Arize AI vs Evidently AI

Both compete in Observability & Monitoring. Arize AI positions itself as “AI observability and evaluation platform for ML models, LLM apps, and agents”, while Evidently AIleads with “Open-source and cloud observability for evaluating, testing, and monitoring ML and LLM systems”. The table below compares what each publishes.

Where Arize AI pulls ahead

Publishes support for SOC 2, HIPAA, GDPR, which Evidently AI does not. AI engineering and ML teams wanting unified LLM/agent observability and evaluation with an open-source (Phoenix) on-ramp to enterprise-scale monitoring

Where Evidently AI pulls ahead

Data science and MLOps teams wanting developer-first, code-native ML/LLM evaluation and monitoring with an open-source foundation

	Arize AI	Evidently AI
Positioning	AI observability and evaluation platform for ML models, LLM apps, and agents	Open-source and cloud observability for evaluating, testing, and monitoring ML and LLM systems
Category	Observability & Monitoring	Observability & Monitoring
Frameworks	SOC 2, HIPAA, GDPR	None published
Deployment	SaaS, Cloud, On-prem, Open-source, API	Open-source, SaaS, Cloud, On-prem, API
Built for	Data Science / ML, Risk, Compliance	Data Science / ML
Founded	2020	2020
Headquarters	Berkeley, California, USA	San Francisco, California, USA
Ownership	Private, independent, venture-backed (as of mid-2026)	Private, independent; venture-backed (Y Combinator alum)
Funding	~$135M total raised across 5 rounds, including a $70M Series C in February 2025 led by Adams Street Partners; earlier $38M Series B (2022) led by TCV	$15M Series A (Dec 2024, led by DN Capital, with Clear Ventures, Fellows Fund, Framework Ventures, Stephens); Y Combinator-backed
Pricing	Free open-source (Phoenix); commercial tiers with free/self-serve entry and enterprise plans (usage/seat-based, custom pricing)	Free open-source core (Apache 2.0); commercial Cloud and Enterprise tiers with undisclosed/contact-sales pricing
Key capabilities	End-to-end agent and LLM tracing Evaluation framework (span/trace/session evals, LLM-as-judge) Drift and performance monitoring Embedding and data quality analysis Bias/fairness monitoring Prompt testing and iteration	100+ evaluation metrics for ML and LLM systems Data and prediction drift detection Reports and test suites (presets and custom) Self-hostable monitoring dashboards LLM evals: hallucination, toxicity, PII, context relevance Synthetic and adversarial test-data generation
Integrations	OpenAI, Anthropic, Google, Amazon Bedrock, LangChain, LangGraph, LlamaIndex, CrewAI, DSPy, OpenTelemetry / OpenInference	Python, GitHub, Databricks, MLflow, Airflow, Grafana
Notable customers	Reddit, DoorDash, Instacart, Uber, Spotify, PagerDuty, Booking.com	DeepL, Wise, Flo Health, PlushCare, Realtor.com, Plaid, Databricks
Best for	AI engineering and ML teams wanting unified LLM/agent observability and evaluation with an open-source (Phoenix) on-ramp to enterprise-scale monitoring	Data science and MLOps teams wanting developer-first, code-native ML/LLM evaluation and monitoring with an open-source foundation
Limitations	Positioned as an observability and evaluation layer rather than a full GRC/policy-enforcement governance suite; enterprise features and depth may require the paid platform beyond open-source Phoenix, and the fast-evolving agent tooling can shift.	Positioned as an evaluation/observability toolkit rather than a full regulatory-compliance or GRC platform; no explicit mapping to named governance frameworks; commercial pricing is not public; governance features are monitoring-oriented rather than policy/attestation-oriented

Which should you shortlist?

Choose Arize AI if aI engineering and ML teams wanting unified LLM/agent observability and evaluation with an open-source (Phoenix) on-ramp to enterprise-scale monitoring

Choose Evidently AI if data science and MLOps teams wanting developer-first, code-native ML/LLM evaluation and monitoring with an open-source foundation

Neither is a substitute for a governance program. Whichever you pick, you still need people who can define the policies the tool enforces.

Full Arize AI profile →Full Evidently AI profile →

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