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PlanetScale vs Turso: Serverless MySQL Showdown
HOW TO GUIDES Dec. 30, 2025, 5:30 p.m.

PlanetScale vs Turso: Serverless MySQL Showdown

When you’re building a modern web app, the database layer often decides whether you’ll scale gracefully or hit a wall at the first traffic spike. Two names keep surfacing in the server‑less MySQL conversation: PlanetScale and Turso. Both promise “pay‑as‑you‑go” elasticity, but they arrive from very different design philosophies. In this deep dive we’ll compare their core architectures, performance quirks, pricing models, and real‑world migration stories, then hand you a few ready‑to‑run Python snippets so you can start experimenting right away.

What’s the “Serverless MySQL” Promise?

Serverless databases aim to hide the underlying servers, auto‑scale compute, and let you focus on SQL instead of ops. In practice, this means you get a connection endpoint that can handle thousands of concurrent requests without manually provisioning replicas or tuning buffers. The “MySQL” tag is a compatibility layer—most providers actually run a custom engine under the hood and translate MySQL wire‑protocol calls.

Because the abstraction is so high‑level, you often trade raw control for convenience. Understanding where PlanetScale and Turso diverge on that trade‑off is essential before you lock in a vendor.

PlanetScale: MySQL‑compatible, Built on Vitess

PlanetScale is a commercial offering built on Vitess, the open‑source sharding framework originally created at YouTube. Vitess splits a logical database into many physical shards, each backed by a MySQL instance. PlanetScale adds a fully managed control plane, automated schema migrations, and a “branch‑and‑merge” workflow that feels like Git for databases.

Key takeaways:

  • Branching: Create isolated development branches, run migrations, test, then merge without downtime.
  • Horizontal scaling: Sharding is automatic; you can add shards on the fly.
  • Strong consistency: Writes go to the primary shard, reads can be served from replicas with eventual consistency guarantees.

How PlanetScale Handles Connections

PlanetScale uses a proxy called pscale that multiplexes client connections over a single TLS tunnel. The proxy buffers queries, routes them to the correct shard, and retries transient failures. From a developer’s perspective, you connect just like you would to any MySQL server.

import pymysql

# Replace with your PlanetScale connection string
conn = pymysql.connect(
    host="aws.connect.psdb.cloud",
    user="my_user",
    password="my_password",
    database="my_db",
    ssl={"ca": "/path/to/ca.pem"}
)

with conn.cursor() as cur:
    cur.execute("SELECT NOW()")
    print("Server time:", cur.fetchone()[0])

This snippet works out of the box on any environment that can reach the PlanetScale endpoint—local dev, CI pipelines, or serverless functions.

Turso: Serverless SQLite with MySQL Compatibility

Turso, launched by the creators of the libSQL engine, markets itself as “serverless SQLite.” It’s a lightweight, file‑based database that runs in the cloud, automatically replicates across regions, and offers a MySQL‑compatible wire protocol via the libsql driver.

While SQLite isn’t a traditional multi‑node system, Turso adds a distributed replication layer that gives you low‑latency reads globally and writes that are coordinated through a consensus algorithm (Raft). The result is a “best‑of‑both‑worlds” experience: the simplicity of SQLite with the scalability of a cloud service.

Connecting to Turso from Python

The official Python client abstracts the details. You provide a URL that includes your auth token, and the library handles connection pooling for you.

import turso

# URL format: libsql://?authToken=
db = turso.connect("libsql://mydb?authToken=YOUR_TOKEN")

with db.cursor() as cur:
    cur.execute("SELECT datetime('now')")
    print("Server time:", cur.fetchone()[0])

Notice the SQL syntax is SQLite‑flavored (e.g., datetime('now')) but you can still run most MySQL‑style queries thanks to the compatibility shim.

Performance Benchmarks: Latency & Throughput

Both platforms claim sub‑millisecond latency for reads when the client is in the same region. In practice, the numbers differ based on workload type.

  • PlanetScale: Because each shard is a full MySQL instance, write throughput scales with the number of shards. However, the proxy adds a ~2‑3 ms overhead on the first hop.
  • Turso: Reads are served from the nearest replica, often under 1 ms. Writes require a Raft round‑trip, which can add 5‑10 ms latency, especially on cross‑region writes.

For read‑heavy workloads (e.g., serving product catalogs or session data), Turso often wins on raw latency. For write‑intensive use cases (e.g., event logging, financial transactions), PlanetScale’s sharding can sustain higher QPS.

Pricing Models: Pay‑As‑You‑Go vs Fixed Plans

Understanding the cost structure early can save you from surprise bills once traffic spikes.

PlanetScale Pricing

  • Free tier: 5 GB storage, 5 M queries/month, 1 branch.
  • Pro tier: $49/month for 50 GB, unlimited branches, and SLA‑backed uptime.
  • Enterprise: Custom pricing, dedicated VPC, and advanced security.

Charges are primarily based on storage and the number of “branches” (each branch consumes its own shard resources). Heavy write workloads can push you into higher tiers quickly.

Turso Pricing

  • Free tier: 1 GB storage, 10 M reads, 1 M writes/month.
  • Pay‑as‑you‑go: $0.15 per GB‑month, $0.10 per million reads, $0.20 per million writes.
  • Team plans: Flat‑rate bundles with additional analytics and backup retention.

Turso’s model is more granular; you only pay for the exact number of reads/writes you issue. This can be cheaper for bursty traffic patterns but may become costly for sustained high‑throughput writes.

Migration Paths: Moving Data In & Out

Switching databases is rarely a one‑click operation. Both providers supply tools to help you migrate, but the steps differ.

PlanetScale Migration

  1. Export your existing MySQL dump using mysqldump.
  2. Create a new PlanetScale database and a “main” branch.
  3. Run pscale deploy-request create to import the dump into a temporary branch.
  4. Validate with pscale branch diff and merge once tests pass.

The branch‑based workflow means you can test migrations in isolation without affecting production traffic.

Turso Migration

  1. Export data to CSV or SQLite format.
  2. Use the turso import CLI to load the file into a new Turso database.
  3. Run a compatibility test suite (the provider offers a turso validate command).
  4. Switch your application’s connection string to the new Turso URL.

Because Turso stores data in a single file per replica, the import process is typically faster for modest datasets, but you lose native MySQL features like stored procedures.

Pro tip: When migrating from MySQL to Turso, rewrite any ON UPDATE CURRENT_TIMESTAMP triggers as generated columns—SQLite does not support that trigger syntax.

Real‑World Use Cases

1. SaaS Startup – Feature Flag Service
A startup built a feature flag platform that needs to serve millions of reads per second with occasional writes when engineers toggle flags. They chose Turso for its global read replicas, achieving sub‑millisecond latency for edge‑deployed functions. Writes were batched during off‑peak hours to mitigate the Raft latency penalty.

2. E‑Commerce Platform – Order Management
An online store with high‑volume checkout flows opted for PlanetScale. Their order table required strong consistency and ACID guarantees. By leveraging PlanetScale’s automatic sharding, they scaled from 500 QPS to 15 k QPS without manual replica management, while the branch workflow allowed developers to test schema changes on a “staging” branch before merging to production.

3. Mobile Game – Leaderboards
A mobile game needed a lightweight backend that could be queried from client devices worldwide. Turso’s edge‑aware replicas let the game fetch the top‑10 scores in under 30 ms on average, improving player retention. The team used the SQLite‑style INSERT OR REPLACE syntax to keep leaderboard updates idempotent.

Feature Comparison Table

Feature PlanetScale Turso
Underlying Engine Vitess + MySQL SQLite + libSQL replication
Branching Workflow Git‑style branches, merge requests None (single main branch)
Horizontal Scaling Automatic sharding Global read replicas, Raft writes
Consistency Model Strong for writes, eventual for reads Strong writes (Raft), eventual reads
SQL Compatibility Full MySQL 8.0 SQLite dialect with MySQL wire‑protocol shim
Serverless Functions Integration Native support for Vercel, Netlify Edge‑ready, works with Cloudflare Workers
Backup & Restore Point‑in‑time backups, automated snapshots Daily snapshots, manual restore via CLI
Pricing Granularity Tiered storage & branch limits Pay‑as‑you‑go reads/writes

Best Practices for Production Deployments

Regardless of which service you pick, there are a handful of patterns that keep your database reliable and cost‑effective.

  • Connection Pooling: Serverless functions spin up quickly, but opening a new TCP connection for each invocation adds latency. Use a library like SQLAlchemy with a pool size of 5–10 for PlanetScale, and the Turso client’s built‑in pool for edge runtimes.
  • Read‑Replica Awareness: Direct read‑heavy queries to the nearest replica (PlanetScale’s read‑only endpoint or Turso’s region‑specific URL) to shave milliseconds off response times.
  • Idempotent Writes: Especially with Turso’s Raft consensus, retries can happen under the hood. Wrap inserts in INSERT ... ON CONFLICT (PostgreSQL style) or INSERT OR REPLACE to avoid duplicate rows.
  • Schema Evolution: Use PlanetScale’s branch workflow for zero‑downtime migrations. For Turso, stage migrations on a copy of the database, run a SELECT COUNT(*) sanity check, then swap the connection string.
Pro tip: When using PlanetScale with AWS Lambda, keep the Lambda warm for at least 5 minutes. Warm Lambdas reuse the underlying pscale proxy connection, cutting the connection‑setup time by up to 80%.

Hands‑On Example: Feature Flag Service with Turso

Below is a minimal Flask app that stores feature flags in Turso and serves them from the nearest edge region. The code demonstrates connection pooling, read‑replica routing, and an idempotent toggle endpoint.

from flask import Flask, request, jsonify
import turso

# Turso connection – automatically picks the closest replica
db = turso.connect("libsql://flags?authToken=YOUR_TOKEN")

app = Flask(__name__)

@app.route("/flags/<name>", methods=["GET"])
def get_flag(name):
    with db.cursor() as cur:
        cur.execute("SELECT enabled FROM flags WHERE name = ?", (name,))
        row = cur.fetchone()
        if row:
            return jsonify({"name": name, "enabled": bool(row[0])})
        return jsonify({"error": "flag not found"}), 404

@app.route("/flags/<name>", methods=["POST"])
def set_flag(name):
    enabled = request.json.get("enabled", False)
    with db.cursor() as cur:
        # INSERT OR REPLACE makes the operation idempotent
        cur.execute(
            "INSERT OR REPLACE INTO flags (name, enabled) VALUES (?, ?)",
            (name, int(enabled))
        )
        db.commit()
    return jsonify({"name": name, "enabled": enabled}), 200

if __name__ == "__main__":
    # Ensure the table exists
    with db.cursor() as cur:
        cur.execute(
            "CREATE TABLE IF NOT EXISTS flags ("
            "name TEXT PRIMARY KEY, "
            "enabled INTEGER NOT NULL)"
        )
        db.commit()
    app.run(host="0.0.0.0", port=8080)

Deploy this to Cloudflare Workers or Vercel Serverless Functions, and you’ll have a globally low‑latency feature flag service with virtually zero ops overhead.

Hands‑On Example: Order Service with PlanetScale

Next, a simple FastAPI endpoint that records orders in a PlanetScale database. The example showcases branch‑based migrations and a connection pool using aiomysql.

import os
import asyncio
from fastapi import FastAPI, HTTPException
import aiomysql

app = FastAPI()

# PlanetScale connection details – stored in env vars
PS_HOST = os.getenv("PS_HOST")
PS_USER = os.getenv("PS_USER")
PS_PASSWORD = os.getenv("PS_PASSWORD")
PS_DB = os.getenv("PS_DB")

async def get_pool():
    return await aiomysql.create_pool(
        host=PS_HOST,
        user=PS_USER,
        password=PS_PASSWORD,
        db=PS_DB,
        ssl={"ca": "/path/to/ca.pem"},
        minsize=5,
        maxsize=20,
    )

@app.on_event("startup")
async def startup():
    # Ensure the orders table exists on the main branch
    pool = await get_pool()
    async with pool.acquire() as conn:
        async with conn.cursor() as cur:
            await cur.execute(
                "CREATE TABLE IF NOT EXISTS orders ("
                "id BIGINT AUTO_INCREMENT PRIMARY KEY, "
                "user_id BIGINT NOT NULL, "
                "amount DECIMAL(10,2) NOT NULL, "
                "created_at TIMESTAMP DEFAULT
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