by Weaviate B.V. · founded 2019 · updated Sep 2026
Weaviate is an open-source vector database with built-in hybrid search, multi-tenancy and a module system that plugs into every major embedding provider. Run it yourself or use Weaviate Cloud; a strong middle path between control and convenience.
Vector DatabasesOpen SourceEstablishedFrom Free (self-hosted) / Cloud from $45 per month (Flex)
LangChainLlamaIndexOpenAI/Cohere/Hugging Face modulesKubernetesGraphQL & REST APIs
Strengths
+True open source with a first-class managed cloud
+Native hybrid (BM25 + vector) search and reranking
+Multi-tenancy designed for SaaS builders
Limitations
-Entry-level Cloud tier now starts at $45/mo after the $25 Serverless plan was retired
-GraphQL API adds a learning curve
-Resource-hungry at very large scale without tuning
Evidence behind this score
Each score is built from the claims below. This is a snapshot, so check the current terms with the vendor before you commit to anything.
3 of 5 claims below are past their re-check window. Pricing, availability and security details move fast, so confirm those with the vendor.
Claim
Source
Checked
BSD-3 licensed core with 12K+ GitHub stars· re-check
GitHub repository
Sep 2025
Native multi-tenancy supports millions of tenants per cluster· re-check
Weaviate documentation
Mar 2025
$50M Series B (2024) at a $200M valuation· re-check
Startup funding trackers
Nov 2024
The $25/mo Serverless tier was retired in October 2025; current Cloud tiers are Flex ($45/mo min, shared infra, 99.5% SLA), Plus ($280/mo annual, 99.9% SLA, SOC 2), and custom-priced Premium (BYOC, HIPAA-eligible)
Vector database market bifurcates: managed convenience vs. open performance
Funding and adoption data show Pinecone consolidating compliance-sensitive enterprise workloads while Qdrant and Weaviate grow fastest among self-hosting startups. Choose by ops capacity, not hype.
RAG quality studies highlight parsing as the biggest lever
Multiple evaluations found document parsing quality moves retrieval accuracy more than embedding model choice, validating investment in parsing layers like LlamaParse before swapping vector stores.