Best Mostly AI Alternatives and Competitors in 2026

Mostly AI is one of the leading enterprise synthetic data platforms, helping organizations generate realistic, privacy-safe datasets for analytics, software testing, and machine learning. By creating statistically accurate synthetic data that preserves the patterns and relationships found in production datasets, Mostly AI enables businesses to innovate with sensitive information while remaining compliant with privacy regulations.

Although Mostly AI is widely recognized for enterprise synthetic structured data, it isn’t the ideal solution for every use case. Some organizations need broader test data management capabilities, while others prioritize data masking, AI-powered privacy engineering, cloud-native security services, or open-source anonymization frameworks. As privacy-preserving AI adoption continues to grow, many businesses evaluate Mostly AI alternatives that better align with their infrastructure, compliance requirements, and development workflows.

This guide compares the leading Mostly AI competitors based on synthetic data quality, privacy protection, enterprise scalability, deployment flexibility, integrations, and AI capabilities to help you choose the right platform for your organization.

What is Mostly AI?

Mostly AI is an enterprise synthetic data platform that uses generative AI to create statistically representative datasets without exposing sensitive customer information. It enables organizations to safely share data, train AI models, perform analytics, and test applications while complying with regulations such as GDPR, HIPAA, and CCPA.

The platform is commonly used across banking, insurance, healthcare, telecommunications, and other highly regulated industries where access to realistic data is essential but privacy risks prevent sharing production datasets.

Why Look for Mostly AI Alternatives?

While Mostly AI is one of the strongest enterprise synthetic data solutions available, different organizations often have different technical and compliance priorities.

• Need integrated test data management instead of synthetic data alone.
• Require enterprise data masking and anonymization capabilities.
• Want cloud-native privacy services for AWS, Azure, or Google Cloud.
• Need broader AI data engineering and privacy automation.
• Prefer open-source privacy frameworks.
• Require stronger DevOps and CI/CD integrations.
• Looking for more flexible deployment or pricing options.
• Need industry-specific compliance or governance capabilities.

Mostly AI Alternatives Comparison

12 Best Mostly AI Alternatives and Competitors

#1 Gretel.ai

Gretel.ai is one of the closest Mostly AI alternatives because both platforms specialize in generating privacy-preserving synthetic data for enterprise organizations. While Mostly AI is best known for producing statistically accurate structured datasets for regulated industries, Gretel.ai extends beyond synthetic data generation by combining privacy engineering, data transformation, anonymization, and AI-powered APIs into a broader developer platform. Organizations building AI products, LLM applications, and data-driven software often choose Gretel.ai when they need more flexibility across the entire data lifecycle rather than synthetic structured data alone.

Compared with Mostly AI, Gretel.ai offers a broader toolkit for engineering teams that need programmable privacy workflows alongside synthetic data generation. Its APIs make it easier to automate data anonymization, generate privacy-safe datasets, and integrate synthetic data into AI development pipelines. For businesses seeking a platform that supports both software engineering and machine learning use cases, Gretel.ai is one of the strongest replacements.

Key Features

• Generates synthetic datasets that preserve statistical patterns without exposing real customer records.
• Combines synthetic data generation with built-in data transformation and anonymization tools.
• Provides developer APIs for integrating privacy-safe data into engineering workflows.
• Supports AI model training, analytics, application testing, and LLM development.
• Evaluates privacy risks before datasets are shared across teams.
• Helps organizations comply with GDPR, HIPAA, CCPA, and other privacy regulations.
• Scales from development projects to enterprise AI and analytics initiatives.

#2 Hazy

Hazy is a strong Mostly AI alternative for organizations that prioritize privacy-preserving data sharing across highly regulated industries. Like Mostly AI, it generates synthetic datasets that retain the statistical characteristics of production data while eliminating personally identifiable information. Its primary focus is helping enterprises unlock sensitive datasets for analytics, research, AI development, and collaboration without violating privacy regulations.

Compared with Mostly AI, Hazy places greater emphasis on enterprise privacy engineering and secure data sharing rather than large-scale synthetic data generation alone. Financial institutions, healthcare providers, insurers, and public sector organizations often choose Hazy when they need to collaborate across departments or external partners while maintaining strict governance over sensitive information.

Key Features

• Generates realistic synthetic datasets that closely reflect production data behavior.
• Protects sensitive customer information while preserving analytical usefulness.
• Enables secure internal and external data sharing without exposing real records.
• Reduces disclosure and re-identification risks using advanced privacy techniques.
• Supports analytics, AI model development, and regulatory reporting.
• Designed specifically for regulated industries with strict compliance requirements.
• Integrates with enterprise data platforms and governance workflows.

#3 Synthesized

Synthesized combines synthetic data generation, intelligent data masking, and automated test data management into a single privacy engineering platform. Organizations comparing Mostly AI alternatives frequently shortlist Synthesized because it serves both AI teams and software engineering teams, allowing them to generate realistic datasets while automatically provisioning compliant development and testing environments.

Unlike Mostly AI, which primarily focuses on synthetic structured data, Synthesized provides a stronger DevOps-centric workflow. Engineering teams can continuously generate privacy-safe test datasets, automate data refreshes, and integrate secure data provisioning directly into CI/CD pipelines, making it particularly attractive for organizations practicing continuous software delivery.

Key Features

• Creates production-like datasets for testing, development, and analytics.
• Combines synthetic data generation with intelligent data masking.
• Automates secure test data provisioning through APIs and DevOps workflows.
• Preserves relationships between complex enterprise datasets.
• Refreshes development environments without manual database preparation.
• Integrates directly into CI/CD pipelines for continuous delivery.
• Supports privacy compliance across modern engineering workflows.

#4 Tonic.ai

Tonic.ai is one of the best Mostly AI competitors for organizations that need secure development and testing environments rather than synthetic data for analytics alone. Instead of focusing exclusively on AI-ready datasets, Tonic.ai creates production-like development databases using intelligent masking, de-identification, and synthetic data generation. This makes it particularly valuable for software engineering teams that need realistic application data while protecting customer privacy.

Compared with Mostly AI, Tonic.ai is more heavily optimized for software development, QA, and DevOps workflows. Engineering teams can rapidly provision compliant testing environments without copying sensitive production databases, helping accelerate release cycles while maintaining regulatory compliance.

Key Features

• Produces realistic development datasets without exposing production records.
• Automatically masks sensitive customer information while preserving application behavior.
• Supports self-service provisioning for developers and QA teams.
• Preserves database relationships and referential integrity.
• Integrates with DevOps pipelines to automate environment creation.
• Enables frequent refreshes of testing environments with minimal manual effort.
• Helps engineering teams comply with GDPR, HIPAA, PCI DSS, and CCPA.

Also Read: Best Tonic.ai Alternatives and Competitors in 2026

#5 Delphix

Delphix is an enterprise data platform that combines data virtualization, masking, and automated provisioning to accelerate software development across complex IT environments. Organizations evaluating Mostly AI alternatives often consider Delphix when they require secure production-like environments for application development rather than synthetic datasets for AI and analytics.

Unlike Mostly AI, Delphix virtualizes production databases instead of generating synthetic copies, allowing development teams to provision full environments in minutes while dramatically reducing storage consumption. This makes it an excellent choice for enterprises modernizing large application portfolios.

Key Features

• Creates virtual production databases without duplicating storage.
• Automates masking before development environments are provisioned.
• Refreshes development databases in minutes instead of days.
• Supports major enterprise databases including Oracle, SQL Server, SAP, and PostgreSQL.
• Integrates with DevOps and CI/CD pipelines for automated delivery.
• Reduces infrastructure costs by eliminating redundant database copies.
• Centralizes governance and auditing across development environments.

#6 K2view Test Data Management

K2view Test Data Management helps enterprises create secure, business-ready test environments by provisioning only the data required for specific business entities instead of entire databases. Organizations searching for Mostly AI alternatives often choose K2view when software delivery speed, enterprise scalability, and operational efficiency are higher priorities than synthetic data generation alone.

Compared with Mostly AI, K2view specializes in intelligent test data provisioning for complex enterprise systems. Its unique micro-database architecture enables development teams to provision realistic datasets quickly while minimizing infrastructure costs and maintaining strict privacy controls.

Key Features

• Creates lightweight business-entity datasets instead of cloning full production databases.
• Delivers secure development environments within minutes.
• Preserves application relationships while masking sensitive information.
• Supports SAP, Salesforce, Oracle, SQL Server, Db2, and other enterprise platforms.
• Enables self-service data provisioning for developers and testers.
• Synchronizes changes with production systems automatically.
• Scales efficiently across large global engineering organizations.

#7 Informatica Test Data Management

Informatica Test Data Management is an enterprise platform that helps organizations create secure, right-sized datasets for application development, testing, and modernization. While Mostly AI focuses on generating synthetic structured data for AI and analytics, Informatica emphasizes masking, subsetting, provisioning, and governance across large enterprise environments. Organizations already invested in the Informatica ecosystem often choose it because it integrates naturally with their broader data management strategy.

Compared with Mostly AI, Informatica is better suited for enterprises managing complex application landscapes where secure test data provisioning is a core operational requirement. Its ability to combine synthetic records with masked production data also gives organizations greater flexibility when preparing development environments.

Key Features

• Creates smaller, production-like datasets through intelligent data subsetting.
• Combines synthetic records with masked production data for realistic testing.
• Automates secure test data delivery across multiple environments.
• Supports Oracle, SAP, SQL Server, Salesforce, Db2, and other enterprise systems.
• Integrates with Informatica Intelligent Data Management Cloud.
• Provides centralized governance, auditing, and compliance reporting.
• Reduces storage costs by eliminating unnecessary production data copies.

#8 IBM watsonx.data intelligence

IBM watsonx.data intelligence is designed for organizations that need trusted, governed data before it is used for AI, analytics, or software development. While it is not a dedicated synthetic data platform, many enterprises evaluating Mostly AI alternatives consider IBM because of its comprehensive approach to data governance, metadata management, privacy, and regulatory compliance across large data estates.

Unlike Mostly AI, IBM focuses on ensuring data quality, discoverability, lineage, and policy enforcement throughout the entire data lifecycle. This makes it particularly valuable for enterprises building responsible AI initiatives where governance is as important as privacy.

Key Features

• Discovers and classifies sensitive enterprise data automatically.
• Tracks metadata and data lineage across hybrid environments.
• Enforces governance policies before data reaches AI or analytics teams.
• Improves data quality for machine learning and reporting workloads.
• Integrates with IBM’s AI, analytics, and cloud ecosystem.
• Provides centralized access controls and compliance management.
• Supports enterprise-wide responsible AI initiatives.

#9 Google Cloud Sensitive Data Protection

Google Cloud Sensitive Data Protection is Google’s managed privacy service for discovering, classifying, and de-identifying sensitive information across cloud environments. Organizations already building data platforms on Google Cloud frequently compare it with Mostly AI because it provides native privacy controls that integrate directly with Google’s analytics and AI services.

Compared with Mostly AI, Google Cloud Sensitive Data Protection focuses on identifying and protecting sensitive information rather than generating entirely synthetic datasets. This makes it an excellent option for organizations that want to secure production data before it is used for analytics, development, or machine learning.

Key Features

• Automatically discovers sensitive information across Google Cloud services.
• Applies masking, tokenization, and de-identification using configurable policies.
• Protects structured and unstructured datasets at enterprise scale.
• Integrates natively with BigQuery, Cloud Storage, and Vertex AI.
• Supports automated compliance across cloud-based data pipelines.
• Includes built-in detectors for financial, healthcare, and personal information.
• Continuously monitors sensitive data across Google Cloud environments.

#10 Broadcom Test Data Manager

Broadcom Test Data Manager is a mature enterprise solution for creating compliant development and testing environments through automated data masking, subsetting, and provisioning. Organizations evaluating Mostly AI alternatives often consider Broadcom when they require secure production-like test environments instead of AI-focused synthetic data generation.

Unlike Mostly AI, Broadcom is designed specifically for enterprise software delivery. It helps large IT organizations reduce storage costs, improve testing efficiency, and maintain compliance while providing development teams with realistic datasets.

Key Features

• Creates compliant test datasets through automated masking and subsetting.
• Extracts only the production data required for testing specific applications.
• Supports complex enterprise database environments.
• Automates dataset refreshes across multiple development teams.
• Integrates with enterprise testing and DevOps workflows.
• Enforces governance policies throughout software delivery.
• Built for large organizations running mission-critical applications.

#11 Oracle Data Safe

Oracle Data Safe is a managed cloud security service that helps organizations discover, mask, monitor, and protect sensitive information stored in Oracle databases. Businesses operating primarily within Oracle environments often evaluate it as a Mostly AI competitor because it provides enterprise-grade privacy protection while simplifying compliance for development and testing teams.

Compared with Mostly AI, Oracle Data Safe is optimized specifically for Oracle workloads rather than synthetic data generation. It allows organizations to create secure development datasets directly from production databases while maintaining strong security controls and continuous monitoring.

Key Features

• Discovers and classifies sensitive information stored in Oracle databases.
• Creates masked datasets suitable for development and testing.
• Continuously audits database activity and privileged user access.
• Performs automated security assessments and risk analysis.
• Generates compliance reports for regulatory requirements.
• Integrates natively with Oracle Cloud Infrastructure.
• Simplifies database security through centralized administration.

#12 Microsoft Presidio

Microsoft Presidio is an open-source framework that detects, classifies, and anonymizes personally identifiable information across structured and unstructured datasets. Although it isn’t a complete synthetic data platform like Mostly AI, organizations often use it alongside AI and analytics workflows to sanitize sensitive information before data is shared or processed.

Compared with Mostly AI, Microsoft Presidio gives engineering teams complete flexibility to build custom privacy workflows without relying on proprietary platforms. Its open-source architecture makes it particularly appealing for developers who want to integrate privacy controls directly into their applications and data pipelines.

Key Features

• Detects personally identifiable information across multiple data formats.
• Supports custom recognizers for organization-specific sensitive information.
• Replaces or anonymizes confidential data before it enters development workflows.
• Integrates easily into Python applications and data engineering pipelines.
• Supports AI preprocessing, analytics, and compliance automation.
• Fully open source with extensive customization capabilities.
• Helps organizations meet privacy requirements while retaining usable data.