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AI infrastructure · Tool
Weaviate
ResearchedWeaviate is an open-source, AI-native database platform unifying vector search, RAG, and memory. It offers built-in embeddings, a Query Agent, multimodal support, and over 20M downloads, serving startups through enterprises with deployment-agnostic scaling.
Official site snapshots
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Read-only public captures of Weaviate’s homepage. Screenshots are dated, never live embeds, and open full-screen.
In one minute
Start here for the decision-making essentials: what Weaviate does, who it is for, how it is accessed, and the first-party sources behind this profile.
Pricing5 options
Flex plan starts at $45/mo, pay-as-you-go, no commitment, with 99.5% uptime and standard s
Premium plan starts at $400/mo, prepaid contract, with up to 99.95% uptime and enterprise
Weaviate Query Agent: free to try with 1,000 requests/month
$30 per organization monthly
Weaviate Cloud is free to start across the entire product suite
Platforms
Shared CloudDedicated CloudAI database / Vector Database
API accessNot public
FoundedNot disclosed by source
AvailabilityWeb / remote
LicenseWeaviate is offered as an open-source platform.
Best suited to
Source-backed fit Teams building RAG applications on cloud infrastructure Startups, scale-ups, and enterprises needing AI-native vector search Large-scale multi-tenant production deployments Projects wanting built-in embeddings without external pipelines Multimodal AI across text, images, audio, and video Used by leading startups, scale-ups, and enterprises; thousands of customers.
Decision support
6 sourced answersCommon questions and adoption checks
Short answers to the questions buyers and builders commonly ask about Weaviate. Each answer cites the shared ledger below, where every source is listed once.
01What does Weaviate say it can do?
Vector Database: store, index, and search high-dimensional vectors at any scale. · Query Agent translates natural-language intent into optimized database queries automatical · Built-in vector generation (embeddings) from text, images, and more; no external embedding · Engram creates personalized AI experiences that learn and adapt to each user over time.
Vector Database Store, index, and search high-dimensional vectors at any scale.
02Who is Weaviate intended for?
Used by leading startups, scale-ups, and enterprises; thousands of customers.
Weaviate is a core piece of the stack for leading startups, scale-ups, and enterprises.
Ledger citation[1] weaviate.io
03What use cases does Weaviate describe?
Large-scale multi-tenant search use cases, with cited deployments storing 50K+ tenants in · Flex tier is positioned for prototypes, pilots, and small use cases with a zero-commitment · Premium tier is for teams scaling AI in production who need predictable pricing and enhanc · Building RAG applications on Google Cloud
50K+ Tenants stored in a single cluster
04What should teams verify before adopting Weaviate?
The Weaviate endpoint must be accessible for Vertex AI RAG Engine integration · Availability guarantees exclude planned maintenance windows (announced at least one week i
The only requirement is that the Weaviate endpoint is accessible.
05What pricing information is available for Weaviate?
Flex plan starts at $45/mo, pay-as-you-go, no commitment, with 99.5% uptime and standard s · Premium plan starts at $400/mo, prepaid contract, with up to 99.95% uptime and enterprise · Weaviate Query Agent: free to try with 1,000 requests/month; $30 per organization monthly · Weaviate Cloud is free to start across the entire product suite
Flex Starts at $45 /mo Pay-as-you-go
06Does Weaviate document API access?
Weaviate cluster connects via cluster API key
You can connect your Weaviate cluster to the RAG Engine by passing in your Weaviate cluster API key.
Ledger citation[2] weaviate.io/blog/google-rag-api
Decision guide
AI infrastructureCapabilities and operating fit
This profile connects the jobs Weaviate is described as handling with its delivery model, access options and the subjects used to match it to related products in this directory.
Common use cases
- Vector and hybrid search retrieval at scale
- Large-scale multi-tenant deployments with 50K+ tenants in a single cluster
- Building RAG applications on Google Cloud via Vertex AI RAG Engine
- Multimodal RAG with text, images, audio, and video in native formats
- Prototypes, pilots, and small use cases on Flex tier
- Production AI scaling with predictable pricing on Premium tier
Topics mapped
Verified capabilities
- Vector Database: store, index, and search high-dimensional vectors at any scale.
- Query Agent translates natural-language intent into optimized database queries automatical
- Built-in vector generation (embeddings) from text, images, and more; no external embedding
- Engram creates personalized AI experiences that learn and adapt to each user over time.
- Weaviate Embeddings: hosted embedding models with pay-as-you-go GPU-powered access, includ
- Weaviate Cloud includes native AI services (Embeddings and Query Agent) on every plan, bil
- Supports both vector and hybrid search retrieval
- Supports multiple index types including vector and inverted index
Recorded integrations
Integrated with Google Cloud Vertex AI RAG EngineCan integrate with first-party and third-party language modelsWeaviate integrates with Google's Gemini for multimodal RAG implementations.Google Cloud Platform (GCP)Amazon Web Services (AWS)Microsoft AzureFirecrawlContext Data VectorETLContextual AIAryn
Access signals
- Pricing model
- Flex plan starts at $45/mo, pay-as-you-go, no commitment, with 99.5% uptime and standard s · Premium plan starts at $400/mo, prepaid contract, with up to 99.95% uptime and enterprise · Weaviate Query Agent: free to try with 1,000 requests/month; $30 per organization monthly · Weaviate Cloud is free to start across the entire product suite
- API
- Not publicly listed
- Source links
- 17 recorded
Source-backed
Updated July 22, 2026Verified facts
Each fact points to a recorded source, making it easy to distinguish verified product information from claims that need checking.
The AI database developers love | Weaviate
AI-native database platform combining vector search, RAG, and memory · Open-source platform · Over 20M open source downloads
Weaviate is an AI-native database platform combining vector search, RAG, and memory in one open-source product.
Vector Database: store, index, and search high-dimensional vectors at any scale.
Query Agent translates natural-language intent into optimized database queries automatically.
Built-in vector generation (embeddings) from text, images, and more; no external embedding pipeline required.
Engram creates personalized AI experiences that learn and adapt to each user over time.
View 32 more verified facts
Weaviate is offered as an open-source platform.
Weaviate is deployment-agnostic and open source.
Over 20 million open-source downloads.
Used by leading startups, scale-ups, and enterprises; thousands of customers.
Large-scale multi-tenant search use cases, with cited deployments storing 50K+ tenants in a single cluster and 42M+ vectors in production.
Weaviate is described as battle-tested and used in banking where security is important.
Flex plan starts at $45/mo, pay-as-you-go, no commitment, with 99.5% uptime and standard support (next-business-day Sev 1).
Premium plan starts at $400/mo, prepaid contract, with up to 99.95% uptime and enterprise support as fast as 1-hour Sev 1 plus a dedicated Technical Account Team.
Flex tier includes baseline security with RBAC (role-based access control).
Flex tier is positioned for prototypes, pilots, and small use cases with a zero-commitment entry point to experiment and ship quickly.
Premium tier is for teams scaling AI in production who need predictable pricing and enhanced reliability.
Weaviate Embeddings: hosted embedding models with pay-as-you-go GPU-powered access, including Snowflake Arctic-Embed-M v1.5 at $0.025 per 1M tokens and v2.0 at $0.040 per 1M tokens.
Weaviate Query Agent: free to try with 1,000 requests/month; $30 per organization monthly plan with 4,000 requests included and unlimited additional requests billed by usage.
Weaviate Cloud includes native AI services (Embeddings and Query Agent) on every plan, billed by usage.
Weaviate is open-source
Supports text2vec_palm vectorizer for automatic embedding generation
Weaviate clusters can be configured and deployed using Weaviate Cloud
Integrated with Google Cloud Vertex AI RAG Engine
Supports both vector and hybrid search retrieval
Supports multiple index types including vector and inverted index
Weaviate cluster connects via cluster API key
Building RAG applications on Google Cloud
Handles storage, index management, and retrieval for RAG pipelines
Available on Google Cloud infrastructure via Vertex AI RAG Engine
Can integrate with first-party and third-party language models
The Weaviate endpoint must be accessible for Vertex AI RAG Engine integration
Multimodal embeddings enable AI systems to search and reason across text, images, audio, and video in their native formats.
Text, images, audio, and video
Multimodal RAG implementations that work with data in its native form rather than converting to text.
Weaviate integrates with Google's Gemini for multimodal RAG implementations.
Vector-based semantic search via nearest-neighbour retrieval over learned embeddings.
Retrieval over non-text data (podcasts, scanned PDFs, whiteboard photos) without lossy transcription/OCR pipelines.
Practical capabilities
8 documented areasWhat it helps with
A concise view of the jobs, capabilities and integrations described in the recorded product sources.
Vector and hybrid search retrieval at scale
Large-scale multi-tenant deployments with 50K+ tenants in a single cluster
Building RAG applications on Google Cloud via Vertex AI RAG Engine
Multimodal RAG with text, images, audio, and video in native formats
Prototypes, pilots, and small use cases on Flex tier
Production AI scaling with predictable pricing on Premium tier
Large-scale multi-tenant search use cases, with cited deployments storing 50K+ tenants in
Flex tier is positioned for prototypes, pilots, and small use cases with a zero-commitment
Availability
Source checkedWhere it runs and where to get it
Documented product formats, platforms and official distribution destinations. Availability can vary by region and plan.
Cost / license
Flex plan starts at $45/mo, pay-as-you-go, no commitment, with 99.5% uptime and standard s · Premium plan starts at $400/mo, prepaid contract, with up to 99.95% uptime and enterprise · Weaviate Query Agent: free to try with 1,000 requests/month; $30 per organization monthly · Weaviate Cloud is free to start across the entire product suiteWeaviate is offered as an open-source platform.Weaviate is open-sourceYes, open-sourceWeaviate is offered as an open-source platform. · Weaviate is open-source · Yes, open-source
Application types
Vector databaseAI-native database
Platforms
Weaviate is an AI-native database platform combining vector search, RAG, and memory in oneAvailable on Google Cloud infrastructure via Vertex AI RAG EngineWeaviate Shared Cloud generally available on AWS in US East and EuropeShared CloudDedicated CloudAI database / Vector DatabasePremium plan is available in any region on AWS, GCP, or Azure.
Adoption notes
DeploymentWeaviate is deployment-agnostic and open source. · Weaviate clusters can be configured and deployed using Weaviate Cloud · Self-managed environment · Can be self-hosted
LicenseWeaviate is offered as an open-source platform. · Weaviate is open-source · Yes, open-source
Model supportSupports text2vec_palm vectorizer for automatic embedding generation
Data controlWeaviate is described as battle-tested and used in banking where security is important. · Flex tier includes baseline security with RBAC (role-based access control). · Weaviate Cloud supports more granular role-based access control with new Editor and Viewer · Premium plan includes RBAC, SSO/SAML, PrivateLink (AWS), Bring Your Own Key, and bring-you
Learning curveIntermediate
Primary use casesVector and hybrid search retrieval at scale, Large-scale multi-tenant deployments with 50K+ tenants in a single cluster, Building RAG applications on Google Cloud via Vertex AI RAG Engine, Multimodal RAG with text, images, audio, and video in native formats, Prototypes, pilots, and small use cases on Flex tier, Production AI scaling with predictable pricing on Premium tier, Large-scale multi-tenant search use cases, with cited deployments storing 50K+ tenants in, Flex tier is positioned for prototypes, pilots, and small use cases with a zero-commitment, Premium tier is for teams scaling AI in production who need predictable pricing and enhanc, Building RAG applications on Google Cloud, Multimodal RAG implementations that work with data in its native form rather than converti, Hybrid Search, Building AI-powered applications, Supports RAG, Hybrid Search, and Agentic AI solutions, Engram is Weaviate's managed memory and context service for agentic applications, now gene
What to verify before adopting
- The Weaviate endpoint must be accessible for Vertex AI RAG Engine integration
- Availability guarantees exclude planned maintenance windows (announced at least one week i
Evolution and major updates
Research in progressWeaviate timeline
A concise history of software releases and material product changes. Events appear only when a dated source supports what changed.
Scheduled for research
Building a reliable release history.
This profile is being checked for dated releases and material product changes. Nothing appears here until the exact date and event can be verified from a recorded source.
Dated event Short explanation Original source
Citation ledger
17 unique pagesRecorded sources
Facts, answers, structured details, milestones and primary resource links cite this shared ledger. Each external page appears once; release tags from the same GitHub project are grouped under one release history.
- 1weaviate.io 15 facts · 3 answers · Official site
- 2weaviate.io/blog/google-rag-api 12 facts · 4 answers · Documentation
- 3weaviate.io/blog/tags/release 13 facts · 1 answer · Official site
- 4weaviate.io/product 12 facts · Official site
- 5weaviate.io/product/integrations 12 facts · Official site
- 6weaviate.io/pricing 8 facts · 3 answers · Pricing
- 7weaviate.io/blog 8 facts · 1 answer · Official site
- 8weaviate.io/blog/multimodal-guide 6 facts · 2 answers · Official site
- 9weaviate.io/case-studies 8 facts · Official site
- 10weaviate.io/privacy 7 facts · Security
- 11weaviate.io/company/careers 3 facts · Official site
- 12weaviate.io/support-plans 2 answers · Pricing
- 13weaviate.io/blog/graphql-api-design Documentation
- 14weaviate.io/blog/hugging-face-inference-api-in-weaviate Documentation
- 15weaviate.io/blog/hybrid-search-for-web-developers Documentation
- 16weaviate.io/blog/limit-in-the-loop Official site
- 17weaviate.io/blog/weaviate-cloud-pricing-update Pricing
Research status120 substantive facts · 17 source pages · quality score 100/100
Connected directory
Semantic matchesExplore related solutions
These tools share a workflow, capability or audience with Weaviate. They may complement it rather than replace it, and are not presented as integrations or endorsements.
