Arize AI vs Traceloop: Observability, Prompts & Evals Comparison
Compare Arize AI and Traceloop side by side. Both are tools in the Observability, Prompts & Evals category.
The short answer
Choose Arize AI if built on OpenTelemetry standards ensuring interoperability and avoiding vendor lock-in.
Choose Traceloop if acquired by ServiceNow for $60-80M providing strong financial backing and integration opportunities.
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Quick Comparison
| Category | Observability, Prompts & Evals | Observability, Prompts & Evals |
| Pricing | Freemium | open-source |
| Best For | ML teams who need comprehensive observability spanning traditional ML models and LLM applications | Teams already using Datadog/Splunk wanting LLM observability |
| Website | arize.com | traceloop.com |
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Pros & Cons: Arize AI vs Traceloop
- +Built on OpenTelemetry standards ensuring interoperability and avoiding vendor lock-in
- +Impressive scale with 1 trillion spans processed and 5 million monthly OSS downloads
- +Open-source Phoenix OSS option available for self-hosting
- +Enterprise-proven with major clients like DoorDash, Instacart, Reddit, and Uber
- +Comprehensive platform covering development, debugging, evaluation, and production monitoring
- −Enterprise pricing starting at ~$1,000/month may be prohibitive for smaller teams
- −Complexity of full feature set may require longer onboarding and learning curve
- −Less transparent pricing compared to competitors with clear tier structures
- +Acquired by ServiceNow for $60-80M providing strong financial backing and integration opportunities
- +Built on OpenTelemetry standards with open-source OpenLLMetry SDK
- +Recognized as Gartner Cool Vendor demonstrating industry validation
- +One-line code integration for quick setup and deployment
- +Enterprise-ready with SOC 2, HIPAA compliance and multiple deployment options
- −ServiceNow acquisition may lead to platform changes, pricing adjustments, or strategic shifts
- −Small team of 9 employees may be absorbed into larger ServiceNow organization affecting support
- −Limited transparent pricing information following acquisition
- −Uncertainty around long-term product roadmap under new ownership
When to Choose Arize AI vs Traceloop
- Production ML and LLM monitoring
- Embedding quality monitoring
- Model performance tracking
About Arize AI
Arize AI is a unified LLM observability and agent evaluation platform designed for AI application development and production management. The platform enables teams to build, observe, and improve AI systems through integrated development and production capabilities. Built on OpenTelemetry standards and open-source principles, Arize features 'adb,' a proprietary datastore optimized for generative AI workloads with real-time ingestion and sub-second query capabilities. The platform includes an agent framework for building and debugging AI agents, comprehensive tracing for full visibility into LLM application flows, automated evaluators with custom evaluation models, and Alyx, an AI engineering agent that assists with debugging and development. Arize offers experiment testing and optimization capabilities, production monitoring and alerting, a prompt playground for optimization, and data annotation tools. With impressive scale processing 1 trillion spans, 50 million evaluations per month, and 5 million monthly downloads of Phoenix OSS, Arize serves notable clients including DoorDash, Instacart, Reddit, Roblox, Uber, and Booking.com.
About Traceloop
Traceloop is an observability and quality assurance platform designed to help teams ship LLM applications 10x faster by transforming evaluation data into continuous feedback loops. The platform enables developers to monitor, test, and improve large language model applications throughout their lifecycle. Built on OpenTelemetry and shipping with OpenLLMetry (their open-source SDK), Traceloop provides real-time monitoring with just one line of code, giving live visibility into prompts, responses, latency, and more. The platform offers built-in quality evaluations for faithfulness, relevance, and safety that automatically apply to production data, along with custom evaluators that users can define and train on annotated examples. Traceloop features automated quality gates that run evaluations automatically on pull requests and in real-time during app execution, plus LLM drift detection to catch performance degradation before it reaches users. The platform supports 20+ LLM providers including OpenAI, Anthropic, Gemini, Bedrock, and Ollama, and integrates with popular frameworks like LangChain, LlamaIndex, and CrewAI. In March 2026, Traceloop was acquired by ServiceNow for $60-80 million, marking the third Israeli acquisition by ServiceNow in under three months. The platform is SOC 2 and HIPAA compliant with cloud, on-premises, and air-gapped deployment options. Traceloop has been recognized as a Gartner Cool Vendor and serves notable clients including HiBob, Target, Miro, IBM, and Babbel.
What is Observability, Prompts & Evals?
Tools for monitoring LLM applications in production, managing and versioning prompts, and evaluating model outputs. Includes tracing, logging, cost tracking, prompt engineering platforms, automated evaluation frameworks, and human annotation workflows.
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