Data for Multi-platform

The all-in-one convenience creates dependency. Complex queries hit performance limits. Self-hosting requires significant operational knowledge.

SQLite limitations still apply—no concurrent writes from different regions. The embedded replica model has a learning curve. Less battle-tested than Postgres.

Memory-bound storage is expensive at scale. Persistence options have tradeoffs. The licensing changes caused uncertainty. Clustering adds complexity.

HTTP adds latency compared to native Redis protocol. High-throughput workloads get expensive. Some Redis features are not available.

Vendor lock-in for a core AI capability. Costs scale with vector count and queries. Self-hosting is not an option.

Operational burden of self-hosting. Smaller ecosystem than Pinecone. Some advanced features require understanding internals.

Not a general-purpose database—updates and deletes are expensive. Operational complexity for self-hosting. The learning curve for optimal schema design is steep.

Adds complexity to Postgres operations. Some hypertable features have limitations. The learning curve for optimal performance is real.

Not for OLTP workloads—it is analytics-focused. Single-node limits apply. Less ecosystem than traditional databases.

Single writer at a time limits write concurrency. Not designed for network access. Feature set is smaller than Postgres.

Codegen from SQL - not Prisma/Drizzle ORM, not a database. Schema migrations are still yours (golang-migrate, Atlas, etc.). Language support depth varies.