AI visibility report
AI visibility report for Traceloop in LLM Observability Evals & Gateways.
Outside the top three on 23 of the 25 prompts buyers actually ask.
Braintrust is cited on 19 of those losses.
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Start free trialStill absent from 96% of tracked prompt responses
Top-3 citations across 125 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
Perplexity
Gemini Search
Google AI Mode
ChatGPT
Bing CopilotHow to read this. Traceloop appears in 4% of tracked prompt responses. Presence is absolute coverage; share of voice is relative citation share; sentiment measures tone only when the brand appears.
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
What LLM observability tools do ML engineering teams typically use to annotate and review production traces for quality feedback?
Avg # 1.0 · 1 platform
Which LLM observability tools work with OpenTelemetry-compatible backends so we can consolidate LLM traces alongside existing service traces?
Avg # 2.0 · 3 platforms
Where Traceloop is losing5
I'm looking for an LLM observability platform with a great team collaboration workflow — where engineers and PMs can both review trace quality without SQL knowledge.
Competitors on 5 platforms
Track this promptWhich LLM eval platforms support async evaluation at scale without blocking the inference path or adding latency for end users?
Competitors on 5 platforms
Track this promptWhich LLM tracing platforms export trace data to a data warehouse so analysts can run custom eval queries alongside product metrics?
Competitors on 5 platforms
Track this promptWhich LLM observability tools handle PII redaction and data masking in traces for teams with HIPAA or GDPR compliance requirements?
Competitors on 4 platforms
Track this promptWhich LLM observability platforms stay reliable under traffic spikes from batch eval jobs running thousands of LLM calls simultaneously?
Competitors on 4 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
| LLM-specific UI/evals | Best fit | | --- | --- | --- | --- | --- | | OpenLLMetry / Traceloop | Yes | Yes — directly or via Collector | Good | Maximum backend flexibility | | Langfuse | Yes | Yes, via OTLP/Collector | Excellent...
Which LLM observability tools work with OpenTelemetry-compatible backends so we can consolidate LLM traces alongside existing service traces?
...OpenTelemetry-compatible tracing backends for LLM apps that work out of the box are Arize Phoenix, Langfuse, OpenLLMetry (Traceloop), Helicone, Datadog LLM Observability, and Future AGI traceAI. These tools natively support OpenTelemetry (OTLP) spans...
What are the best OpenTelemetry-compatible tracing backends for LLM apps that work out of the box without custom span parsing?
Best LLM Tracing Tools 2026: 6 Compared | | OpenLLMetry (Traceloop) | Drop-in OTel instrumentation | OSS library | Vendor-agnostic, one-line init, broad framework coverage | Teams wanting minimal friction to pipe LLM spans into existing collectors f...
Which LLM observability tools work with OpenTelemetry-compatible backends so we can consolidate LLM traces alongside existing service traces?
Most cited sources6
10Visualizing LLM Performance with OpenTelemetry Tools for Tracing Cost and Latency | Traceloop - LLM Application Observability
traceloop.com·Blog Post
- G8
GitHub - traceloop/openllmetry: Open-source observability for your GenAI or LLM application, based on OpenTelemetry
github.com·Documentation
- 6
The Specialized LLM Observability Platform Built on ...
traceloop.com·Blog Post
- 3
How to Trace LLM Agents and Find Failures
traceloop.com·Blog Post
- D1
LLM observability with OpenTelemetry Collector - traceloop
docs.traceloop.com·Documentation
- G1
Bug Report: Instrumenting of langchain failed - GitHub
github.com·Documentation
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 | Google AI Mode | Perplexity | ChatGPT | Bing Copilot |
|---|---|---|---|---|---|
Capability0/5 cited (0%) | |||||
Which LLM observability tools handle PII redaction and data masking in traces for teams with HIPAA or GDPR compliance requirements? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which LLM evaluation platforms support custom rubric-based scoring for domain-specific correctness beyond generic faithfulness metrics? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Looking for an eval platform that supports automated safety and toxicity scoring on LLM outputs at scale — what are my options? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
What platforms support end-to-end tracing of multi-agent pipelines including tool calls, retrieval steps, and sub-agent spawning? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Which LLM gateways handle multi-provider fallback and automatic retries while preserving full trace context across the switch? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Developer Experience2/5 cited (40%) | |||||
What LLM observability tools do ML engineering teams typically use to annotate and review production traces for quality feedback? | A competitor was cited | Your brand was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
I'm looking for an LLM observability platform with a great team collaboration workflow — where engineers and PMs can both review trace quality without SQL knowledge. | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Which LLM tracing platforms make it easiest to replay a failed multi-step agent run and pinpoint exactly where reasoning went wrong? | A competitor was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which LLM eval platforms have the best prompt playground experience for iterating on system prompts against a saved test dataset? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which LLM gateway tools give developers the best real-time cost and token usage visibility across multiple LLM providers during development? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Integrations & Ecosystem1/5 cited (20%) | |||||
Which LLM observability tools work with OpenTelemetry-compatible backends so we can consolidate LLM traces alongside existing service traces? | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited |
Which LLM observability platforms integrate natively with the most popular agent frameworks so traces appear automatically with no manual instrumentation? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
I'm evaluating LLM eval platforms — which ones integrate with version control to tie prompt regressions back to specific code or config changes? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Which LLM tracing platforms export trace data to a data warehouse so analysts can run custom eval queries alongside product metrics? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
What LLM gateway tools integrate best with secret managers and internal auth systems for enterprise teams rolling out to multiple product teams? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Performance & Reliability0/5 cited (0%) | |||||
Which LLM observability platforms stay reliable under traffic spikes from batch eval jobs running thousands of LLM calls simultaneously? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
What are the most production-hardened LLM gateway options for an enterprise team needing 99.9% uptime with circuit-breaker support? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What LLM tracing platforms handle high-throughput production workloads — millions of traces per day — without degrading query performance? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which LLM eval platforms support async evaluation at scale without blocking the inference path or adding latency for end users? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Which LLM gateways add the least latency overhead when routing between LLM providers — safe to use in production for sub-500ms SLAs? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Setup & First Run0/5 cited (0%) | |||||
Which LLM observability platforms can a small team get running against a production RAG pipeline in under a day? | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
What are the best OpenTelemetry-compatible tracing backends for LLM apps that work out of the box without custom span parsing? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
What's the fastest LLM tracing platform for instrumenting a Python-based agent framework without rewriting existing code? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
I'm evaluating LLM gateway solutions for a startup — which ones have the simplest self-hosted setup with a working UI on day one? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which evaluation platforms for LLM outputs are easiest to plug into an existing CI pipeline for a five-engineer team? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Braintrust | 32.0% | 21.8% | 7.2% | 0.0% | 44.0% | #3.3 | +0.51 |
| 2 | LangChain | 26.4% | 18.8% | 8.8% | 0.0% | 48.0% | #3.3 | +0.42 |
| 3 | Langfuse | 21.6% | 14.3% | 8.0% | 4.0% | 55.2% | #3.1 | +0.59 |
| 4 | Confident AI | 18.4% | 14.7% | 0.0% | 0.0% | 15.2% | #4.9 | +0.43 |
| 5 | Arize AI | 16.0% | 10.9% | 0.0% | 4.8% | 15.2% | #4.4 | +0.51 |
| 6 | Galileo | 12.0% | 6.8% | 0.0% | 12.0% | 10.4% | #2.8 | +0.40 |
| 7 | LiteLLM | 5.6% | 4.1% | 3.2% | 0.0% | 0.0% | #3.4 | +0.52 |
| 8 | Traceloop | 4.0% | 3.8% | 0.0% | 2.4% | 6.4% | #3.6 | +0.25 |
| 9 | Portkey | 4.0% | 3.4% | 0.8% | 0.0% | 21.6% | #5.3 | +0.45 |
| 10 | Helicone | 2.4% | 1.5% | 1.6% | 0.8% | 25.6% | #5.8 | +0.83 |
| 11 | Patronus AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.8% | — | — |
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