Databasus has reached a significant milestone in 2025: it became the most starred PostgreSQL backup tool on GitHub, surpassing established solutions like WAL-G, pgBackRest and Barman. This achievement reflects Databasus becoming the industry standard for PostgreSQL backups, marking a shift in how developers and teams approach database protection — moving away from complex CLI tools toward intuitive, production-ready solutions that work out of the box. From Postgresus to Databasus: the evolution Databasus started its journey under a different name — Postgresus. The original project was a simple UI wrapper for pg_dump, designed to help developers set up PostgreSQL backups without wrestling with command-line tools. The idea was straightforward: make database backups accessible to everyone, not just experienced DBAs. What began as a small utility for personal projects evolved into something much larger. Tens of thousands of users now rely on it daily. The project grew from a simple backup scheduler into a comprehensive backup management system suitable for individuals, teams, companies and large enterprises. The renaming to Databasus marked three important changes: Expanded scope: No longer just a UI for pg_dump — it's a production-grade backup management system
Multi-database support: Added MySQL, MariaDB and MongoDB alongside PostgreSQL
Trademark compliance: "Postgres" is a trademark of PostgreSQL Inc. and cannot be used in project names Expanded scope: No longer just a UI for pg_dump — it's a production-grade backup management system Expanded scope Multi-database support: Added MySQL, MariaDB and MongoDB alongside PostgreSQL Multi-database support Trademark compliance: "Postgres" is a trademark of PostgreSQL Inc. and cannot be used in project names Trademark compliance The project maintained its core philosophy throughout this evolution: backups should be simple to set up, reliable in production and accessible to developers without deep database expertise. Why developers chose Databasus over alternatives The PostgreSQL backup ecosystem has several established tools, each with its own strengths. WAL-G, pgBackRest and Barman are all capable solutions used in production environments worldwide. So why did Databasus gain more traction? Databasus: Databasus: Interface: Web UI
Installation: One-line script or Docker
Multi-database support: PostgreSQL, MySQL, MariaDB, MongoDB
Cloud database support: AWS RDS, Cloud SQL, Azure
Team features: Workspaces, RBAC, audit logs
Built-in notifications: Slack, Discord, Telegram, Teams, Email
Learning curve: Minimal Interface: Web UI Installation: One-line script or Docker Multi-database support: PostgreSQL, MySQL, MariaDB, MongoDB Cloud database support: AWS RDS, Cloud SQL, Azure Team features: Workspaces, RBAC, audit logs Built-in notifications: Slack, Discord, Telegram, Teams, Email Learning curve: Minimal WAL-G: WAL-G: Interface: CLI only
Installation: Binary + configuration
Multi-database support: PostgreSQL, MySQL, MS SQL
Cloud database support: Backup only
Team features: None
Built-in notifications: None
Learning curve: CLI proficiency required Interface: CLI only Installation: Binary + configuration Multi-database support: PostgreSQL, MySQL, MS SQL Cloud database support: Backup only Team features: None Built-in notifications: None Learning curve: CLI proficiency required pgBackRest: pgBackRest: Interface: CLI only
Installation: Manual configuration
Multi-database support: PostgreSQL only
Cloud database support: No
Team features: None
Built-in notifications: None
Learning curve: DBA expertise required Interface: CLI only Installation: Manual configuration Multi-database support: PostgreSQL only Cloud database support: No Team features: None Built-in notifications: None Learning curve: DBA expertise required Barman: Barman: Interface: CLI only
Installation: Manual configuration
Multi-database support: PostgreSQL only
Cloud database support: No
Team features: None
Built-in notifications: None
Learning curve: DBA expertise required Interface: CLI only Installation: Manual configuration Multi-database support: PostgreSQL only Cloud database support: No Team features: None Built-in notifications: None Learning curve: DBA expertise required The comparison reveals a pattern: traditional backup tools were built for DBAs managing self-hosted infrastructure. They assume command-line proficiency, PostgreSQL internals knowledge and the ability to set up WAL archiving. These tools excel at their intended purpose — physical backups with Point-in-Time Recovery for mission-critical systems. Databasus took a different approach. It recognized that most projects don't need second-precise PITR. Hourly or daily backups are sufficient for 99% of applications. By focusing on logical backups with pg_dump, Databasus works seamlessly with both self-hosted databases and cloud-managed services like AWS RDS, Google Cloud SQL and Azure Database. The transparency report that built trust Security-focused projects face a unique challenge: users need to trust that the code handling their database credentials and backup files is reliable. Databasus addressed this by publishing a detailed AI usage policy directly in the project README. AI usage policy The policy explains exactly how AI is used in development: Code review and vulnerability scanning — with developer verification
Documentation cleanup — with developer approval
Development assistance — developer writes final implementation
PR verification — after human review, not instead of it Code review and vulnerability scanning — with developer verification Documentation cleanup — with developer approval Development assistance — developer writes final implementation PR verification — after human review, not instead of it Equally important is what AI is not used for: writing entire features, "vibe coding" or generating code without line-by-line human verification. The project maintains strict quality gates regardless of whether code comes from AI suggestions, external contributors or core maintainers. This transparency matters because Databasus handles sensitive data. It stores database credentials, manages encryption keys and creates backups containing production data. A single vulnerability could expose sensitive information across multiple organizations. By documenting their development practices publicly, the maintainers created accountability and gave users confidence in the codebase. Features that made the difference Databasus didn't just simplify backups — it added capabilities that traditional tools lack entirely. Team collaboration became a key differentiator. Workspaces allow organizations to group databases by project or team. Role-based access control lets administrators define who can view, manage or configure backups. Audit logs track all system activities for compliance and accountability. None of the traditional backup tools offer these features natively. Team collaboration Built-in notifications eliminated the need for custom scripting. Teams receive backup status updates through Slack, Discord, Telegram, Microsoft Teams, Email or webhooks. When a backup fails at 3 AM, the on-call engineer gets notified immediately — without setting up external monitoring infrastructure. Built-in notifications Security by default meant AES-256-GCM encryption for all sensitive data, unique encryption keys for each backup file and read-only database connections that minimize risk even if credentials are compromised. Security by default Enterprise-grade while remaining easy to use The most impressive aspect of Databasus's growth is that it scaled to enterprise needs without sacrificing simplicity. New users can still deploy with a single Docker command: docker run -d \
  --name databasus \
  -p 4005:4005 \
  -v ./databasus-data:/databasus-data \
  --restart unless-stopped \
  databasus/databasus:latest docker run -d \
  --name databasus \
  -p 4005:4005 \
  -v ./databasus-data:/databasus-data \
  --restart unless-stopped \
  databasus/databasus:latest Five minutes later, they have a working backup system with a web interface. No configuration files to edit, no WAL archiving to set up, no cron jobs to manage. The same tool that handles backups for individual side projects also runs in production environments serving thousands of users. This balance between simplicity and capability is rare. Most tools either stay simple and hit limitations at scale, or become powerful but require significant expertise to operate. Databasus managed to grow in capability while keeping the onboarding experience straightforward. What this means for the PostgreSQL ecosystem Databasus becoming the most starred PostgreSQL backup tool signals its position as the new industry standard for database protection. This represents a broader trend where developers increasingly expect infrastructure tools to have good UX. The command-line-first approach that dominated database tooling for decades is giving way to solutions that prioritize accessibility without sacrificing reliability. The numbers tell the story: with tens of thousands of daily active users and more GitHub stars than any competing tool, Databasus has become the go-to choice for PostgreSQL backups across individual projects, startups and enterprises. This widespread adoption establishes it as the baseline solution that teams evaluate other tools against. This doesn't mean traditional tools are obsolete. pgBackRest and Barman remain excellent choices for organizations requiring second-precise PITR on self-hosted infrastructure. WAL-G serves teams who prefer CLI workflows and need advanced features like delta backups. These tools solve different problems for different audiences. But for the majority of projects — from side projects to production applications to enterprise deployments — Databasus proved that backups don't have to be complicated. A well-designed interface, sensible defaults and built-in features for teams can make database protection accessible to everyone. Looking forward The project continues to evolve. PostgreSQL remains the primary focus, with 100% support for versions 12 through 18. MySQL, MariaDB and MongoDB support expands the tool's reach for organizations running multiple database systems. New storage destinations and notification channels get added based on community feedback. The star count milestone is meaningful, but it reflects something more important: a growing community of developers who found a backup solution that works for them. The real measure of success isn't GitHub stars — it's the databases being protected and the teams sleeping better knowing their data is safe. Website: https://databasus.comGitHub: https://github.com/databasus/databasusTransparency report: https://github.com/databasus/databasus?tab=readme-ov-file#ai-disclaimer Website https://databasus.com GitHub https://github.com/databasus/databasus Transparency report https://github.com/databasus/databasus?tab=readme-ov-file#ai-disclaimer

This story contains AI-generated text. The author has used AI either for research, to generate outlines, or write the text itself. 

Microsoft

Google

Google Cloud

MongoDB

Databasus Became the Most Popular PostgreSQL Backup Tool in 2025

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

ELK, Loki, and Graylog Were Overkill, So I built Log Bull

Prompt Engineering for Architects: Using LLMs to Validate System Design Constraints

The MCP Hype Train: A Protocol’s Promise vs. Production Reality

Production Environment: Where AI Agent Demos Go To Die

10 Ways to Optimize Your Database

10 Ways to Reduce Data Loss and Potential Downtime Of Your Database

ELK, Loki, and Graylog Were Overkill, So I built Log Bull

Prompt Engineering for Architects: Using LLMs to Validate System Design Constraints

The MCP Hype Train: A Protocol’s Promise vs. Production Reality

Production Environment: Where AI Agent Demos Go To Die

10 Ways to Optimize Your Database

10 Ways to Reduce Data Loss and Potential Downtime Of Your Database

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps