DeepChecks
Automates and monitors LLMs for quality, compliance, and performance.. [Freemium]
Last verified:
What is DeepChecks?
Deepchecks is an enterprise-grade AI testing, observability, and monitoring platform designed for LLM Evaluation. It unifies evaluation, observability, testing, and monitoring to provide visibility, control, and trust across AI systems in production. The platform addresses the unique quality problems introduced by generative AI that cannot be solved with simple rules or unit tests, offering auto-scoring pipelines, dataset generation, and LLM judge creation within minutes.
Deepchecks ML Testing is also available as an open-source Python package for comprehensively validating machine learning models and data with minimal effort. It covers data integrity checks, model evaluation, train-test validation, and supports tabular data, natural language processing, and computer vision. The open-source version includes pre-built suites for data integrity, train-test validation, and model evaluation, with checks for unstructured data and model explainability.
The platform is designed for AI teams, data scientists, ML engineers, and enterprises deploying production AI systems. It supports CI/CD integration for testing LLM apps, monitors them in production, and enables comparison of different prompt, model, agent, and AI system versions. Deepchecks is used by leading AI teams at companies like Moovit, Lovehoney Group, and global pharmaceutical companies.
Key features include version comparison of prompts/models/agents, auto-scoring pipelines for nuanced constraints, dataset generation, LLM judge creation, AI-assisted annotations, data slicing and dicing, CI/CD integration for LLMs, and production monitoring. The platform offers enterprise-grade security and compliance built from day one, with secure access controls, data isolation, and auditability for regulated organizations.
DeepChecks pricing
Pricing model: Freemium
Deepchecks offers flexible pricing designed to scale with AI applications. The Basic plan is ideal for small teams and startups, including up to 3 seats, 1 AI application, up to 5K DPUs/month, 3 months data retention, unlimited prompt-based metrics, multi-lingual AI applications, and Know Your Agent (KYA). The Scale plan is for teams with several production-grade AI applications, including all Basic features plus 5 seats, 3 AI applications, 20K DPUs/month, premium support, premium compliance, and guided platform onboarding. The Enterprise plan is for companies with high data volumes and advanced security needs, offering custom seats and AI applications, custom DPUs/month, enterprise-grade security, enterprise support package, and dedicated customer success team. Additional options include Deepchecks on SageMaker AI (AWS-managed version with on-prem data locality and Amazon Bedrock integration) and Deepchecks Dedicated (single tenant option with cloud on-prem and bare-metal options, custom SLA, and custom features).
DeepChecks pros
- Enterprise-grade AI testing and monitoring platform
- Unifies evaluation, observability, testing, and monitoring in one platform
- Provides visibility and control for trusting AI systems in production
- Auto-scoring pipeline addresses nuanced constraints
- Generate datasets and create LLM judges within minutes
- Test LLM apps within CI/CD and monitor in production
- 0x improvement in time to production for new LLM apps
- Significant decrease in hallucinations and low-quality responses
- Enterprise-grade security and compliance built from day one
- Multiple deployment options for data privacy constraints
- Open-source Python package for ML model validation
- Comprehensive data integrity checks for inconsistencies
- Train-test validation to detect drift and leakage
- Model evaluation with performance metrics and benchmarks
- Supports tabular data, NLP, and computer vision models
- Code-level root cause analysis up to 70% faster
- Pre-built suites easily extensible with custom checks
- Seamless AWS SageMaker AI integration
- Multi-lingual AI application support
- Know Your Agent (KYA) capability included
DeepChecks cons
- Pricing details not publicly disclosed - requires contact for quote
- Enterprise features require Enterprise plan enrollment
- Open-source version lacks production monitoring dashboard
- Computer vision support limited to PyTorch framework
- Basic plan limited to 3 seats and 1 AI application
- Data retention limited to 3 months on Basic plan
- Scale plan only includes 5 seats maximum
- Custom features require Enterprise Dedicated plan
Frequently asked questions about DeepChecks
What is the difference between evaluating an LLM and evaluating an LLM application?
LLM evaluation primarily focuses on the model's internal capabilities including perplexity, reasoning, and factual accuracy. LLM application evaluation measures system-level performance - how well the system works in practice. Application evaluation factors include task success rate, latency, user satisfaction, and cost.
What are the key challenges in evaluating LLMs?
Traditional metrics often fail to align with human judgment. Detecting hallucinations and measuring bias remain difficult challenges. Additionally, current models still struggle with tasks requiring long-context understanding, making evaluation complex.
When should evaluation begin in the LLM development lifecycle?
Evaluation should begin early, during pre-training or prototyping stages. Early evaluation helps guide model design decisions and identify potential biases before they become major issues, saving time and resources in the long run.
What happens during pre-deployment evaluation?
Pre-deployment evaluation includes stress tests, human feedback studies, system benchmarks, and ethical assessments to ensure the model is production-ready. This comprehensive approach verifies the LLM can fulfill its designated tasks effectively and safely across varied environments.
Why is iterative evaluation important for LLMs?
Iterative evaluation allows ongoing model refinement throughout the development cycle. It adapts to a variety of use cases while ensuring that efficiency and safety standards are met. Regular assessments track progress and identify new challenges or improvement opportunities.
What deployment options does Deepchecks offer?
Deepchecks offers multiple deployment options to accommodate any data privacy constraints, including cloud deployment, AWS SageMaker AI managed version with on-prem data locality, and Deepchecks Dedicated with single tenant options, cloud on-prem options, and bare-metal options for maximum control and privacy.
How does Deepchecks integrate with CI/CD pipelines?
Deepchecks provides CI/CD integration via GitHub for automating model validation workflows. It enables continuous checks for data drift, performance degradation, and bias, allowing teams to test LLM apps within their CI/CD pipeline and monitor them in production.
What types of data does Deepchecks support?
Deepchecks supports tabular data, natural language processing (NLP), and computer vision (CV) data. For tabular data it supports scikit-learn, XGBoost, PyTorch, and more. For computer vision, it currently supports the PyTorch framework with built-in support for TensorFlow and custom frameworks.
What security and compliance features does Deepchecks include?
Enterprise-grade security and compliance are built into the platform from day one. Deepchecks supports secure access controls, data isolation, and auditability to meet requirements of regulated and security-conscious organizations. Premium and Enterprise plans include premium compliance and enterprise-grade security features.
What is Know Your Agent (KYA)?
Know Your Agent (KYA) is a capability included in Deepchecks that provides visibility and understanding of AI agents. It is included in the Basic plan and helps teams evaluate and understand their AI systems better, contributing to the platform's goal of providing control and trust across AI systems in production.