026 the company has been acquired by ServiceNow, and future product roadmap and standalone availability are subject to change under new ownership.
Frequently asked questions
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | Gemini Search | ChatGPT | Perplexity |
|---|---|---|---|
Evaluation1/5 cited (20%) | |||
Which LLM platforms have the best workflows for human annotation and labeling of model outputs? | |||
What tools provide model-graded evaluation with calibrated reference-free scoring for chatbots? | |||
Which LLM eval platforms support running automated evaluations on production traces with custom metrics? | |||
What are the best tools for detecting hallucinations and faithfulness issues in RAG pipelines? | |||
Which evaluation platforms let me convert development-time evals into production guardrails automatically? | |||
Gateways & Routing0/5 cited (0%) | |||
What gateways have the lowest latency overhead when routing high-volume LLM traffic? | |||
Which LLM gateways are open-source and self-hostable for teams that don't want a SaaS dependency? | |||
Which AI gateways let me route between OpenAI, Anthropic, and open-source models with a single API call? | |||
What LLM gateway platforms support automatic fallbacks, retries, and load balancing across providers? | |||
Which AI proxies handle rate limiting, key rotation, and cost tracking across teams centrally? | |||
Production Readiness0/5 cited (0%) | |||
What AI eval platforms support on-premise or VPC deployment for regulated industries? | |||
What LLM monitoring platforms integrate with PagerDuty, Slack, or Datadog for alerting workflows? | |||
Which observability tools include real-time alerting on quality drops, not just latency? | |||
Which AI guardrail platforms provide pre-execution intervention to block unsafe agent actions before they run? | |||
Which LLM observability platforms scale to billions of traces per month at enterprise volumes? | |||
Setup & First Run0/5 cited (0%) | |||
Which AI observability platforms can be self-hosted with one command using Docker Compose? | |||
Which LLM observability tools work with OpenTelemetry so I don't have to add yet another vendor SDK? | |||
I want to add eval tracking to my agent — which platforms have the simplest Python decorator-style integration? | |||
What's the easiest way to log every LLM call my app makes for debugging without changing my application architecture? | |||
What's the fastest way to start tracing my LLM application calls without rewriting my code? | |||
Tracing & Debugging2/5 cited (40%) | |||
Which LLM observability tools show token usage, latency, and cost per step in an agent pipeline? | |||
What platforms support replaying production traces in development for reproducible debugging? | |||
Which observability platforms offer the best agent execution tracing for multi-step LLM workflows? | |||
What tools let me drill into a single user session to debug exactly what my agent did at each step? | |||
Which AI observability tools surface unknown failure patterns I wouldn't have written tests for? | |||
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Braintrust | 26.7% | 26.4% | 2.7% | 0.0% | 26.7% | #8.5 | +0.39 |
| 2 | Confident AI | 13.3% | 8.0% | 0.0% | 4.0% | 13.3% | #5.0 | +0.37 |
| 3 | LangChain | 13.3% | 6.9% | 5.3% | 0.0% | 13.3% | #9.3 | +0.44 |
| 4 | Langfuse | 13.3% | 18.4% | 6.7% | 2.7% | 13.3% | #12.1 | +0.51 |
| 5 | Galileo | 12.0% | 10.9% | 0.0% | 12.0% | 12.0% | #5.5 | +0.52 |
| 6 | Arize AI | 12.0% | 13.8% | 0.0% | 0.0% | 12.0% | #12.9 | +0.45 |
| 7 | BerriAI (LiteLLM) | 5.3% | 2.3% | 4.0% | 0.0% | 2.7% | #9.0 | +0.40 |
| 8 | Helicone | 5.3% | 10.3% | 1.3% | 5.3% | 5.3% | #18.2 | +0.32 |
| 9 | Traceloop | 4.0% | 1.7% | 0.0% | 4.0% | 4.0% | #3.7 | +0.20 |
| 10 | Portkey | 2.7% | 1.1% | 0.0% | 0.0% | 2.7% | #11.0 | +0.42 |
| 11 | Patronus AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
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