SQLite Alternatives: When to Choose Something Else

SQLite is one of the most deployed databases in the world. It's in your phone, your browser, your TV, probably your car. For many applications, it's the perfect choice. But it's not the right choice for everything.

Understanding when SQLite falls short helps you pick the right alternative. This isn't about SQLite being bad. It's about using the right tool for the job.

When SQLite Works Perfectly

Before discussing alternatives, let's be clear about SQLite's strengths:

• Embedded databases: Applications that need local storage
• Single-user applications: Desktop apps, mobile apps, CLI tools
• Development and testing: Fast, file-based, zero configuration
• Read-heavy workloads: Excellent read performance
• Small to medium datasets: Handles gigabytes comfortably

If your use case fits these patterns, SQLite is probably the right choice. Don't overcomplicate things.

When You Need an Alternative

Problem: Multiple Writers

SQLite uses a single-writer model. Only one process can write at a time, and writers block readers (unless you use WAL mode, which helps but doesn't fully solve the problem).

Signs you've hit this limit:

• Write operations queue up
• SQLITE_BUSY errors appear
• Performance degrades under concurrent writes

Alternatives:

• PostgreSQL: Multiple connections can write simultaneously
• MySQL: Excellent concurrent write handling
• Turso/libSQL: SQLite-based but with better replication

Problem: Multiple Servers

SQLite is a file-based database. Multiple application servers can't safely access the same SQLite file over a network (NFS/SMB will corrupt your data eventually).

Signs you've hit this limit:

• You need to scale horizontally
• Multiple services need database access
• You're moving to containers/Kubernetes

Alternatives:

• PostgreSQL: Client-server architecture, handles many connections
• MySQL: Same, designed for network access
• CockroachDB: Distributed SQL, scales horizontally

Problem: High Write Throughput

SQLite's single-writer model limits write throughput. Even with WAL mode, you'll max out around thousands of writes per second on good hardware, impressive for an embedded database, but limiting at scale.

Signs you've hit this limit:

• Write latency increases under load
• Transaction conflicts
• Benchmarks show write bottlenecks

Alternatives:

• PostgreSQL: Handles tens of thousands of TPS
• MySQL: Similar write throughput
• Cassandra/ScyllaDB: Optimized for extreme write throughput

Problem: Large Datasets

SQLite handles gigabytes well. Terabytes? That's pushing it. Performance degrades, and maintenance becomes challenging.

Signs you've hit this limit:

• Database file exceeds 100 GB
• VACUUM takes hours
• Index operations are painfully slow

Alternatives:

• PostgreSQL: Handles terabytes efficiently
• ClickHouse: For analytical workloads on large datasets
• BigQuery/Snowflake: For truly massive analytical data

Problem: Complex Transactions

SQLite has ACID transactions, but they're database-wide locks. Complex transactions with many reads and writes can block the entire database.

Signs you've hit this limit:

• Long-running transactions block everything
• Deadlocks in application logic
• Need for row-level locking

Alternatives:

• PostgreSQL: Row-level locking, MVCC
• MySQL (InnoDB): Row-level locking
• Any client-server database: Better transaction isolation

The Alternatives

PostgreSQL

Best for: General-purpose replacement

PostgreSQL is the most common SQLite "upgrade path." It's open source, feature-rich, and handles most workloads well.

Why choose it:

• Excellent concurrent access
• Rich feature set (JSON, full-text search, etc.)
• Strong ecosystem and tooling
• Handles scale well

Trade-offs:

• Requires server setup/management
• More complex than SQLite
• Overhead for simple use cases

MySQL/MariaDB

Best for: Web applications, familiar ecosystem

MySQL is another solid general-purpose choice, especially if your team already knows it.

Why choose it:

• Proven at scale (Facebook, Twitter, etc.)
• Excellent tooling and hosting options
• Strong replication features

Trade-offs:

• Similar complexity to PostgreSQL
• Fewer advanced features than PostgreSQL

DuckDB

Best for: Analytical workloads on local data

DuckDB is "SQLite for analytics," an embedded database optimized for OLAP queries.

Why choose it:

• Columnar storage for fast analytics
• Embedded like SQLite (no server)
• Excellent Pandas/Arrow integration
• Handles larger datasets than SQLite for analytics

Trade-offs:

• Not designed for OLTP workloads
• Younger project, smaller ecosystem
• Write performance is not the focus

Turso / libSQL

Best for: SQLite with replication

Turso is built on libSQL, a SQLite fork that adds server capabilities and replication.

Why choose it:

• SQLite compatibility
• Edge replicas for global distribution
• Scale beyond single-server limitations
• Generous free tier

Trade-offs:

• Still newer technology
• Not all SQLite features may work
• Vendor dependency

Redis

Best for: Caching and session storage

If you're using SQLite for caching or session data, Redis is purpose-built for this.

Why choose it:

• In-memory speed
• Built-in expiration
• Data structures beyond key-value
• Pub/sub, streams, etc.

Trade-offs:

• Not a relational database
• Data durability requires configuration
• Memory-based (costs scale with data)

Firestore/MongoDB

Best for: Document-shaped data

If your data doesn't fit neatly into tables, document databases might be more natural.

Why choose them:

• Flexible schemas
• Nested data structures
• Good for content, catalogs, etc.

Trade-offs:

• No SQL (different query paradigm)
• Less suitable for relational data
• Can be expensive at scale

Migration Considerations

SQLite to PostgreSQL

Most straightforward migration path:

# Export from SQLite
sqlite3 mydb.db .dump > dump.sql

# Convert syntax differences
# - INTEGER PRIMARY KEY → SERIAL or GENERATED
# - Datetime handling differs
# - Boolean is a real type in PostgreSQL

# Import to PostgreSQL
psql mydb < dump.sql

Key differences to handle:

• Auto-increment syntax
• Date/time functions
• Boolean handling (SQLite uses 0/1)
• String concatenation (|| works in both)

SQLite to MySQL

Similar process, but more syntax differences:

• Backticks for identifiers in MySQL
• Different datetime functions
• AUTO_INCREMENT instead of AUTOINCREMENT

Tools like pgloader can help automate conversions.

Decision Framework

Stay with SQLite if:

• Single application, single server
• Read-heavy workload
• Data fits comfortably in memory
• Simplicity is a priority

Move to PostgreSQL/MySQL if:

• Multiple servers need database access
• Concurrent writes are important
• Data is growing beyond comfort
• You need advanced features

Consider DuckDB if:

• Analytical queries on local data
• Data science workflows
• Need embedded but analytical

Consider Turso if:

• Want SQLite but need replication
• Global edge distribution matters
• Comfortable with newer technology

The Honest Advice

Most developers move away from SQLite too early. The desire to use "real" databases leads to unnecessary complexity. A well-tuned SQLite database handles more than most people think.

But when you do hit SQLite's limits (concurrent writes, multiple servers, or scale), don't fight it. PostgreSQL is the safe default for most applications. It's what SQLite would be if SQLite needed to handle concurrent network access at scale.

The key is recognizing which limit you've actually hit, not which limit you might theoretically hit someday.