Deepchecks vs Superwise
Both compete in Observability & Monitoring. Deepchecks positions itself as “Open-source-led testing, evaluation and monitoring for ML models and LLM applications”, while Superwiseleads with “Agentic Management Platform for building, monitoring, and governing AI at scale”. The table below compares what each publishes.
Where Deepchecks pulls ahead
Publishes support for SOC 2, GDPR, HIPAA, which Superwise does not. Data science and ML engineering teams wanting code-first, open-source-backed validation of models and LLM apps, with an enterprise upgrade path for production 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 | Open-source-led testing, evaluation and monitoring for ML models and LLM applications | Agentic Management Platform for building, monitoring, and governing AI at scale |
|---|---|---|
| Category | Observability & Monitoring | Observability & Monitoring |
| Frameworks | SOC 2, GDPR, HIPAA | 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 | 2021 | 2019 |
| Headquarters | Tel Aviv, Israel | Tel Aviv, Israel |
| Ownership | Independent | Acquired by Blattner Technologies in January 2023 (terms undisclosed); operates as a Blattner Tech company as of mid-2026 |
| Funding | $14M seed led by Alpha Wave Ventures | ~$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; commercial enterprise LLM Evaluation platform (pricing not public) | Commercial enterprise SaaS; custom pricing |
| Key capabilities |
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| Integrations | OpenAI, Anthropic Claude, Amazon Bedrock, LangChain, CrewAI, NVIDIA, AWS SageMaker, Datadog | OpenAI, Anthropic, Amazon Bedrock, Azure OpenAI, Google Vertex AI, Cohere, Mistral AI, LangChain, LlamaIndex, CrewAI, AutoGen |
| Notable customers | None published | None published |
| Best for | Data science and ML engineering teams wanting code-first, open-source-backed validation of models and LLM apps, with an enterprise upgrade path for production monitoring. | Regulated enterprises and AI platform teams needing a unified governance control plane spanning observability, guardrails, and policy enforcement for models and agents |
| Limitations | Oriented toward technical ML/engineering users rather than non-technical GRC or legal teams, and its regulatory-framework mapping is lighter than dedicated AI-governance and compliance platforms. | 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 Deepchecks if data science and ML engineering teams wanting code-first, open-source-backed validation of models and LLM apps, with an enterprise upgrade path for production 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
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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