Economics of PlanetScale, TiDB, and Aurora in branch-based deployment

Workflow and schema management

Teams prioritize branching to isolate schema changes from production traffic. PlanetScale provides development branches that copy only the schema, though the Data Branching feature can include data. You can create a deploy request to merge these changes into production. To create a branch, you click the New branch button on the dashboard, name it, and select the base branch. Amazon Aurora is a cloud-native relational database service that replicates data across storage nodes to provide fast read and write access. It provides 15 replicas per primary instance across different availability zones. Aurora storage scales automatically in 10GB increments up to 128TB without manual intervention. However, Aurora lacks native tooling for schema changes and does not support online schema changes during production. Users must use Aurora Backtrack to recover from any DDL operations that introduce breaking changes. TiDB Serverless is a MySQL-compatible serverless database system that shields developers from details like sharding and replication. It provides vector search for building graph-based RAG and AI-assisted SQL query editing. TiDB also provides database branching for rapid development. PlanetScale focuses on core MySQL functionality but lacks AI-driven enhancements. If you are choosing between these architectures, how much operational risk can your team handle during a deployment?

Pricing models and capacity

PlanetScale uses a model where you reserve resources like RAM and CPU for Pro Scaler plans starting at $39 per month. For variable workloads, TiDB Serverless uses a pay-as-you-go model and charges based on actual usage like writes, reads, and CPU time. Amazon Aurora presents different complexity with its Standard and I/O-Optimized tiers. Aurora I/O-Optimized removes separate I/O charges and helps when I/O costs exceed 25% of the total database spend. In one scenario, Aurora I/O-Optimized for an education system resulted in a cost of $136 per month, whereas PlanetScale offered a flat rate of $99. For a distribution center with 8 GB of data and 20 GB of backup data, the Aurora On-Demand I/O Optimized configuration with a db.r7g.xlarge instance costs $571 per month, while the PlanetScale Scaler Pro PS-320 configuration reaches $699 per month.

While Aurora Standard charges separately for storage and I/O requests, Aurora I/O-Optimized removes I/O charges for qualifying I/O-intensive estates. PlanetScale plans tend to be cheaper on infrastructure for many workloads because it uses commodity-grade instances to right-size resources for the user at a lower cost.

The scaling verdict

Scaling decisions depend on whether your application requires horizontal sharding or vertical scaling to meet the specific needs of your workload. PlanetScale uses Vitess to shard data across nodes, which prevents the need to rewrite application code as you grow. Aurora scales reads by adding replicas and scales writes through its distributed storage layer. PlanetScale Pro Scaler is limited to a single cloud region, which makes it less reliable for multi-region needs than TiDB Serverless or Aurora. You should realize that PlanetScale is cheaper on infrastructure for many workloads because it uses commodity-grade instances to right-size resources. Because PlanetScale includes two read replicas in its pricing model, it provides a significant advantage over Aurora when you need to add multiple replicas to handle heavy read workloads or provide necessary high availability for all your critical production traffic. When a distribution center requires a primary instance and two read replicas to handle heavy loads, the Aurora cost reaches $1621, whereas PlanetScale stays at $699, which results in a massive 132% savings for the PlanetScale option. Avoid Aurora if your budget cannot accommodate the massive price jump when adding replicas for high availability.