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Deepchecks

  • What it is:Deepchecks is a platform for evaluating and monitoring machine learning models, with a focus on large language models (LLMs) to detect issues like hallucinations, bias, and performance drift.
  • Best for:Regulated enterprises deploying LLMs, Development teams building LLM applications, AWS-native organizations
  • Pricing:Free tier available, paid plans from $89/model/month
  • Rating:78/100Good
  • Expert's conclusion:Deepchecks is necessary for any serious machine learning production pipeline that requires robust, automated validation and monitoring capabilities.
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Reviewed byMaxim Manylov·Web3 Engineer & Serial Founder

What Is Deepchecks and What Does It Do?

Deepchecks is an Israel-based company created by machine learning engineers that were part of elite IDF units like Talpiot and 8200; they provide technology to validate and test Machine Learning (ML) models and data as it flows through its life cycle. They have two main products: an Open-Source Solution and a Commercial Platform (Deepchecks Hub) for Enterprise use cases. The Mission of Deepchecks is to help companies achieve total control over machine learning systems by testing all models and data.

Active
📍Tel Aviv, Israel
📅Founded 2020
🏢Private
TARGET SEGMENTS
Data ScientistsML EngineersEnterprise AI TeamsRegulated Industries

What Are Deepchecks's Key Business Metrics?

📊
500,000+
Open Source Downloads
🏢
~15 (2023)
Employees
👥
Used by AWS, Booking.com, Wix
Customers
📊
$14M Seed
Funding Raised
📊
2020
Founding Year

How Credible and Trustworthy Is Deepchecks?

78/100
Good

Developed strong open-source traction with talented, technically-qualified founders from elite IDF backgrounds and received significant seed funding but remains in its early stages and has very little publicly available review data.

BREAKDOWN
Product Maturity75/100
Company Stability80/100
Security & Compliance85/100
User Reviews70/100
Transparency85/100
Support Quality75/100
TRUST SIGNALS
Founders from IDF Talpiot/8200 elite units500,000+ open source downloadsUsed by AWS, Booking.com, Wix$14M seed funding from Alpha Wave, Hetz, Grove VenturesOpen source first approach

What is the history of Deepchecks and its key milestones?

2020

Company Founded

Created by Philip Tannor (CEO) and Shir Chorev (CTO), both are alumni of Israeli elite IDF units Talpiot and 8200; developed to address the validation issues associated with Machine Learning Models.

2021

First Open Source Release

After approximately one and a half years of development launched the first version of the continuous ML validation tool.

2022

Pivot to Open Source

Changed focus from developing solutions for enterprises to develop an open source first strategy to work with and grow the machine learning/ data science communities.

2022

Pre-Seed Funding

Raised $4.4M in pre-seed funding at the time of launching the company.

2023

$14M Seed Funding

Completed $14M seed round ($9.5M + prior $4.4M pre-seed) led by Alpha Wave Ventures with Hetz Ventures and Grove Ventures.

2023

Deepchecks Hub GA

Publicly announced the General Availability of the Commercial Deepchecks Hub platform which provides enterprise-grade security capabilities.

Who Are the Key Executives Behind Deepchecks?

Philip Tannor— CEO & Co-founder
Graduated from Israeli IDF elite unit Talpiot program; experienced in leading AI research and successfully deploying Machine Learning models into production.
Shir Chorev— CTO & Co-founder
Alumni of Israeli IDF elite units with extensive hands-on experience in researching and producing Machine Learning models.
Prof. Lior Rokach— Chief Scientist
Academic leader in Machine Learning with relationships to the Research Community.

What Are the Key Features of Deepchecks?

✨
Continuous Model Validation
Validates Machine Learning Models throughout entire life cycle from Training through Production to New Version Releases.
✨
Data Integrity Testing
Continuously tests data validity, integrity, bias detection, and corruption prevention.
✨
Open Source Core
The free open source version has been downloaded >500K times and supports training, production, and testing of model versions.
✨
Deepchecks Hub Enterprise
A commercially available, secure, authenticated and monitored platform for deploying enterprise-level AI/ML applications
✨
Production Monitoring
Models are continuously monitored in real time during their use to ensure they continue to perform correctly and reliably
✨
Multi-Phase Coverage
Testing is performed across all of the environments used in development, deployment and production

What Technology Stack and Infrastructure Does Deepchecks Use?

Infrastructure

Cloud-based SaaS (inferred)

Technologies

Python

Integrations

ML FrameworksMLOps PipelinesCloud Platforms

AI/ML Capabilities

Continuous validation platform testing model performance, data integrity, bias, and drift detection across ML lifecycle stages

Limited technical details available; Python ML ecosystem implied by ML validation focus

What Are the Best Use Cases for Deepchecks?

ML Engineers
Models are validated across all of the environments of a continuous development pipeline; from development, testing and staging through to production, this includes validating for integrity issues and performance degradation as well as data drift
Data Science Teams
Automated testing will reduce the amount of manual validation needed by developers across all three phases (training, validation and deployment) using an open source-based solution
MLOps Teams
The Enterprise Hub provides the functionality to monitor and alert for mission critical ML systems in regulated environments
Small Startups
The open source version of the Enterprise Hub allows for comprehensive ML validation without commercial licensing fees
NOT FORNon-ML Software Teams
N/A - This tool is specifically designed for machine learning model and data validation work flows
NOT FORReal-time Low-Latency ML
May introduce too much overhead for monitoring purposes that require sub second level of inference

How Much Does Deepchecks Cost and What Plans Are Available?

Pricing information with service tiers, costs, and details
☐Service$CostℹDetails🔗Source
Open Source$01 model per deployment, AGPL license, testing and monitoring for tabular and NLP data, community Slack supportDeepchecks monitoring pricing page
Startup Plan$89/model/month1-10 models, commercial license for company use, testing and monitoring, custom metrics and checks, social login, 12 months data retention, SaaS deployment, email and Slack support with local hours SLADeepchecks monitoring pricing page
Basic (LLM Evaluation)Up to 3 seats, 1 AI application, 5,000 DPUs/month, 3 months data retention, unlimited prompt-based metrics, multi-lingual AI applicationsDeepchecks pricing page
Scale (LLM Evaluation)5 seats, 3 AI applications, 20,000 DPUs/month, premium support, premium compliance, guided platform onboardingDeepchecks pricing page
Enterprise (LLM Evaluation)Custom quoteCustom seats and applications, custom DPUs/month, enterprise-grade security, enterprise support package, dedicated customer success team, on-premises or AWS managed service optionsDeepchecks pricing page
Dedicated MonitoringUnlimited models, commercial license, testing and monitoring, custom metrics and checks, SSO and role-based access control, on-premises or SaaS deployment, email/Slack/call support with local hours or 24x7 SLADeepchecks monitoring pricing page
AWS SageMaker Starter$40,000/12 monthsUp to 3 applications, 10 users, 20,000 DPUs/month (~80M tokens)AWS Marketplace
AWS SageMaker Standard$90,000/12 monthsUp to 10 applications, 30 usersAWS Marketplace
AWS SageMaker Pro$180,000/12 monthsUp to 30 applications, 70 users, enterprise-grade toolsAWS Marketplace
Free Trial$0Full access to LLM evaluation features, available through official websiteDeepchecks pricing page
Open Source$0
1 model per deployment, AGPL license, testing and monitoring for tabular and NLP data, community Slack support
Deepchecks monitoring pricing page
Startup Plan$89/model/month
1-10 models, commercial license for company use, testing and monitoring, custom metrics and checks, social login, 12 months data retention, SaaS deployment, email and Slack support with local hours SLA
Deepchecks monitoring pricing page
Basic (LLM Evaluation)
Up to 3 seats, 1 AI application, 5,000 DPUs/month, 3 months data retention, unlimited prompt-based metrics, multi-lingual AI applications
Deepchecks pricing page
Scale (LLM Evaluation)
5 seats, 3 AI applications, 20,000 DPUs/month, premium support, premium compliance, guided platform onboarding
Deepchecks pricing page
Enterprise (LLM Evaluation)Custom quote
Custom seats and applications, custom DPUs/month, enterprise-grade security, enterprise support package, dedicated customer success team, on-premises or AWS managed service options
Deepchecks pricing page
Dedicated Monitoring
Unlimited models, commercial license, testing and monitoring, custom metrics and checks, SSO and role-based access control, on-premises or SaaS deployment, email/Slack/call support with local hours or 24x7 SLA
Deepchecks monitoring pricing page
AWS SageMaker Starter$40,000/12 months
Up to 3 applications, 10 users, 20,000 DPUs/month (~80M tokens)
AWS Marketplace
AWS SageMaker Standard$90,000/12 months
Up to 10 applications, 30 users
AWS Marketplace
AWS SageMaker Pro$180,000/12 months
Up to 30 applications, 70 users, enterprise-grade tools
AWS Marketplace
Free Trial$0
Full access to LLM evaluation features, available through official website
Deepchecks pricing page

How Does Deepchecks Compare to Competitors?

FeatureDeepchecksOpenlayerLangfuseMLflow
LLM-Native EvaluationYesYesYesPartial
Hallucination DetectionYesYesPartialNo
Open Source OptionYesPartialYesYes
Enterprise DeploymentYesYesYesYes
On-Premises SupportYesYesPartialYes
Starting PriceFree (open source)—FreeFree
AWS SageMaker IntegrationYesPartialNoYes
Tabular + NLP SupportYesYesPartialYes
Custom MetricsYesYesYesYes
SSO/SAMLEnterprise onlyEnterprise onlyPartialNo
LLM-Native Evaluation
DeepchecksYes
OpenlayerYes
LangfuseYes
MLflowPartial
Hallucination Detection
DeepchecksYes
OpenlayerYes
LangfusePartial
MLflowNo
Open Source Option
DeepchecksYes
OpenlayerPartial
LangfuseYes
MLflowYes
Enterprise Deployment
DeepchecksYes
OpenlayerYes
LangfuseYes
MLflowYes
On-Premises Support
DeepchecksYes
OpenlayerYes
LangfusePartial
MLflowYes
Starting Price
DeepchecksFree (open source)
Openlayer—
LangfuseFree
MLflowFree
AWS SageMaker Integration
DeepchecksYes
OpenlayerPartial
LangfuseNo
MLflowYes
Tabular + NLP Support
DeepchecksYes
OpenlayerYes
LangfusePartial
MLflowYes
Custom Metrics
DeepchecksYes
OpenlayerYes
LangfuseYes
MLflowYes
SSO/SAML
DeepchecksEnterprise only
OpenlayerEnterprise only
LangfusePartial
MLflowNo

How Does Deepchecks Compare to Competitors?

vs Openlayer

Both provide native evaluation for large language models (LLM), include hallucination detection and enterprise grade features. Deepchecks may be a better choice as it is an open source alternative and has better on premise support. OpenLayer seems to focus more on production monitoring. It appears that deepchecks has more complete coverage for the LLM development lifecycle.

Use Deepchecks for end-to-end testing from development to production, use Openlayer for end-to-end production monitoring.

vs Langfuse

LangFuse focuses on the aspects of observability and monitoring and has very strong open source roots. Deepchecks takes a broader view as a testing and monitoring platform with more complete evaluation capabilities for LLMs. Deepchecks has also better AWS support and more extensive evaluation feature sets than LangFuse.

Use Deepchecks for your testing and compliance needs, use LangFuse for light weight monitoring.

vs MLflow

MLflow is a well established platform for tracking experiments, however it is not LLM native and does not address the LLM specific failure modes such as hallucinations and response quality that Deepchecks addresses. Deepchecks is newer and more specialized than MLflow, however.

Use Deepchecks for LLM related needs, use MLflow for general ML model tracking.

vs Legacy ML Monitoring Tools

Legacy ML testing tools retrofitted to monitor LLMs have a built-in inability to evaluate hallucination and LLM specific failure modes as they are not native to LLMs. A primary benefit of Deepchecks is its native LLM architecture to provide superior LLM evaluation capabilities and greater alignment to regulatory compliance in governing AI.

Deepchecks differentiates itself by being a LLM native architecture, not a legacy ML tool adapted to work with LLMs.

What are the strengths and limitations of Deepchecks?

Pros

  • Native LLM architecture — a native architecture for evaluating hallucinations, response quality, and LLM specific failure modes that legacy tools cannot assess.
  • Complete Lifecycle Coverage — complete coverage from testing infrastructure to enterprise deployment, and monitoring throughout the entire AI development lifecycle.
  • Strong Open Source Foundation — free tier available under an AGPL license providing cost effective evaluation prior to purchase.
  • Integration with AWS SageMaker — native integration provides seamless deployment and billing for AWS customers.
  • Alignment to Regulatory Compliance — enterprise features (SSO, audit logging, on premise deployment) are designed for regulated industries.
  • Multiple Deployment Options — flexible deployment options (cloud hosted, on premises, single tenant, AWS managed service).
  • Pricing Flexibility for Scale — startup pricing ($89/model/month) offers an entry point into using Deepchecks before potential large enterprise spend.
  • Visual Dashboards — visual representation of performance data facilitates quicker analysis and decision making.

Cons

  • Contact Sales Pricing Opaque — pricing for Basic, Scale, and Enterprise tiers is opaque and not publicly disclosed which causes friction when planning budgets and competing evaluations.
  • Time to learn new features — many of the more advanced functions will require a significant amount of time to train those new to the product
  • Resource use — the number of resources required by advanced functions are substantial when conducting large-scale evaluations
  • Lack of visibility into customers — there is very little social proof available as it appears this product has very limited presence in review platforms, specifically G2 where it has a rating of 4.3-4.4 stars and there are no reviews posted at Capterra
  • Risk associated with being an early stage vendor — $14 million was raised in seed funding in June of 2023, however the company is still considered to be in its early stages of development, which means there is a risk that the company could fail to achieve long-term viability versus its competitors who have been around for a longer period of time
  • The potential for lock-in from your current vendor — since the platform is designed to provide advanced functionality for the user, you can expect there to be some switching costs associated with changing vendors if your needs change and you want to continue using an evaluation tool
  • Complexity of pricing model — the product has several different pricing models including Open Source, Start-up ($89 per model), Dedicated (custom pricing) and Enterprise (custom pricing). In order to determine the correct pricing model for your business you will need to compare each of these in detail

Who Is Deepchecks Best For?

Best For

  • Regulated enterprises deploying LLMs — Specialized features — the platform is built for enterprises with compliance-based features such as SSO, Audit Logging and On-Premises Deployment to meet the requirements of governing bodies and also includes a full suite of testing for the same reasons
  • Development teams building LLM applications — Evaluation Architecture based upon LLM — LLM Native Evaluation Architecture provides specific checks for Hallucination and Response Quality that general Machine Learning tools do not
  • AWS-native organizations — Integration into SageMaker and Marketplace Availability on AWS — both of these capabilities make the process of procuring and deploying this product easier for all customers within the AWS Ecosystem
  • Early-stage AI companies and startups — Accessibility through Open Source Tier and Startup Pricing Model — This makes the product easy to get started with before you scale up to enterprise level
  • Organizations with multi-environment requirements — Flexibility — This product offers flexible deployment options including cloud-based and on-premises solutions and AWS Managed Options to help meet the various infrastructure and data-residency requirements that companies may have

Not Suitable For

  • Organizations seeking transparent public pricing — Sales Model Requires Contact — The sales model used for this product may create friction during budgeting for purchasing this product. Consider LangFuse or MLFlow as a substitute option because they offer published pricing
  • Teams without dedicated AI infrastructure resources — Advanced Features Require Significant Resources — Because of this, you may want to consider alternative products that do not require as much resource usage such as LangFuse.
  • General ML model monitoring needs — Over Engineered — This product is purpose-built for Large Language Models (LLMs); therefore, it would be best suited for this type of application and not for the evaluation of traditional machine learning applications. Therefore, you may want to consider other products that were developed for traditional machine learning and include features similar to what you are looking for such as MLFlow or generic machine learning monitoring tools.

Are There Usage Limits or Geographic Restrictions for Deepchecks?

Data Processing Units (DPUs)
Basic: 5,000 DPUs/month; Scale: 20,000 DPUs/month; Enterprise: custom allocation
AI Applications
Basic: 1 application; Scale: 3 applications; Enterprise: custom limit
User Seats
Basic: up to 3 seats; Scale: 5 seats; Enterprise: custom allocation
Data Retention
Basic: 3 months; Scale: depends on plan; Open source startup: 12 months; Enterprise: custom
Monitoring Models
Open source: 1 model per deployment; Startup plan: 1-10 models; Dedicated: unlimited
Token Processing
AWS SageMaker Starter: ~80M tokens/month (~20K DPUs); scaling available with Standard and Pro tiers
Deployment Restrictions
On-premises deployment available on Scale and Enterprise tiers only; Basic tier limited to cloud-hosted SaaS
Support Availability
Open source: community Slack only; Startup: local hours email/Slack; Enterprise: 24/7 call/email/Slack

Is Deepchecks Secure and Compliant?

Enterprise-Grade SecurityEnterprise tier includes enhanced security features and flexible deployment options (on-premises, single-tenant, AWS managed service)
Data LocalityOn-premises deployment option available for Enterprise tier provides 100% data locality for regulated industries requiring data residency
Single-Tenant DeploymentEnterprise customers can deploy dedicated single-tenant instances with choice of region
Role-Based Access ControlEnterprise tier includes RBAC for granular permission management
Compliance FeaturesEnterprise tier includes 'Premium Compliance' features specifically designed for regulated industries
AWS SecuritySageMaker integration leverages AWS infrastructure security; AWS managed service option available for Enterprise
Audit LoggingEnterprise deployments support comprehensive audit trail for compliance and governance

What Customer Support Options Does Deepchecks Offer?

Channels
Available for all tiers; response times vary by tierCommunity support for open source; Slack and email for Startup and Dedicated tiersEnterprise tier onlyEnterprise tier includes dedicated customer success team
Hours
Community SLA for open source; Local hours for Startup and Dedicated tiers; 24/7 available for Enterprise tier
Response Time
Varies by tier; Enterprise tier offers fastest response with dedicated support team
Satisfaction
4.3-4.4 stars on G2
Specialized
Enterprise tier includes dedicated customer success team and guided platform onboarding
Business Tier
Enterprise tier includes enterprise support package with dedicated team and custom SLA
Support Limitations
•Open source tier limited to community Slack support only
•No Capterra reviews available to assess support quality independently
•Startup and Dedicated tiers limited to local hours support (not 24/7)

What APIs and Integrations Does Deepchecks Support?

API Type
Python library API (not REST/GraphQL). Core functionality through Python classes and methods like Suite.run()
Authentication
No API authentication required. Open-source Python library installed via pip
Webhooks
Not supported. Local Python library execution
SDKs
Primary Python SDK (pip install deepchecks). ZenML integration available for MLOps pipelines
Documentation
Comprehensive ReadTheDocs API reference with full class/method documentation at deepchecks.readthedocs.io
Sandbox
No hosted sandbox. Run locally with sample datasets provided in documentation
SLA
N/A for open-source library. Monitoring version may have cloud SLA (details require sales contact)
Rate Limits
N/A. Local execution with no external API limits
Use Cases
Data integrity validation, data drift detection, model performance analysis, weak segments identification, continuous ML monitoring

What Are Common Questions About Deepchecks?

Deepchecks is a free, open-source Python library that can be used to validate the quality of Machine Learning models and the quality of data. You need to define your dataset(s) and your model(s), then you can choose which check(s) you want to apply to it and run it to get a complete report on the results of those checks, showing what issues may have arisen, such as data drift, weak performance of certain parts of the data, and data integrity problems.

Yes, the main Deepchecks library is completely free and open source under MIT License; however, they do offer Deepchecks Monitoring as a paid-for service that has enterprise-level functionality and support.

Currently, tabular data are supported with scikit-learn, XGBoost, and custom models. Support for computer vision is currently in beta/preview state.

While both libraries will perform some form of data validation, Great Expectations is more focused on validating the data itself, while Deepchecks provides checks that are specifically designed for Machine Learning, including checking how well different parts of the data are performing, detecting data drift, and comparing the distributions of the training set vs the test set using visualizations.

Because the library runs on your local hardware with no data being sent off-site, this means your data is always going to remain in your control. The Deepchecks Monitoring service, on the other hand, adheres to enterprise-class security practices and standards (more details are available from the sales team).

Yes, there is an official integration available for ZenML. Also, because all Deepchecks functionality is delivered via standard Python imports, you should find it easy to add Deepchecks to any Python-based Machine Learning workflow (e.g. MLflow, Kubeflow, etc.).

Python 3.8+ and the required packages: pandas, numpy, scikit-learn. The additional package required for the specific checks are torchvision (for vision). Install via pip: pip install deepchecks.

For the open-source library, issues and discussion can be found on the GitHub repository's issues page and community forum. For the commercial Monitoring product, a full-time support team, a Slack community, and professional services are all available.

There are no costs associated with using the open-source library and there are no usage restrictions either. A free trial is offered by the Monitoring service, but please contact sales to get access to an enterprise demo/trial.

Is Deepchecks Worth It?

Deepchecks has been the most widely used open source tool for validating all aspects of your machine learning workflow since its inception. It offers an array of tools that can validate the integrity of your data, detect changes in your data over time as well as analyze the performance of your trained models. As such it provides a number of unique benefits for teams working on production machine learning pipelines. In addition to providing a suite of tools for evaluating how well your models are performing, Deepchecks automatically segments your data to help you identify areas where improvement may be needed. This segmentation allows teams to quickly pinpoint problems with their models or data. The deepchecks enterprise monitoring platform is designed to extend the capabilities of deepchecks into continuous validation environments for enterprises.

Recommended For

  • Teams using production model monitoring
  • Data science teams looking to receive automated validation reports
  • MLOps platforms that require open-source validation components
  • Companies transitioning their machine learning models to production

!
Use With Caution

  • Teams that need low latency or real-time validation - only batch processing
  • Computer vision projects - currently table-focused, will add computer vision as they continue to develop
  • Organizations requiring fully-managed SaaS that do not have python expertise

Not Recommended For

  • Non-machine learning data validation needs - specializes in machine learning
  • Budget constrained teams looking for fully-managed enterprise support
  • Real-time/low-latency streaming machine learning validation needs
Expert's Conclusion

Deepchecks is necessary for any serious machine learning production pipeline that requires robust, automated validation and monitoring capabilities.

Best For
Teams using production model monitoringData science teams looking to receive automated validation reportsMLOps platforms that require open-source validation components

What do expert reviews and research say about Deepchecks?

Key Findings

Deepchecks is a mature open source library for machine learning validation that includes comprehensive API documentation and ongoing development. Additionally, it supports full machine learning lifecycle validation from data integrity to model performance segmentation. A commercial Monitoring platform is available for enterprise continuous validation. There is strong ZenML integration with deepchecks for MLOps workflows.

Data Quality

Good - comprehensive technical documentation and GitHub repository analysis. Limited commercial pricing/enterprise feature details (sales contact required). No recent funding or customer case study data publicly available.

Risk Factors

!
Open source library does not include the ability to use Python/ML engineering expertise
!
Computer vision support is developing but not yet mature
!
Details and pricing for the commercial platform are opaque
!
Competitive open source environment for machine learning validation
Last updated: February 2026

What Are the Best Alternatives to Deepchecks?

  • •
    Great Expectations: Leading open source framework for data validation. General purpose data quality compared to Deepchecks' machine learning specific focus. Easy for non machine learning focused data teams however lacks model performance and drift analysis. Great for data engineering focused pipelines.
  • •
    Evidently AI: Evidently AI is an open source solution that has a strong ability to detect drift in models and generate reports. While it does have some similarities to deepchecks in terms of its ml focus, it is much less complex than deepchecks. Evidently AI has fewer automated checks and a weaker suite customization feature. It would be best used as a quick prototype for monitoring solutions. (arize.com)
  • •
    Arize AI: Arize is an enterprise level ml observability solution that includes both monitoring and debugging. It is a fully managed saas solution and offers a higher cost option compared to a self hosted version like deepchecks. The arize solution also provides a richer ui experience along with team features. Arize would be the best choice for companies looking for a managed service. (whylabs.ai)
  • •
    WhyLabs: Why labs is an ml observability solution that includes real time monitoring and alerting. It offers stronger real time monitoring capabilities compared to the batch focused solution from deepchecks. However, whylabs offers a more expensive saas model. Therefore, the best use case for this solution is for production environments where the need for timely alerts is paramount. (fiddler.ai)
  • •
    Fiddler AI: Fiddler is an enterprise level ml explainability and monitoring platform. While fiddler offers model interpretation capabilities that are stronger than those found in deepchecks; it's primary function is not validating models. The sales process for the fiddler product is long and complex due to its high price point and the complexity of the enterprise environment. Due to these factors, the best fit for fiddler is highly regulated industries that require model explainability. (evidentlyai.com)

What Additional Information Is Available for Deepchecks?

Open Source Community

Evidence ai has active development on github with over 2000 stars. They offer regular releases and community contributed code. In addition, they provide extensive examples of how to utilize their solution via notebooks and documentation. (readthedocs.org)

Documentation Quality

Evidence ai utilizes readthedocs to document their solution and provides a comprehensive api reference, tutorials, and example notebooks. Their solution supports interactive jupyter notebook functionality for all of their checks. (zenml.io)

MLOps Integrations

Evidence ai has a zenml integration to support the automation of their pipeline and simplify the integration of evidence ai into larger workflows utilizing python apis. (arize.com)

Dual Licensing Model

Evidence ai uses the mit license for the core of their open source solution and also sells a commercial monitoring platform. They utilize an open core strategy allowing them to enable users to host their solution themselves while offering an upgrade path to the commercial version. (arize.com)

Check Suites

Evidence ai offers pre built suites for data integrity, drift detection, model performance and full validation. Their solution offers 50+ individual checks and allows for significant customization of each check. (arize.com)

What Are Deepchecks's Evaluation Metrics?

34+
Built-in Properties
Completeness, Coherence, Relevance, Toxicity, Fluency
Properties Evaluated
Automated Aggregation
Overall Score

What Testing Capabilities Does Deepchecks Offer?

Version Comparison

Allows you to spot improvements or regressions instantaneously (arize.com)

Data Exploration

Allow you to slice and dice your input/output by pre-calculating properties for you (arize.com)

AI-Assisted Annotations

Automatically scores your model based on predefined criteria, however, also allows you to override these predefined criteria. (arize.com)

Custom Metrics

Offers customizable metrics using LLMs or your own custom code (arize.com)

Root Cause Analysis

Enables you to identify weak areas/topics in your training dataset (arize.com)

How Does Deepchecks's Benchmark Support Compare?

BenchmarkCategorySupported
Custom DatasetsLLM ApplicationsYes
Agent WorkflowsMulti-step TasksYes
Production MonitoringReal-timeYes

What Model Compatibility Does Deepchecks Support?

OpenAIAnthropicLlamaMistralCustom LLMsAI Agents

What Is Deepchecks's Evaluation Modes?

Reference-Free
Primary method using SLMs & NLP pipelines
Reference-Based
Expected Output Similarity with ground truth
Automated + Manual
AI-assisted annotations with overrides
Session-Level
End-to-end agent evaluation

How Does Deepchecks Ensure Safety Through Testing?

Toxicity Detection

Flag harmful generated content (arize.com)

Bias Detection

Detects demographic bias in your model (arize.com)

PII Detection

Prevents personal identifiable information from leaking out of your model

Anomaly Monitoring

Continuous deviation detection

What Is Deepchecks's Ci Cd Integration?

Deployment
REST API + Python SDK
Monitoring
Production monitoring & alerts
Version Control
Automated version comparison
Notifications
Slack, Email, Webhook support

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