How we reduced our backend server costs by 40% using Redis caching before launching

Hey Product Hunt community!

When preparing for a launch or handling sudden user traffic, scaling servers can quickly become expensive if every single user request hits your main database.

While stress-testing our API endpoints for our upcoming SaaS, we noticed that 70% of database queries were fetching the exact same static configuration and user preference data over and over.

Here is the exact setup we used to optimize response times and cut server overhead:

1. In-Memory Redis Caching: We cached high-frequency read-only data with a 5-minute TTL (Time To Live). This dropped direct database hits by over 60%.

2. Query Optimization: Replaced heavy nested SQL queries with indexed lookups, reducing response time from ~320ms to under 45ms.

3. Asynchronous Task Queue: Moved non-essential tasks (like sending welcome emails or triggering notifications) to background workers using Celery.

This simple optimization not only handled 5x higher concurrent users during stress tests but also cut our estimated monthly infrastructure costs significantly.

For fellow tech builders and founders:

What is your go-to database optimization technique before going live on Product Hunt?

Have you ever experienced a sudden database bottleneck during a traffic spike? How did you solve it?

Would love to hear your favorite backend tricks!