Arize AI vs Superwise
Both compete in Observability & Monitoring. Arize AI positions itself as “AI observability and evaluation platform for ML models, LLM apps, and agents”, while Superwiseleads with “Agentic Management Platform for building, monitoring, and governing AI at scale”. The table below compares what each publishes.
Where Arize AI pulls ahead
Publishes support for SOC 2, HIPAA, GDPR, which Superwise 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 Superwise pulls ahead
Regulated enterprises and AI platform teams needing a unified governance control plane spanning observability, guardrails, and policy enforcement for models and agents
| Positioning | AI observability and evaluation platform for ML models, LLM apps, and agents | Agentic Management Platform for building, monitoring, and governing AI at scale |
|---|---|---|
| Category | Observability & Monitoring | Observability & Monitoring |
| Frameworks | SOC 2, HIPAA, GDPR | None published |
| Deployment | SaaS, Cloud, On-prem, Open-source, API | SaaS, Cloud, On-prem, API |
| Built for | Data Science / ML, Risk, Compliance | Data Science / ML, Risk, Compliance, GRC, Security |
| Founded | 2020 | 2019 |
| Headquarters | Berkeley, California, USA | Tel Aviv, Israel |
| Ownership | Private, independent, venture-backed (as of mid-2026) | Acquired by Blattner Technologies in January 2023 (terms undisclosed); operates as a Blattner Tech company as of mid-2026 |
| 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 | ~$4.6M in seed funding (including a $4.5M round in March 2020 led by Capri Ventures and F2 Capital) prior to the 2023 acquisition |
| Pricing | Free open-source (Phoenix); commercial tiers with free/self-serve entry and enterprise plans (usage/seat-based, custom pricing) | Commercial enterprise SaaS; custom pricing |
| Key capabilities |
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| Integrations | OpenAI, Anthropic, Google, Amazon Bedrock, LangChain, LangGraph, LlamaIndex, CrewAI, DSPy, OpenTelemetry / OpenInference | OpenAI, Anthropic, Amazon Bedrock, Azure OpenAI, Google Vertex AI, Cohere, Mistral AI, LangChain, LlamaIndex, CrewAI, AutoGen |
| Notable customers | Reddit, DoorDash, Instacart, Uber, Spotify, PagerDuty, Booking.com | None published |
| 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 | Regulated enterprises and AI platform teams needing a unified governance control plane spanning observability, guardrails, and policy enforcement for models and agents |
| 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. | A smaller vendor (roughly 19 employees) now owned by Blattner Technologies, so scale and long-term independence differ from larger competitors; explicit certification against named frameworks (EU AI Act, NIST AI RMF, ISO 42001) is not clearly published despite strong governance positioning, and named public customer references are limited. |
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 Superwise if regulated enterprises and AI platform teams needing a unified governance control plane spanning observability, guardrails, and policy enforcement for models and agents
Neither is a substitute for a governance program. Whichever you pick, you still need people who can define the policies the tool enforces.
AI Governance Tool Selection Kit
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