An open-source serverless Postgres platform that separates storage and compute for autoscaling, branching, and scale-to-zero.
Neon is an open-source serverless PostgreSQL database platform that separates storage from compute. It provides autoscaling, instant database branching similar to Git, and the ability to scale compute resources down to zero when not in use, offering a modern, cost-effective alternative to traditional managed PostgreSQL services.
Developers and teams building cloud-native applications who want PostgreSQL compatibility with modern features like branching, serverless scaling, and reduced costs for development and staging environments.
Neon offers full PostgreSQL compatibility while introducing innovative features like instant branching and scale-to-zero, which are not available in traditional managed PostgreSQL services. Its open-source nature and separation of storage and compute provide flexibility and cost savings.
Neon: Serverless Postgres. We separated storage and compute to offer autoscaling, code-like database branching, and scale to zero.
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Enables creating isolated, copy-on-write branches in seconds for development and testing, as demonstrated with commands like 'cargo neon timeline branch' in the quick start.
Compute nodes automatically scale down to zero when idle, eliminating costs for inactive databases, a core feature highlighted in the key descriptions.
Maintains standard PostgreSQL protocol and syntax, allowing seamless use with existing clients like psql, as emphasized in the philosophy and quick start.
Separates storage and compute for independent scaling, optimizing resource utilization and handling variable workloads, as outlined in the architecture overview.
Setting up a local development environment requires installing numerous dependencies and specific tool versions across Linux and macOS, as detailed in the lengthy installation sections.
As a relatively new open-source project, it may lack some third-party integrations and community support compared to mature PostgreSQL distributions like Amazon RDS.
The decoupled architecture can introduce additional latency for storage access, which might impact performance-sensitive applications, a trade-off for scalability.