There is no universal winner. Choose SQLite when an embedded database file and application-local data fit your deployment; Turso when its SQLite-compatible approach and vendor-described managed, self-hosted, replication, or vector-search features fit; and PostgreSQL when your application needs a shared client-server database. The deciding questions are where data lives, how many writers must work at once, whether the app needs local or offline behavior, and how you plan to operate vector retrieval—not which database is supposedly fastest.
How the three database choices differ
| Database | Operating model | Writes and concurrency | Vector search | Best-fit starting point |
|---|---|---|---|---|
| SQLite | Embedded database file | In WAL mode, readers can run alongside a writer, but only one writer can write at a time. | May use extensions or other components; check build and deployment compatibility. | Applications that benefit from local data and a compact deployment. |
| Turso | SQLite-compatible database with managed and self-hosted options, according to Turso. | Turso describes its system as supporting concurrent writes using MVCC. | Turso describes vector search as a product feature. | Applications considering SQLite compatibility alongside hosted, replicated, edge, or per-tenant patterns. |
| PostgreSQL | Client-server database; hosting topology depends on the deployment. | PostgreSQL documentation covers its MVCC concurrency model. | The open-source pgvector extension adds vector similarity search. | Applications that need a shared database service and PostgreSQL’s transaction and concurrency model. |
The Turso capabilities in this comparison are vendor descriptions, not independent performance findings. Feature availability, behavior, compatibility, and service terms can depend on the version or offering. See Turso’s product overview, and verify the details for the version and deployment you intend to use.
When SQLite fits—and where its write limit matters
SQLite is embedded: an application uses a database file rather than necessarily relying on a separate database server. That can be a natural fit when data should stay close to the application, including local-first or single-machine workloads. “Embedded” does not mean featureless; SQLite documents SQL facilities including JSON functions and FTS5. Its appropriate-use guidance is useful for assessing deployment fit, and its documentation describes the available facilities.
What WAL does—and does not—allow
SQLite’s Write-Ahead Logging (WAL) mode allows readers to continue while a writer is active, but it does not permit multiple simultaneous writers: there can be only one writer at a time. WAL also uses shared memory, so its readers must be on the same machine as the database. These constraints matter if an AI application has multiple workers writing concurrently or if the database file would be shared across machines. Consult the SQLite WAL documentation when deciding how to place and access the file.
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Questions to check before choosing SQLite
- Can the write pattern tolerate a single writer at a time, including bursts from jobs, users, and agent processes?
- Will every WAL reader run on the same machine as the database?
- How will the application handle file placement, backups, and recovery?
- Do any required extensions work with the exact SQLite build and deployment target?
When Turso is worth evaluating
Turso describes itself as a SQLite-compatible database and offers managed and self-hosted forms. Its overview presents replication, concurrent writes, and vector search among its stated capabilities, and positions the product for edge, local-first, and per-tenant database patterns. Those features make Turso a candidate when you want to evaluate SQLite compatibility alongside a hosted or distributed service model; they do not establish a particular latency, throughput, durability, price, or compatibility outcome for your application.
Verify the details that affect your design
- SQL and API compatibility: Test the queries, types, transactions, and client interfaces your application actually uses rather than assuming every SQLite behavior is identical.
- Replication behavior: Establish how replication works for your chosen deployment, including the consistency behavior relevant to reads and writes.
- Operational model: Compare who manages upgrades, availability, backups, and incident response for managed versus self-hosted deployment.
- Current limits and terms: Check the applicable plan, service limits, and pricing at the time you choose; they can change.
When PostgreSQL fits—and how vectors fit in
PostgreSQL is a client-server database, making it a fit to evaluate when multiple application components need a shared database service. Its official documentation explains PostgreSQL’s MVCC model. For vector similarity search, the open-source pgvector extension provides a PostgreSQL-based option.
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Needing embeddings or vector retrieval does not by itself decide the database choice: Turso describes built-in vector search, and PostgreSQL can use pgvector; SQLite deployments may use extensions or other components. Compare the retrieval requirements, extension or service compatibility, and operational work for the specific implementation you would run.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose by deployment and workload, not a universal speed claim
No head-to-head benchmark for a representative AI-application workload establishes a universal performance or cost winner among these options. Instead, use the following checks to narrow the field, then validate the short list with a proof of concept that resembles your own workload.
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- Data placement: Is the database local to one application, distributed across edge locations, or a shared service for multiple components?
- Writers: How many processes or locations write at once, and what happens when writes arrive concurrently?
- Local or offline behavior: Must the application read or write data locally without depending on a remote database service?
- Vector retrieval: Which vector-search implementation meets the application’s requirements, and what does it add to deployment and operations?
- Operational ownership: Who handles backups, upgrades, availability, monitoring, and recovery?
- Cost under real use: Compare current service terms and the operating effort for your expected workload rather than inferring cost from product category or feature lists.
A useful proof of concept should exercise the expected read and write pattern, concurrent writers, vector queries if needed, and recovery or deployment workflow. Measure the outcomes that matter to the application and confirm the chosen product’s current compatibility and operating terms before committing.
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