There is no single best database for every AI application. Choose SQLite when data belongs close to the application and its write pattern fits an embedded database; consider Turso when its SQLite-compatible, hosted or self-hosted features suit your deployment; and choose PostgreSQL when a shared client-server database fits your application’s needs. Vector search alone does not settle the choice: all three approaches can support it, though the implementation differs.
How the three databases differ
| Option | Operating model | Write behavior | Vector-search path |
|---|---|---|---|
| SQLite | Embedded database, commonly used as a local file | In WAL mode, readers can work alongside a writer, but only one writer can write at a time. | Extensions or other components; check compatibility with the chosen build and deployment. |
| Turso | SQLite-compatible database with managed and self-hosted options, as described by Turso | Turso describes its system as supporting concurrent writes using MVCC; verify the behavior and limits for the version and service you plan to use. | Turso describes vector search as a product feature; check current implementation and compatibility. |
| PostgreSQL | Client-server database; hosting topology depends on how it is deployed | Its documentation describes MVCC, a concurrency-control approach that lets database sessions access data while helping maintain transactional consistency. | The open-source pgvector extension adds vector similarity search. |
SQLite’s appropriate-use guidance frames it as a deployment choice, not a database that is inherently unsuitable for serious applications. Its documentation also covers SQL capabilities including JSON functions and FTS5 full-text search.
When SQLite is a good fit
SQLite is a strong candidate when the application can keep its database local, an embedded database simplifies deployment, and the workload does not require multiple simultaneous writers to the same database. That can suit an AI feature that stores application-local state, documents, or other data without needing a separate database server.
Understand the WAL limit before choosing it
Write-Ahead Logging (WAL) lets readers and a writer operate at the same time, but it does not allow multiple writers at once: a WAL database has only one writer at a time. SQLite’s WAL documentation also says readers must be on the same machine because WAL relies on shared memory. That makes WAL a poor fit for treating one database file as a shared network database across machines.
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Assess where the database file will live, how the application will back it up, and whether any required extensions are supported in the target build. If writes might contend, test the application’s actual write pattern rather than assuming the embedded model will behave like a multi-writer server.
When Turso may fit
Turso describes its product as an open-source, SQLite-compatible database and offers managed and self-hosted forms. Its product overview also describes replication, concurrent writes, and vector search, and positions the system for local-first, edge, and per-tenant workloads. Those are vendor-described capabilities, not independent guarantees of latency, throughput, durability, or compatibility. See Turso’s product overview.
Turso is worth evaluating when you want a SQLite-compatible approach but need a hosted or distributed service model, or when its stated features align with your deployment. Before building around it, check the compatibility details for your specific SQLite SQL, APIs, extensions, and application libraries. Also verify how replication behaves for your consistency needs and what current service plans permit; compatibility and service terms can vary by version and change over time.
When PostgreSQL may fit
PostgreSQL is a client-server database, making it a natural candidate when an application needs a shared database service rather than an embedded file. Its official MVCC introduction explains its concurrency model. The right deployment still depends on how the database is hosted and operated, as well as the application’s schema and workload.
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If the AI application needs vector similarity search, PostgreSQL can use pgvector, an open-source extension. That means “we need vectors” is not, by itself, a reason to choose Turso over PostgreSQL or vice versa. Compare the retrieval features and operational trade-offs you actually need, including the vector index and query behavior you intend to use.
Choose by deployment and workload, not a speed claim
No head-to-head benchmark for a representative AI application workload is established here, so there is no supported universal speed or cost winner. Compare the options under your own data, queries, write pattern, deployment geography, and operational constraints.
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- Deployment topology: Do you want an embedded database file, a managed or self-hosted SQLite-compatible service, or a client-server PostgreSQL deployment?
- Writers and locations: How many application instances may write at once, and are they on one machine or distributed across regions?
- Offline and local behavior: Must the application continue working locally, or is a shared service the better fit?
- Vector retrieval: Which vector-search implementation and index fit the retrieval workload? Compare the actual features rather than treating vector support as a yes-or-no differentiator.
- Operations: Who owns backups, upgrades, service availability, replication behavior, and troubleshooting?
- Cost: Review current pricing and estimate it against expected storage, traffic, compute, and operational needs; do not infer cost from product category alone.
Run a workload-specific proof of concept
Before committing, build a small proof of concept against the same representative data and application flow for each serious candidate. Include ordinary reads and writes, the expected number of concurrent writers, any vector retrieval, and the deployment arrangement you plan to use. Record the results and operational work needed, then check current pricing, version-specific compatibility, and service terms before making the production choice.
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