Neon
Instantly provision, branch, and scale Postgres databases with seamless serverless performance and flexibility.
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Neon
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Neon is a serverless Postgres platform beloved by developers for building reliable, scalable applications. It offers instant database provisioning, automatic scaling, and innovative database branching - letting teams create isolated copies for development and testing. With features like point-in-time recovery, read replicas, and support for AI applications through HNSW indexing, Neon streamlines database operations. Trusted by companies like Retool and Koyeb, it seamlessly integrates with existing tech stacks and frameworks.

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Features we love
Create isolated database copies for dev/testing instantly.
Scale database compute up/down automatically on workload.
Provision new Postgres databases in only 300ms.
Toksta's take

Neon's serverless Postgres offering stands out with its autoscaling and branching capabilities. Creating isolated database copies for development, testing, or preview deployments streamlines workflows considerably. Businesses can leverage Neon's serverless driver for efficient querying in serverless functions and edge environments, accelerating application development and responsiveness. The instant provisioning and seamless integration with existing tech stacks are also compelling features.

By contrast, Neon isn't without its quirks. Integrating the serverless driver with Prisma requires extra steps, and the SSL mode=require connection string might necessitate environment adjustments. The need to reset branches after merging to the main production branch, dropping all changes, presents a significant limitation for collaborative workflows.

While Neon introduces innovative features, potential users must weigh the benefits against the setup complexities and the branch management caveat.

Spotlighted by
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Adam Skjervold
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subscribers
Growth tip

Utilize Neon's branching feature to create isolated environments for each new feature or update you're developing. This allows your team to test changes with real data without risking corruption or downtime in your production environment, ultimately leading to faster, more reliable deployments and reduced risk of errors impacting your live users.

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