Datadog LLM vs Traceloop: Observability, Prompts & Evals Comparison
Compare Datadog LLM and Traceloop side by side. Both are tools in the Observability, Prompts & Evals category.
The short answer
Choose Datadog LLM if seamless integration with Datadog's full observability suite for unified application monitoring.
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 | Enterprise | open-source |
| Best For | Enterprise teams already using Datadog who want to add LLM monitoring | Teams already using Datadog/Splunk wanting LLM observability |
| Website | datadoghq.com | traceloop.com |
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Pros & Cons: Datadog LLM vs Traceloop
- +Seamless integration with Datadog's full observability suite for unified application monitoring
- +Built-in sensitive data scanning and prompt injection detection for enhanced security
- +Established enterprise platform with proven reliability and 9,700+ employees globally
- +Automatic cost estimation for LLM requests using public pricing models
- +Quick setup in seconds using their SDK
- −Premium automatically activated at $120/day when LLM spans detected with no opt-out
- −Can be expensive at scale compared to specialized LLM observability tools
- −Requires familiarity with Datadog ecosystem for optimal usage
- −Per-span pricing model may become costly for high-volume applications
- +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 Datadog LLM vs Traceloop
- Unified monitoring for AI and traditional services
- Enterprise LLM monitoring at scale
- Correlating LLM performance with infrastructure
About Datadog LLM
Datadog LLM Observability is a comprehensive monitoring platform designed to help teams deliver LLM applications to production faster with end-to-end tracing across AI agents, structured experiments, and robust quality and security evaluations. The platform provides complete visibility into inputs, outputs, latency, token usage, and errors across AI agent workflows. It features structured experiment management for testing prompt changes, model swaps, and parameter tuning, along with quality evaluations including hallucination detection and output clustering for drift identification. Security features include sensitive data scanning and prompt injection detection. As part of the broader Datadog platform, LLM Observability integrates seamlessly with APM and Real User Monitoring for unified full-stack visibility, allowing teams to correlate LLM workloads with backend services, infrastructure, and user sessions.
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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