sqlite-vec alternatives

19 tools we tested head to head against sqlite-vec, ranked — and what each one actually does differently.

last reviewed 23 jul 2026 · from our best 20 vector databases ·list curated by Onur Ozcanx in

first — what you'd be leaving

sqlite-vec ranks #15 of 20 in our vector databases testing. vector search anywhere sqlite runs, including the browser.

65/100

unbeatable for on-device and edge retrieval — brute-force only, so know where the ceiling is, and know it's still alpha.

why people look for an alternative

−brute force only — no approximate index
−still alpha after a long pre-1.0 period
−degrades linearly past roughly a million vectors

stay with sqlite-vec if runs anywhere sqlite does, including browser wasm is the thing you care about most — nothing below beats it on that.

the short version

best alternativepgvectoralmost everyone — start here and migrate on a measured threshold, not a vendor's table92/100

pgvector

#1 in vector databases · the correct default for most teams, and it quietly got good

92/100

verdictfree, permissive, runs on every managed postgres, and the only option here that can update an embedding and its parent row in one transaction.

pgvector vs sqlite-vec

	sqlite-vec	pgvector
price	free — Apache-2.0	free — PostgreSQL License
free tier	yes	yes
license	Apache-2.0	PostgreSQL License — permissive
deployment	embedded, anywhere sqlite runs	extension on any postgres
index types	brute force only	hnsw, ivfflat, binary quantization
hybrid search	via sqlite fts	via postgres full-text; you write the fusion
pricing model	free	free — you pay for postgres

switch foralmost everyone — start here and migrate on a measured threshold, not a vendor's table

pros

+free and permissively licensed, on every managed postgres
+transactional consistency between embeddings and business rows
+0.8.0 iterative index scans fixed the filtered-recall flaw
+half-precision and binary quantization cut footprint dramatically

cons

−index builds are slow and lock-heavy at large scale
−no native distributed sharding
−you own all the tuning yourself

visit pgvector →full pgvector review →

Qdrant

#2 in vector databases · filtering built into the graph traversal, not bolted around it

90/100

verdictthe best dedicated option: apache-2.0 with no strings, the strongest filtered-search design in the category, and the easiest serious self-host.

Qdrant vs sqlite-vec

	sqlite-vec	Qdrant
price	free — Apache-2.0	free tier; cloud billed hourly on compute and storage
free tier	yes	yes
license	Apache-2.0	Apache-2.0
deployment	embedded, anywhere sqlite runs	self-host or managed cloud
index types	brute force only	hnsw + scalar/binary/product/turboquant
hybrid search	via sqlite fts	sparse vectors, native
pricing model	free	hourly compute + storage, rates unpublished

switch forteams who genuinely need a dedicated vector database and want to self-host it

pros

+filters applied inside hnsw traversal — best filtered search here
+apache-2.0 with no revenue caps or service restrictions
+four quantization modes including turboquant
+single rust binary — easiest serious self-host

cons

−cloud per-unit rates are not published
−hybrid and private cloud are quote-only
−smaller enterprise support footprint than the incumbents

visit Qdrant →full Qdrant review →

Milvus / Zilliz Cloud

#3 in vector databases · the credible answer at a billion vectors

88/100

verdictthe widest index selection anywhere and genuine billion-scale — and version 2.6 removed the external message queue that made it painful to run.

Milvus / Zilliz Cloud vs sqlite-vec

	sqlite-vec	Milvus / Zilliz Cloud
price	free — Apache-2.0	free self-hosted; zilliz cloud billed per compute unit
free tier	yes	yes
license	Apache-2.0	Apache-2.0
deployment	embedded, anywhere sqlite runs	self-host, lite, or zilliz cloud
index types	brute force only	hnsw, ivf, diskann, gpu cagra, scann, rabitq
hybrid search	via sqlite fts	full, with sparse indexes
pricing model	free	compute units; rates largely unpublished

switch for100m to 1bn+ vectors, where nothing else on this list is really appropriate

pros

+widest index selection: diskann, gpu cagra, rabitq, ivf family, sparse
+genuinely proven at a billion vectors and beyond
+2.6 removed the kafka/pulsar dependency
+apache-2.0 with over 100k collections for multi-tenancy

cons

−heaviest self-host here despite 2.6's improvements
−over-engineered below roughly 10m vectors
−the vendor also maintains the main public benchmark

visit Milvus / Zilliz Cloud →full Milvus / Zilliz Cloud review →

Elasticsearch

#4 in vector databases · the licence objection to using it for vectors is largely obsolete

85/100

verdictbest-in-class hybrid retrieval with transparent serverless pricing — and its dense and sparse vector code sits in the agpl-licensed tree, not the restricted one.

Elasticsearch vs sqlite-vec

	sqlite-vec	Elasticsearch
price	free — Apache-2.0	serverless from $0.09 per search VCU-hour; storage $0.047/GB/month
free tier	yes	yes
license	Apache-2.0	AGPLv3 / SSPLv1 / Elastic 2.0 triple
deployment	embedded, anywhere sqlite runs	self-host, cloud, serverless
index types	brute force only	lucene hnsw, int8/int4, bbq
hybrid search	via sqlite fts	best in class — bm25 + rrf
pricing model	free	vcu-hours + storage per gb

switch forteams already running elasticsearch, and anyone who needs real hybrid search

pros

+vector search is available under agplv3, not licence-restricted
+best-in-class hybrid with real bm25 and rank fusion
+transparent serverless pricing with 50gb free on vector profiles
+better binary quantization and mature filtering

cons

−jvm heap tuning and real operational weight
−licence structure is complex even where it's permissive
−the elser sparse model is separately restricted

visit Elasticsearch →full Elasticsearch review →

Weaviate

#5 in vector databases · multi-tenancy and hybrid search, priced per million dimensions

84/100

verdictthe strongest combination of hybrid search and per-tenant isolation here — with a pricing model that punishes large embeddings in a way no competitor's does.

Weaviate vs sqlite-vec

	sqlite-vec	Weaviate
price	free — Apache-2.0	$45 / month minimum; vector dimensions from $0.00465 per million
free tier	yes	yes
license	Apache-2.0	BSD-3-Clause
deployment	embedded, anywhere sqlite runs	self-host, cloud, or byoc
index types	brute force only	hnsw + product/binary/scalar quantization
hybrid search	via sqlite fts	first-class bm25 + dense fusion
pricing model	free	per million vector dimensions

switch formulti-tenant saas rag where each customer needs isolation

pros

+native multi-tenancy with isolated shards and cold offloading
+first-class hybrid search with configurable fusion
+bsd-3-clause — clean permissive licence
+excellent documentation

cons

−dimension-based pricing doubles when you double embedding size
−$45/month minimum on the entry cloud tier
−historically memory-hungry without quantization

visit Weaviate →full Weaviate review →

Chroma

#6 in vector databases · the best developer experience, and honest per-unit pricing

82/100

verdictpip install and go, with genuinely transparent pricing — the right call up to a few million vectors and the wrong one well before a hundred.

Chroma vs sqlite-vec

	sqlite-vec	Chroma
price	free — Apache-2.0	$0 base with usage-based billing; $0.33 per GiB-month storage
free tier	yes	yes
license	Apache-2.0	Apache-2.0
deployment	embedded, anywhere sqlite runs	embedded, self-host, or cloud
index types	brute force only	hnsw
hybrid search	via sqlite fts	metadata + full-text; weaker than rivals
pricing model	free	per gib stored, written and queried

switch forprototypes and small-to-mid production where developer speed matters most

pros

+best developer experience in the category
+genuinely transparent per-unit pricing with no base fee
+apache-2.0
+usage-based billing starts at zero

cons

−hnsw only — thinnest tuning surface here
−unproven above roughly 10m vectors
−local mode and cloud are different code paths

visit Chroma →full Chroma review →

AWS S3 Vectors

#7 in vector databases · storage at six cents a gigabyte, and it reprices the floor of the market

80/100

verdictroughly five times cheaper per gigabyte than the premium managed options, and explicitly built for cost rather than latency — choose it deliberately, not by default.

AWS S3 Vectors vs sqlite-vec

	sqlite-vec	AWS S3 Vectors
price	free — Apache-2.0	$0.06 per GB per month storage
free tier	yes	no
license	Apache-2.0	proprietary, managed only
deployment	embedded, anywhere sqlite runs	aws managed
index types	brute force only	not documented
hybrid search	via sqlite fts	not a strength
pricing model	free	per gb stored, per million queries

switch forlarge, cold, infrequently queried corpora where sub-second is acceptable

pros

+$0.06/GB/month — the cheapest credible storage here
+two billion vectors per index, 31 regions
+fully published, legible rate card
+first 512KB returned per query is free

cons

−sub-second latency — wrong for interactive agents
−128KB minimum per PUT punishes unbatched writes ~21×
−index algorithm and internals undocumented

visit AWS S3 Vectors →full AWS S3 Vectors review →

turbopuffer

#8 in vector databases · object-storage economics with the best namespace multi-tenancy

78/100

verdictthe architecture the rest of the category converged on, with genuinely good per-tenant isolation — behind unpublished unit rates and no self-host option.

turbopuffer vs sqlite-vec

	sqlite-vec	turbopuffer
price	free — Apache-2.0	$16 / month minimum usage
free tier	yes	no
license	Apache-2.0	proprietary, managed only
deployment	embedded, anywhere sqlite runs	managed; byoc at enterprise
index types	brute force only	not fully documented
hybrid search	via sqlite fts	supported
pricing model	free	usage, above plan minimums

switch formulti-tenant products with large, mostly-cold corpora

pros

+excellent economics on large, mostly-cold corpora
+namespace-per-tenant isolation is genuinely well designed
+low $16/month entry minimum
+well-regarded engineering team and public writing

cons

−per-unit rates are not published anywhere fetchable
−closed source with no self-host option
−no free tier, and higher tail latency by design

visit turbopuffer →full turbopuffer review →

OpenSearch

#9 in vector databases · apache-2.0 with no licence anxiety, and serverless finally scales to zero

76/100

verdictthe safe institutional choice on aws — genuinely apache-2.0, with faiss and lucene engines, and the always-on cost floor finally removed.

OpenSearch vs sqlite-vec

	sqlite-vec	OpenSearch
price	free — Apache-2.0	free self-hosted; serverless billed per OCU-hour
free tier	yes	yes
license	Apache-2.0	Apache-2.0
deployment	embedded, anywhere sqlite runs	self-host or aws managed/serverless
index types	brute force only	hnsw, ivf, faiss + lucene, disk modes
hybrid search	via sqlite fts	supported, mature
pricing model	free	ocu-hours; scales to zero on nextgen

switch foraws-committed teams who want vector search without licence questions

pros

+apache-2.0 throughout with no licence ambiguity
+faiss and lucene engines, plus disk-based and quantized modes
+serverless nextgen scales to zero after 10 minutes
+native on aws with full hybrid search

cons

−vector collections can't share compute units with other workloads
−forked-project ergonomics lag elasticsearch
−compute-unit accounting is confusing

visit OpenSearch →full OpenSearch review →

Vespa

#10 in vector databases · the most sophisticated ranking engine here, with the steepest learning curve

75/100

verdicta full search and ranking engine rather than a vector store — if ranking quality is your product, rank it second; if you just need rag, it's overkill.

Vespa vs sqlite-vec

	sqlite-vec	Vespa
price	free — Apache-2.0	$0.05 per vCPU-hour on the startup plan
free tier	yes	no
license	Apache-2.0	Apache-2.0
deployment	embedded, anywhere sqlite runs	self-host or vespa cloud
index types	brute force only	hnsw + tensor ranking framework
hybrid search	via sqlite fts	best in class, multi-phase ranking
pricing model	free	per vcpu / gb / gpu hour

switch forproducts where retrieval and ranking quality is the differentiator

pros

+unmatched ranking and hybrid sophistication
+most transparent resource pricing in the category
+apache-2.0 and proven at very large scale
+unit prices decline as allocation grows

cons

−steepest learning curve here by a distance
−$20,000/month minimum on the enterprise tier
−overkill for straightforward rag

visit Vespa →full Vespa review →

+ 9 more tested, not detailed here

we ranked 20 vector databases in total. the 9 that didn't make this page are written up in the full ranking →

how these were compared

every tool on this page went through the same test as sqlite-vec — same tasks, same order, scored the same way. the comparison tables are the figures from that testing, not vendor spec sheets.

the vector databases test in full →

was this useful?