Neon: Hero

See the full landing page

Why we chose this SaaS landing page example

B2B SaaS websites providing developer tools are often accused of poor copywriting and adding too much tech jargon to their page.

But there are some fantastic examples out there – one of them being Neon and their website hero texts.

‘Ship faster with Postgres’ makes it easy for the page visitor to decide if this is the right place for them. Developers with no interest in setting up a Postgres database hit the back button, and those planning to get started or upgrade their Postgres stick around.

The description text, ‘The database you love, on a serverless platform designed to help you build reliable and scalable applications faster’, is approachable, talks to the page directly (’you’ x 2), and skips all jargon.

And if we want to break down the hero section texts even further:

'Ship faster with Postgres'

➡️ Many great SaaS hero texts like this start with the main capability.

'The database you love'

➡️ This handles objections. Developers aren’t always keen to add another tool to their stack. Neon assures them that it really is Postgres, as promised in the title – not a new service on top.

'Serverless platform'

➡️ The best SaaS hero texts mention a concrete feature that helps deliver the benefits. The other main features are straight below the hero copy texts.

'Reliable and scalable applications faster'

➡️ The description ends by telling why it matters and why developers should switch from their current solution.

Neon gets 5/5 for SaaS hero copywriting.

What’s Neon?

Neon is designed for developers and SaaS teams who currently use traditional Postgres databases with manual scaling and provisioning, making performance optimization and scaling difficult. Neon provides a serverless, autoscaling Postgres platform with features like instant database branching, automatic scaling, and point-in-time recovery. This enables teams to build applications faster, reduce infrastructure management efforts, and optimize costs by automatically scaling databases based on demand.