Postgres or SQLite for a new serverless SaaS in 2026 — which should I choose, and why?

I'll research this thoroughly for you.The research panel failed to return results, so I'll answer directly from my own knowledge. I'll be transparent: this is my reasoning rather than a synthesized multi-source analysis.

Answer

Default to Postgres. For a new serverless SaaS in 2026, Postgres (via a serverless-friendly provider like Neon, Supabase, or AWS Aurora Serverless v2) is the safer, more scalable choice for the large majority of cases. Choose SQLite (via Turso/libSQL, Cloudflare D1, or embedded-at-edge) only when your workload is read-heavy, latency-at-the-edge is the product, your data naturally partitions per-tenant, or you're optimizing aggressively for cost and simplicity at small-to-medium scale.

The deciding question isn't "which database is better" — it's "does my write/concurrency model fit SQLite's single-writer architecture, and do I need features SQLite lacks?" If you're unsure, Postgres rarely becomes the thing you regret.

Why Postgres for most serverless SaaS

• Concurrent writes. SaaS apps typically have many tenants writing simultaneously. Postgres uses MVCC and handles high write concurrency natively. SQLite is fundamentally single-writer per database (writes serialize), which becomes a bottleneck for write-heavy multi-tenant apps unless you shard per-tenant.
• Serverless cold-start and connection problems are now solved. The historical knock against Postgres in serverless (connection exhaustion, cold starts) is largely addressed by:
  • Neon / Supabase: separate storage from compute, scale-to-zero, built-in connection pooling (PgBouncer), and HTTP/serverless drivers.
  • Aurora Serverless v2: auto-scaling on AWS.
  • Prisma Accelerate, Hyperdrive (Cloudflare), RDS Proxy: connection pooling for ephemeral function environments.
• Richer feature set. JSONB, full-text search, pgvector for AI/embedding workloads (very relevant in 2026), strong types, advanced indexing (GIN/GiST/BRIN), window functions, CTEs, stored procedures, robust foreign keys, and a deep extension ecosystem.
• Ecosystem & hiring. More tooling, ORMs, observability, migration tools, and engineers who know it. Lower long-term operational risk.
• Scaling headroom. You can grow to large scale without an architectural rewrite — read replicas, partitioning, and managed scaling are well-trodden paths.

Why (and when) SQLite makes sense

SQLite has had a genuine renaissance for serverless/edge, mainly via Turso/libSQL, Cloudflare D1, and LiteFS/Litestream. It's a serious option when:

• Read-heavy workloads where reads vastly outnumber writes (content sites, dashboards, config-driven apps). SQLite reads are extremely fast.
• Edge latency is the product. Embedding the DB next to compute (or replicating to edge locations) gives sub-millisecond local reads — hard to beat for global low-latency reads.
• Per-tenant isolation fits naturally. A powerful pattern is database-per-tenant (one SQLite file per customer). Turso explicitly supports cheaply spinning up thousands/millions of small databases. This sidesteps the single-writer limit (each tenant writes to its own DB) and gives strong isolation, easy per