PGlite and SQLite-vec take different approaches to vector search in the browser. PGlite brings Postgres features and indexed search, while SQLite-vec keeps the runtime and data model closer to SQLite.

Short answer: Choose PGlite when Postgres compatibility and indexed search matter. Choose SQLite-vec when a smaller SQLite-based runtime and flat or quantized search fit the dataset.

PGlite wins on index-build stability for massive sets, while SQLite-vec wins on low-latency startup and memory efficiency.

The architectural shift to the browser

Local-first software requires moving the database to the user's device. For years, that meant trading away the advanced retrieval capabilities of backend systems like pgvector or Pinecone.

The arrival of high-performance WebAssembly (WASM) builds for PGlite and SQLite-vec has erased that trade-off.

Local retrieval can remove a network round trip from an interactive search path.

Moving retrieval into the browser removes the first half of that latency chain entirely, and the box starts keeping up with the typist.

Local-first WASM lifecycle: the loading sequence

Implementing vector search in WASM means juggling three distinct environments: the main thread, the Web Worker, and the WASM memory heap. PGlite runs a full Postgres instance inside that heap.

SQLite-vec acts as a highly optimized C extension bolted onto the SQLite engine, closer to a single sharp tool than a whole workshop.

What a reproducible comparison should measure

No benchmark artifact accompanies this article, so it does not report measured results. A reproducible comparison should report bundle size, index-build time, query latency distributions, memory use, and recall.

SQLite-vec loads as an extension, while PGlite starts a Postgres runtime inside the WebAssembly sandbox. That architectural difference affects startup work and feature coverage.

PGlite and the power of HNSW

PGlite brings the industry-standard pgvector implementation to the edge. Its primary advantage is the HNSW (Hierarchical Navigable Small World) index.

HNSW is a graph-based index for approximate nearest-neighbor search, using graph navigation instead of comparing the query with every stored vector.

Search complexity: flat vs indexing

The graph works like the express layers of a subway map: instead of stopping at every station, a query hops along sparse long-distance edges first, then drops down to local stops only near the answer.

SQLite-vec and the speed of quantization

SQLite-vec bets on a different idea. Rather than building complex graph indexes, it optimizes for extremely fast brute-force scans through vector quantization.

Quantization shaves the precision off each dimension to save memory and speed up the arithmetic.

Binary quantization: mapping and reduction

Binary quantization is the most aggressive form of this optimization. Each 32-bit floating point dimension collapses to a single bit based on whether the value is positive or negative, like throwing away the exact GPS coordinates and keeping only which side of the street something sits on.

That collapse buys a 32x reduction in memory footprint and lets the CPU swap expensive floating-point dot products for XOR-based Hamming distance, which counts mismatched bits in a single cheap instruction.

## The precision tax: accuracy vs speed Nothing in AI engineering comes free. Quantization buys speed and pays for it with a recall penalty, so the results drift slightly away from the true nearest neighbors. A query that should surface the three most relevant support docs might return two of them plus a near-miss fourth. ## Dimensionality and the scaling wall As vector dimensionality climbs, the performance gulf between these engines widens. Standard open-source models like `all-MiniLM` use 384 dimensions. OpenAI's [`text-embedding-3-large`](https://developers.openai.com/api/docs/models/text-embedding-3-large) uses 3,072 dimensions by default, a jump rooted in [how embedding models trade dimensionality for information density](/articles/embedding-models-compared/). A flat scan does proportionally more arithmetic as vector dimension grows. Benchmark the crossover against the target browser, vector count, and recall requirement. PGlite's HNSW index stays relatively flat, because the graph lets a query skip most vectors no matter how wide each vector is. Dimensionality stretches the cost of comparing two vectors, and HNSW simply compares far fewer of them. ## Memory constraints in the WASM heap Browser and WebAssembly memory limits constrain how large an in-memory vector index can grow, and those limits vary by runtime. HNSW indexes store both vectors and graph edges, while flat quantized indexes can use less memory. Measure heap use in the target browser instead of assuming a universal footprint. ## Economic outcomes of edge retrieval Moving retrieval to the client can reduce per-query backend compute, but it shifts compute and memory cost to the user's device. Your retrieval compute budget quietly migrates off the AWS bill and onto the user's electricity bill. ## Choosing the right architecture Picking between these two engines comes down to your application's data model and scale.
Architectural decision matrix
PGlite is the correct choice for applications that need a real relational database. If your search results must be joined with complex metadata, filtered via JSONB, or [combined with BM25 full-text search in a hybrid retrieval setup](/articles/hybrid-search-bm25-vector-search/), or synced with a backend Postgres instance, the PGlite bundle size is a small price to pay. A local documentation assistant with a bounded corpus can fit SQLite-vec's simpler deployment model. ## The future of edge retrieval As models become practical on more local hardware, retrieval can move into the same client-side application. Shipping either PGlite or SQLite-vec pulls the biggest friction point out of the user's loop. Seasoned practitioners already know the fastest request is the one that never leaves the machine. Local-first vector databases have graduated from a nice optimization to the new baseline for responsive, private, and scalable AI software. ## FAQ **Can I run PGlite and SQLite-vec in the same application?** Yes. Some teams run SQLite-vec for a fast, low-precision "first pass" search, then hand the candidates to PGlite for a high-precision "refinement" pass. A two-stage design can show approximate candidates first and rerank them with a higher-precision index before settling the result set. **Do these databases work in mobile browsers?** Yes. Both run in iOS Safari and Android Chrome via WASM. SQLite-vec is particularly well-suited for mobile due to its low memory footprint and efficient use of CPU registers for binary math. **What is the "lost in the middle" problem for local RAG?** That one is a [context window limitation rather than a database limitation](/articles/llm-context-windows-explained/). Even with flawless retrieval, packing too much context into a small local model degrades its reasoning, since the relevant passage gets buried among the filler. Choose top-K from retrieval and answer-quality evals for the local model rather than copying a cloud pipeline's setting.