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Evidently AI vs HoneyHive

Evidently AI logo

Evidently AI

8.3#2 in machine learning model monitoring softwareFrom $80 a month
Free planWebAPILinuxmacOS
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HoneyHive logo

HoneyHive

6.4#17 in ai llm evaluation toolsFree plan
Free planWebAPISelf-hosted
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What both publish

FactEvidently AIHoneyHive
IntegrationsDocumented integration examples cover Jupyter and Colab, Streamlit, MLflow, DVCLive, Airflow, Metaflow, FastAPI, PostgreSQL, Prefect, Grafana, Prometheus and AWS SES.HoneyHive documents integrations for agent frameworks and platforms including LangChain, LangGraph, CrewAI, OpenAI Agents SDK, and Microsoft Copilot Studio.
Self-hostingA local or remote Evidently service can be run with filesystem, SQLite, Postgres or other SQL-like storage, plus S3-compatible backends.Self-hosted deployments place both the control plane and data plane in the customer's environment and are managed by HoneyHive.
SupportCommunity support is provided through Discord, GitHub issue reporting, documentation, courses and direct email contact.The Developer plan includes community support, while Enterprise lists dedicated technical account management and quarterly business reviews.
TracingOpen-source tracing records inputs, outputs, intermediate events and function calls, and allows traces to be exported as tabular datasets.The platform lets teams inspect traces step by step, including tool calls, prompts, and agent decisions.

Evidently AI plans

  • DeveloperFree
  • Open-sourceFree
  • Pro$80 a month
  • EnterprisePriced on request
  • StartupsPriced on request

HoneyHive plans

  • DeveloperFree
  • EnterprisePriced on request

Pick Evidently AI if…

It suits teams evaluating or monitoring AI applications and predictive ML models, including those that want open-source tooling or a hosted platform. Its synthetic-data and red-team capabilities may also suit teams assessing adversarial inputs and risks.

Pick HoneyHive if…

HoneyHive is aimed at AI platform teams, AI engineers, domain experts, and risk and governance teams evaluating agents in production. The Developer plan may suit smaller teams that fit its event, user, workspace, and retention limits.

Charts they share