What are the main alternatives to building an in-house LLM evaluation and tracing stack versus buying a managed platform?

“LangSmith – tracing and evaluation workflows, with commercial and self-hosting options depending on the offering.”

“LangSmith – LLM tracing, evaluation, and development workflows.”

“Braintrust – evaluation, experimentation, and production monitoring.”

“Langfuse – tracing, prompt management, evaluations, and datasets.”

“Advantages: Faster time to value, community-developed functionality, and more control than a fully managed service.”

“Disadvantages: You still own deployment, upgrades, scaling, integrations, and potentially significant customization.”

“Arize Phoenix – tracing, evaluation, and experimentation.”

“Datadog – application performance monitoring, LLM observability, infrastructure monitoring.”

“Arize AX – enterprise AI observability and evaluation.”

“Weights & Biases Weave – LLM tracing and evaluation integrated with an ML experimentation ecosystem.”

“OpenLLMetry – open-source instrumentation based on OpenTelemetry.”

“LangWatch – LLM observability, evaluation, and monitoring.”

“MLflow – experiment tracking, model lifecycle management, and LLM tracing.”

“Dynatrace – enterprise application observability and AI monitoring.”

“OpenTelemetry compatibility is particularly valuable because it helps decouple instrumentation from the backend you eventually choose.”