About
Traceloop is an LLM reliability and observability platform designed for teams building AI-powered applications. It connects evaluations, monitoring, and CI/CD pipelines into a single continuous feedback loop so every release ships with measurably better quality. Setup takes just one line of code, giving teams instant visibility into prompts, responses, latency, and token usage with no manual configuration required. The platform includes built-in quality checks for faithfulness, relevance, and safety that run automatically against live traffic or pull requests. For teams with unique requirements, Traceloop lets you define custom evaluators by annotating real examples and training a domain-specific scorer that reflects your own quality standards. These evaluations can run on every PR, in real-time production traffic, or both. Traceloop is built on OpenTelemetry and ships with OpenLLMetry, its open-source instrumentation SDK, ensuring transparency and avoiding vendor lock-in. It supports 20+ providers including OpenAI, Anthropic, Google Gemini, AWS Bedrock, and Ollama, as well as vector databases like Pinecone and Chroma, and frameworks such as LangChain, LlamaIndex, and CrewAI. SDKs are available in Python, TypeScript, Go, and Ruby. Designed for both startups and enterprises, Traceloop can be deployed in the cloud, on-premises, or in air-gapped environments. The platform is SOC 2 and HIPAA compliant, making it suitable for regulated industries. It is now part of ServiceNow.
Key Features
- One-Line Observability Setup: Instrument your LLM app with a single line of code to gain instant visibility into prompts, responses, latency, token usage, and more — no complex configuration needed.
- Built-In Quality Evaluations: Automatically run trusted metrics like faithfulness, relevance, and safety against your real data without writing a single test, giving you an instant quality baseline.
- Custom Evaluator Training: Define quality on your own terms by annotating real examples and training a custom evaluator that scores model outputs the same way your team would.
- CI/CD Pipeline Integration: Integrate evaluations directly into your pull request workflow or run them in real-time production traffic to enforce quality thresholds and catch regressions early.
- OpenTelemetry-Native & Multi-Provider: Built on OpenTelemetry with the open-source OpenLLMetry SDK, supporting 20+ LLM providers, vector databases, and orchestration frameworks like LangChain and LlamaIndex.
Use Cases
- Monitoring production LLM applications for quality drift, latency regressions, and unexpected model behavior before users are affected.
- Running automated evaluation suites on every pull request to enforce quality thresholds and prevent regressions from being deployed.
- Building custom quality evaluators by annotating real production examples to score outputs according to domain-specific standards.
- Gaining full observability into prompt/response pairs, token usage, and latency across multiple LLM providers from a single dashboard.
- Deploying a compliant LLM observability stack in regulated industries (healthcare, finance) using on-premises or air-gapped infrastructure.
Pros
- Minimal Setup Friction: One line of code delivers full observability, making it extremely fast to onboard and start gaining insights without complex infrastructure changes.
- Open Source & No Vendor Lock-In: The OpenLLMetry SDK is fully open source and built on OpenTelemetry standards, giving teams full transparency and portability across providers.
- Broad Ecosystem Compatibility: Supports 20+ LLM providers, major vector databases, and popular frameworks, fitting naturally into virtually any modern AI stack.
- Enterprise-Grade Security: SOC 2 and HIPAA compliance, plus cloud, on-prem, and air-gapped deployment options make it viable for even the most security-sensitive organizations.
Cons
- Primarily Developer-Focused: The platform is optimized for engineering teams; non-technical stakeholders may find the observability and eval workflows difficult to interpret without developer support.
- Custom Evaluators Require Data Annotation: Building a custom evaluator requires annotating real examples first, which adds time and effort before teams can benefit from domain-specific quality scoring.
- Advanced Features May Require Paid Tier: While a free tier is available, production-scale monitoring, custom evaluators, and enterprise deployment options likely require a paid subscription.
Frequently Asked Questions
How quickly can I integrate Traceloop into my existing LLM application?
Traceloop is designed for rapid onboarding — a single line of code using the OpenLLMetry SDK or the native OpenTelemetry gateway (Hub) is all you need to start collecting live visibility into prompts, responses, latency, and more.
Which LLM providers and frameworks does Traceloop support?
Traceloop supports 20+ LLM providers including OpenAI, Anthropic, Google Gemini, AWS Bedrock, and Ollama. It also works with vector databases like Pinecone and Chroma, and frameworks such as LangChain, LlamaIndex, and CrewAI.
Can I deploy Traceloop in a private or air-gapped environment?
Yes. Traceloop offers cloud, on-premises, and air-gapped deployment options, making it suitable for enterprises with strict data residency or security requirements.
What is OpenLLMetry and is it really open source?
OpenLLMetry is Traceloop's open-source instrumentation SDK built on the OpenTelemetry standard. It is available on GitHub, allowing the community to inspect, contribute to, and extend it without vendor lock-in.
How does Traceloop detect LLM quality degradation over time?
Traceloop continuously monitors model outputs using built-in metrics (faithfulness, relevance, safety) as well as custom evaluators you define. It can alert teams to quality drift in real-time or flag regressions during CI/CD pipeline runs before code reaches production.
