Evidently AI vs Superwise
Both compete in Observability & Monitoring. Evidently AI positions itself as “Open-source and cloud observability for evaluating, testing, and monitoring ML and LLM systems”, while Superwiseleads with “Agentic Management Platform for building, monitoring, and governing AI at scale”. The table below compares what each publishes.
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
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 | Open-source and cloud observability for evaluating, testing, and monitoring ML and LLM systems | Agentic Management Platform for building, monitoring, and governing AI at scale |
|---|---|---|
| Category | Observability & Monitoring | Observability & Monitoring |
| Frameworks | None published | None published |
| Deployment | Open-source, SaaS, Cloud, On-prem, API | SaaS, Cloud, On-prem, API |
| Built for | Data Science / ML | Data Science / ML, Risk, Compliance, GRC, Security |
| Founded | 2020 | 2019 |
| Headquarters | San Francisco, California, USA | Tel Aviv, Israel |
| Ownership | Private, independent; venture-backed (Y Combinator alum) | Acquired by Blattner Technologies in January 2023 (terms undisclosed); operates as a Blattner Tech company as of mid-2026 |
| Funding | $15M Series A (Dec 2024, led by DN Capital, with Clear Ventures, Fellows Fund, Framework Ventures, Stephens); Y Combinator-backed | ~$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 core (Apache 2.0); commercial Cloud and Enterprise tiers with undisclosed/contact-sales pricing | Commercial enterprise SaaS; custom pricing |
| Key capabilities |
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| Integrations | Python, GitHub, Databricks, MLflow, Airflow, Grafana | OpenAI, Anthropic, Amazon Bedrock, Azure OpenAI, Google Vertex AI, Cohere, Mistral AI, LangChain, LlamaIndex, CrewAI, AutoGen |
| Notable customers | DeepL, Wise, Flo Health, PlushCare, Realtor.com, Plaid, Databricks | None published |
| Best for | Data science and MLOps teams wanting developer-first, code-native ML/LLM evaluation and monitoring with an open-source foundation | 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 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 | 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 Evidently AI if data science and MLOps teams wanting developer-first, code-native ML/LLM evaluation and monitoring with an open-source foundation
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
A vendor-comparison worksheet plus EU AI Act, NIST AI RMF and ISO/IEC 42001 requirement checklists — so you can shortlist tools against the obligations that actually apply to you.
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