Read replicas have been part of the Postgres toolkit for years. For many teams, they're the default way to offload pressure from the primary database. But as workloads grow and expectations for performance and efficiency increase, the traditional read replica model starts to show its limits.
Postgres replicas work. They’ve helped countless teams move reads off their primary, improve availability, and keep critical workloads running smoothly.
They’re a proven tool for:
For many, read replicas are the first step toward scaling a production database.
But as systems evolve, teams often run into a familiar set of challenges.
Most replicas are sized to handle traffic spikes, but those spikes may only last an hour or two a day. The rest of the time, you're paying for unused capacity. For teams watching cloud spend, that adds up quickly.
In many setups, especially with managed services like RDS, replicas must be provisioned to match the size of the primary, even when the workload doesn’t demand it. You end up paying for the full instance, even if your read workload only needs a fraction of that compute.
Adding a new replica typically means creating a snapshot of the primary, restoring it, and waiting for replication to catch up. For large databases, this process can take significant time. It’s not something you can rely on when traffic spikes unexpectedly.
Many teams use tools like PgBouncer or cloud-native proxies for connection pooling, but these solutions don’t handle read/write splitting on their own. Read/write splitting often means using more advanced tools like PgPool, or adding custom logic to the application.
Load balancing typically requires another layer, such as HAProxy in front of the pooler. It all ends up being a lot to manage just to route read queries.
Want to run a report or offload traffic from an internal tool? Traditional replicas don’t make this easy. There’s no lightweight way to scale up for a few hours and then scale back down. You’re locked into the same heavy provisioning process either way.
Even once replicas are running, they need attention. Teams monitor health and lag, update configurations, test failovers, and handle edge cases. The effort grows alongside your infrastructure.
The setup is easy. The ongoing maintenance? That's where it gets messy.
We stood up plenty of read scaling infrastructure at past startups, but it never felt like the best use of our time, and maintaining it was always a burden.
So we built something better.
Springtail provides elastic read replicas for Postgres that scale up or down instantly. There’s no need to change your application or primary database.
Instead of creating a full copy of your data for each replica, Springtail’s architecture uses a shared data layer across its nodes. This allows it to:
You keep your existing Postgres instance. Springtail connects via logical replication and handles the rest.
Springtail is designed to meet teams where they are.
Some teams use it early to avoid over-provisioning. Others bring it in after building internal tooling that becomes brittle or hard to maintain. Some just want an easier way to isolate analytics or internal traffic from the primary database.
What they all share is a need for read scaling without the operational drag.
That said, Springtail isn’t for everyone. Some teams prioritize fine-grained control or have strict infrastructure policies that require managing everything in-house. Springtail is a better fit for those that want elasticity, lower overhead, and fewer moving parts, especially in fast-growing or cloud-native environments.
We know adding new infrastructure can feel risky, and that’s why we made Springtail safe to adopt and easy to turn off.
No migration. No lock-in. Just a better replica layer, ready when you need it.
This isn’t about replacing Postgres. It’s about rethinking a pattern that no longer fits how modern infrastructure grows and adapts.
If you're scaling Postgres and tired of wrangling replicas, we’d love to show you what we’ve built.
Explore our docs or request access to get started.
