Deepchecks
Testing and monitoring platform for AI and LLM apps, built on an open-source Python testing library.
How the 6.0 was reached
- Quality of output5/10
4,061 GitHub stars and active repo, modest adoption; licence unclear. github.com
- Ease of use8/10
pip or conda install, 5-minute quickstart and documentation. deepchecks.readthedocs.io
- Pricing value6/10
Free trial Basic plan with limits; Scale plan by demo request, no published price. deepchecks.com
- API and integration quality5/10
Python package and SaaS/VPC deployment with SSO; no API reference or integrations list in evidence. deepchecks.com
- Solves the problem it claims to solve6/10
Open-source testing library has real GitHub adoption; the enterprise LLM observability claim rests on vendor descriptions. github.com
6.0/10 is the mean of the 5 criteria that apply. Scored on the five-criteria rubric v1, 29 September 2026. How scoring works
Pricing
- Up to 3 seats
- 1 AI application
- 5K DPUs a month
- 3 months data retention
- 5 seats
- 3 AI applications
- 20K DPUs a month
- Premium support and compliance
- Custom seats, applications and DPUs
- Dedicated customer success team
Key Features
- Compares versions of prompts, models and agents
- Auto-scoring pipeline and LLM judges
- Dataset generation
- SaaS and VPC deployment with SSO
- SOC2 Type 2, GDPR and HIPAA support
Pros & Cons
Pros
- pip or conda install with a quickstart and docs
- Open-source library has 4,061 GitHub stars
- Compliance list includes SOC2 Type 2, GDPR and HIPAA
- Free trial tier lets teams start without a sales call
Cons
- No published price beyond the free trial
- No API reference or integrations list in the evidence
- Licence of the open-source library is unclear
- Enterprise observability claims rest on vendor descriptions
Deepchecks suits teams that want to test and monitor LLM apps and can start with the free trial. Buyers who need public pricing or a documented API should check those points before committing.
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