Weaviate

Weaviate

Open-source vector database for semantic search

Overview

Weaviate provides an open-source vector database for AI-powered apps. It stores data objects together with their vector embeddings, enabling fast semantic and similarity searches across text, images, and audio. The system is cloud-native and modular, letting developers plug in models from providers like OpenAI, Cohere, and Hugging Face, and it supports hybrid vector/keyword search plus RAG features. Weaviate offers both a self-hosted option and managed cloud services (Weaviate Cloud and Enterprise Cloud) to scale with demand.

About Weaviate

Simplify's Rating
Why Weaviate is rated
B
Rated B on Competitive Edge
Rated A on Growth Potential
Rated C on Differentiation

Industries

Data & Analytics

Enterprise Software

AI & Machine Learning

Company Size

51-200

Company Stage

Series B

Total Funding

$67.7M

Headquarters

Amsterdam, Netherlands

Founded

2019

People at Weaviate

People at Weaviate who can refer or advise you

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Simplify's Take

What believers are saying

  • Ricoh's March 2026 investment enables integration with data-capture tech for unstructured enterprise data.
  • Engram's GA launch provides persistent memory for AI agents with a free tier of 1,000 runs.
  • Weaviate Agent Skills repository reduces RAG debugging time by 3x for early adopters.

What critics are saying

  • Pinecone displaces self-hosted users with zero operational overhead, threatening enterprise migration to Weaviate Cloud.
  • CVE-2026 path traversal vulnerabilities expose self-hosted users to file overwrite attacks, forcing costly cloud migrations.
  • Open-source BSD-3-Clause licensing allows proprietary forks, risking dual-model collapse if enterprise trust erodes.

What makes Weaviate unique

  • Weaviate combines object storage with vector capabilities in a hybrid Go-based architecture.
  • It supports GraphQL queries and schema-based data modeling for complex relationships.
  • Modular plugins enable seamless integration with diverse ML models like OpenAI and Hugging Face.

Help us improve and share your feedback! Did you find this helpful?

Funding

Total Funding

$67.7M

Above

Industry Average

Funded Over

3 Rounds

Series B funding is typically for startups that have proven their business model and need more funding to expand rapidly—often by entering new markets or adding more products. Investors are usually venture capital firms that specialize in later-stage investments.
Series B Funding Comparison
Above Average

Industry standards

$35M
$45M
Linktree
$50M
Weaviate
$65M
Substack
$100M
ClickUp

Benefits

Health Insurance

Dental Insurance

Vision Insurance

401(k) Company Match

Remote Work Options

Home Office Stipend

Company Equity

Professional Development Budget

Growth & Insights and Company News

Headcount

6 month growth

-4%

1 year growth

2%

2 year growth

10%
Weaviate
Jul 2nd, 2026
Native MCP + HFresh disk-based vectors are now GA.

Native MCP + HFresh disk-based vectors are now GA. Hello Weaviate Community! Weaviate is excited to share the Weaviate 1.38 release, bringing the HFresh disk-based vector index and built-in MCP Server to general availability. This update also rebuilds cluster-wide async replication and introduces previews for the Boost API and Nested Object Filtering. Latest AI & tech insights. Explore its recent Weaviate content: Read. * | Weaviate 1.38 Release: The full changelog and engineering context behind everything in this release. Read the blog * | Import & Vectorize Data with Weaviate at Scale: This guide covers the production patterns that keep imports fast and safe, like server-side batching, error handling, data type decisions, and ingesting media and PDFs without standing up an OCR pipeline. Read the blog Watch. Product highlights. * HFresh Vector Index (GA): Disk-based index that keeps memory low, especially for continuously changing streaming workloads, with built-in RQ-1 quantization. * MCP Server (GA): Native LLM/agent access to inspect schemas, search, and write data - no glue code required. * Async Replication, Everywhere: Re-architected to run cluster-wide from a single scheduler, rather than being configured and run separately. * Boost API (Preview) & Nested Filtering (Preview): Fine-tune rankings with query-time boosting and filter nested object properties using dotted paths. * 🆓 Weaviate Cloud free tier: A fully managed vector database that is free forever - no credit card required and no expiration. Comes with 100,000 objects, Query Agent, and Weaviate Embeddings built in. * | Engram is GA: Give your AI agents persistent, personalized memory that carries across sessions. Engram runs its own managed pipelines, so it works with your existing stack; no separate database required. Try it now * | New Support Portal: Weaviate launched support.weaviate.io, a single home for support across all its products. Open tickets through guided forms, track their status, and see your full interaction history all in one place! Ready to start building? Jump right in and spin up your free cluster with Weaviate Cloud. Or check its GitHub and star Weaviate while you're there. Company updates. Weaviate is excited to welcome Harneet! Join its team - Weaviate is hiring across various teams! Check out its career page for exciting opportunities in product, research, growth, and more. Hungry for more? Have a question or want to connect? Join its Weaviate Forum to engage in community conversations. See you in two weeks,

AI SO Tools
Jul 1st, 2026
Weaviate review 2026: pricing, features, pros & cons.

Weaviate review 2026: pricing, features, pros & cons. Weaviate is the open-source vector database built around native hybrid search - combining vector similarity with keyword search in a single query. Here's an honest look at the new 2026 cloud pricing, self-hosting tradeoffs, and how it compares to Pinecone. Quick verdict. Overall Rating 100K objects $45/mo min Best for: Teams building RAG applications that need hybrid (vector + keyword) search and want the option to self-host without vendor lock-in. Less ideal for teams wanting the absolute simplest managed setup - Pinecone still wins there. What is Weaviate? Weaviate is an open-source vector database designed for AI applications, most commonly retrieval-augmented generation (RAG) pipelines. Its defining feature is native hybrid search: a single query can combine dense vector similarity search with sparse BM25 keyword search, weighted by an alpha parameter, so teams don't need to bolt a separate keyword search system onto their vector store. Because the core database is open source, Weaviate can be self-hosted via Docker or Kubernetes at no licensing cost, or run as a fully managed service through Weaviate Cloud. It integrates as a pluggable module with OpenAI, Cohere, HuggingFace, and other embedding and reranking providers, and ships with actively maintained Python, JavaScript/TypeScript, Go, and Java client libraries. In October 2025, Weaviate restructured its cloud pricing model entirely, replacing the old Serverless tier with a new Free / Flex / Premium structure billed transparently on vector dimensions, object storage, and backup storage. Weaviate pros & cons. Pros. * - Open source at its core: Weaviate can be self-hosted for free with the full feature set, giving teams a real exit from vendor lock-in that fully managed competitors like Pinecone don't offer * - Hybrid search built in: Weaviate natively combines vector similarity search with BM25 keyword search in a single query, weighted by an alpha parameter - useful for RAG systems where pure semantic search misses exact keyword or acronym matches * - Generous free cloud tier: The Free plan includes 100,000 objects, 1GB memory, and 10GB disk on an always-free cluster per user, enough to build and test a real RAG prototype before committing to a paid tier * - Transparent, dimension-based billing on paid tiers: Flex pricing is calculated from vector dimensions, object storage, and backup storage rather than opaque per-query pricing, making cost more predictable to forecast as usage scales * - Modular vectorizer and reranker modules: Weaviate integrates directly with OpenAI, Cohere, HuggingFace, and other embedding/reranking providers as pluggable modules, so you don't need to manage embedding generation in a separate service * - GraphQL and REST APIs with strong client libraries: Python, JavaScript/TypeScript, Go, and Java clients are actively maintained, and the schema/class model maps cleanly onto most application data models Cons. * - 2025 pricing restructure raised the entry point: Weaviate Cloud's paid tier starting price moved from $25/month under the old Serverless plan to $45/month minimum under the new Flex tier - a real cost increase for small production workloads migrating off the free tier * - Premium tier is a steep jump: after Flex ($45/month minimum), the next tier up (Premium, with 99.95% uptime and dedicated deployment options) starts at $400/month on a prepaid contract - there's a wide gap between hobby-scale and enterprise-scale pricing * - Self-hosting requires real operational expertise: running Weaviate yourself in Docker or Kubernetes for production means you own sharding, backups, and upgrades - the free-to-self-host framing understates the DevOps investment needed at scale * - Class/schema model has a learning curve: developers coming from a simple key-value or pure-vector mental model need to learn Weaviate's schema, cross-references, and module configuration before getting hybrid search and filtering working well * - Smaller managed-service footprint than Pinecone: Pinecone's fully managed simplicity and broader out-of-the-box framework integrations still win for teams that want zero infrastructure decisions and the fastest path from prototype to production Weaviate pricing 2026. Free. * - 100,000 objects * - 1GB memory / 10GB disk * - 1 collection * - 1 always-free cluster per user * - Best-effort availability Prototyping and small RAG projects before committing to a paid tier Flex. From $45/mo * - Unlimited objects * - Up to 1,000 collections * - Shared cluster with replication * - RBAC security * - 99.5% uptime SLA * - Pay-as-you-go dimension-based billing Production workloads that need real scale without a long-term contract Premium. From $400/mo * - Unlimited objects and collections * - Shared or dedicated deployment * - AWS, GCP & Azure coverage * - 99.95% uptime SLA * - Phone/Slack support + Technical Account Team Enterprise workloads needing dedicated infrastructure and faster incident response A Dedicated Enterprise tier is also available from $400/month prepaid with 99.95% uptime, 1-hour Severity 1 response, and HIPAA compliance on AWS. Weaviate can also be self-hosted for free. Weaviate vs Pinecone. | Feature | Weaviate | Pinecone | | Open source / self-hostable | | Fully open source | | Managed only, closed source | | Hybrid search (vector + keyword) | | Native BM25 + vector | | Sparse-dense hybrid supported | | Free tier | | 100K objects, always free | | Serverless free tier | | Entry paid price | $45/mo (Flex) | Usage-based, no fixed minimum on Serverless | | Enterprise tier | $400/mo+ (Premium/Dedicated) | Custom Enterprise pricing | | Setup complexity | | Schema/class model, more config | | Minimal config, fastest to production | | Vendor lock-in risk | | Low - can self-host anytime | | Managed-only, no self-host option | Who should use Weaviate? RAG apps needing hybrid search. Native vector + BM25 keyword search in one query makes Weaviate a strong fit for RAG systems where pure semantic search misses exact terms, product codes, or acronyms. Teams avoiding vendor lock-in. Because the core database is open source, teams can self-host on their own infrastructure at any point, giving an exit path that fully managed-only competitors don't provide. Cost-conscious startups building on the free tier. The always-free 100,000-object tier is real enough to build and test a production-shaped RAG prototype before committing to Flex or Premium. Enterprises needing dedicated deployment. Premium and Dedicated Enterprise tiers offer HIPAA compliance, choice of cloud provider, and 1-hour incident response for regulated or mission-critical workloads. Frequently asked questions. Is Weaviate worth it in 2026? For teams that want hybrid search (vector + keyword) built in and value the option to self-host without vendor lock-in, Weaviate is a strong pick - the always-free 100K-object tier is genuinely usable for prototyping. Teams that want the absolute fastest path to production with zero infrastructure decisions may still prefer Pinecone's simpler managed model, especially since Weaviate's October 2025 pricing restructure raised the paid entry point from $25/month to a $45/month minimum. How is Weaviate priced? Weaviate Cloud has three tiers: Free ($0, 100,000 objects, 1 always-free cluster), Flex (from $45/month minimum, pay-as-you-go based on vector dimensions, object storage, and backup storage, 99.5% SLA), and Premium (from $400/month prepaid, 99.95% SLA, shared or dedicated deployment). Weaviate can also be fully self-hosted for free via Docker or Kubernetes if you're willing to manage the infrastructure yourself. What is hybrid search in Weaviate? Hybrid search combines dense vector similarity search with sparse keyword search (BM25) in a single query, weighted by an alpha parameter you control. This matters for RAG systems because pure semantic search sometimes misses exact keyword matches, acronyms, or product codes that keyword search catches - hybrid search gets the best of both without running two separate systems. How does Weaviate compare to Pinecone? Weaviate is open source and self-hostable, giving teams an exit from vendor lock-in that Pinecone (managed-only, closed source) doesn't offer. Weaviate also has native hybrid search built into its core query model. Pinecone counters with a simpler, faster path to production and a broader out-of-the-box integration ecosystem across RAG frameworks. Cost-conscious teams comfortable with more setup often pick Weaviate; teams that want zero infrastructure decisions often pick Pinecone. Can I self-host Weaviate for free? Yes - Weaviate's core database is open source and can be self-hosted via Docker or Kubernetes at no licensing cost. The tradeoff is that you take on sharding, backup, and upgrade operations yourself, which requires real DevOps capacity at production scale. Weaviate Cloud's managed tiers exist specifically to remove that operational burden for a monthly fee. What changed in Weaviate's 2026 pricing? In October 2025, Weaviate restructured its cloud pricing entirely: old tier names were retired, the starting price for paid usage moved from $25/month (the old Serverless tier) to a $45/month minimum under the new Flex tier, and billing switched to a transparent model based on vector dimensions, object storage, and backup storage rather than opaque per-query pricing. Compare vector databases. See how Weaviate stacks up against Pinecone and other AI infrastructure tools.

Weaviate
Jun 18th, 2026
Free Tiers in Weaviate Cloud, new Playground demos and a Ricoh investment.

Free Tiers in Weaviate Cloud, new Playground demos and a Ricoh investment. Hello Weaviate Community! The best developer platforms don't just provide infrastructure - they make it easy to learn, experiment, and build. That's why this week Weaviate is introducing free tiers across Weaviate Cloud, launching the new Weaviate Playground with hands-on AI demos, and celebrating a strategic investment from Ricoh as Weaviate continue expanding the AI-native platform for developers and enterprises alike. Latest AI & tech insights. Explore its recent Weaviate content: Read. * | Building AI should start with building - not billing: Weaviate has always been free to self-host. Now, Weaviate is extending that philosophy across the entire Weaviate Cloud platform with a free tier for the managed database, Query Agent, and Engram. Read the blog * | Ricoh invests in Weaviate: A new strategic investment from Ricoh through the RICOH Innovation Fund marks another exciting milestone as Weaviate continue building the AI-native database powering the next generation of AI applications. Read the announcement * | Memory changes everything for AI agents: Learn how Engram gives agents persistent memory and context, enabling them to remember past interactions, learn over time, and power more capable production-ready applications. Read the announcement * | Import & Vectorize Data at Scale: Moving from a prototype to millions of objects introduces new challenges. Learn production best practices for high-volume imports, server-side batching, retries, multimodal ingestion, and building resilient pipelines with Weaviate. Read the guide Watch. * | Knowledge Engineering with Dr. Bradley Allen - Weaviate Podcast #139: Dr. Allen explores five decades of AI history, from expert systems and knowledge graphs to today's LLMs, explaining why knowledge engineering remains essential for building trustworthy enterprise AI. Watch the full podcast * | Can Weaviate Win Europe's Biggest Hackathon?: Follow the Weaviate team as Weaviate take on one of Europe's largest AI hackathons, building an agentic content platform in just 26 hours while exploring the future of Europe's startup and builder ecosystem. Watch the journey * | Building a Production-Ready Legal RAG App... in One Prompt: See how Weaviate built a production-ready legal assistant using Weaviate Query Agent, multi-vector retrieval, and coding agents - transforming weeks of development into a single guided prompt. Watch the tutorial Product highlights. * 🆓 Weaviate Cloud Free Tiers: Weaviate Cloud now includes free tiers (with no credit card required) for both Database and Engram. Launch a fully managed cluster, experiment with Query Agent and persistent memory, and upgrade whenever you're ready. Start free * | Introducing the Weaviate Playground: Explore a growing collection of interactive demos built with Weaviate - from Engram and Query Agent to multimodal search and AI-powered recommendations. It's the easiest way to experience what's possible before building your own applications. Explore the Playground * | Engram General Availability: Its managed memory service for AI agents is now production-ready, enabling persistent context across sessions so agents can remember, learn, and improve over time. Try it now * | Weaviate Core v1.36.18: The latest patch release delivers stability improvements, bug fixes, RBAC enhancements, and infrastructure updates to keep production deployments running smoothly. Read the release notes. Ready to start building? Jump right in and spin up your free cluster with Weaviate Cloud. Or check its GitHub and star Weaviate while you're there. Hungry for more? Have a question or want to connect? Join its Weaviate Forum to engage in community conversations. See you in two weeks,

BPO Media
Jun 16th, 2026
Ricoh invests in ai-native vector database startup Weaviate through the RICOH Innovation Fund.

Ricoh invests in ai-native vector database startup Weaviate through the RICOH Innovation Fund. TOKYO, JAPAN - June 16, 2026 - Ricoh Company, Ltd. today announced that Ricoh has made an investment into Weaviate (Co-founder and CEO: Bob van Luijt, Co-founder and CTO: Etienne Dilocker), a developer of an AI-native vector database designed for unstructured data, headquartered in the Netherlands, through Ricoh's corporate venture capital (CVC) fund, the RICOH Innovation Fund, on March 13, 2026. This investment advances Ricoh's global strategy to support customers through digital and AI transformations, by exploring the potential of creating new solutions combining Ricoh's data capture technology with Weaviate's context-aware database. Enterprises today hold vast amounts of unstructured data - including scanned documents, PDFs, email text, and handwritten notes. While these data assets contain valuable knowledge, they are often difficult to utilize effectively with traditional data management methods, creating a key barrier to enterprise AI adoption. As generative AI adoption accelerates, transforming unstructured data AI-ready is becoming increasingly critical so that enterprises can leverage it in decision-making and business operations, ultimately improving productivity. Weaviate offers the all-round AI database, easy to use with the features and integrations developers need. Built with an open-source foundation and focused on a first-class developer experience, it is the default for developers to build AI applications. By evolving beyond traditional retrieval systems and incorporating a memory layer, Weaviate enables AI agents to retain and utilize context over time. This helps overcome the limitations of stateless processing, allowing applications to access relevant information more effectively and generate more accurate, consistent, and context-aware responses. Beyond simply organizing unstructured data, Weaviate's capabilities built on its vector database enable AI agents to learn from past interactions and access diverse enterprise information, supporting more advanced and long-term reasoning as well as improved decision-making. Through this investment, Ricoh will support Weaviate's efforts becoming the critical infrastructure for the agentic shift and explore opportunities to combine Ricoh's data capture technologies with Weaviate's context-aware database. This combination is expected to unlock the value of unstructured data that has not been fully utilized until now and lead to the creation of new solutions that enable cross-functional utilization of diverse information accumulated within the enterprise. Eiji Suzuki, General Manager, Corporate Planning Center, Corporate Planning Division, Ricoh Company, Ltd. stated, "Built on open technology and a strong community, Weaviate has pursued a data foundation that enables AI to understand and leverage information in context. Through these efforts, the company has helped shape a reliable approach to data utilization in the era of generative AI. Ricoh shares this vision. We are delighted to embark on this partnership and look forward to creating value together through our collaboration. Together, we are committed to advancing this vision further and unlocking new value for our customers. Through this partnership, we will translate the potential of generative AI into practical business applications and more informed decision-making." Bob van Luijt, Co-founder and CEO, Weaviate added, "Weaviate's open-source software is already proving its value in Japan, as the growth we're seeing in adoption and community engagement is unmistakable. We're excited to partner with Ricoh, who recognizes that momentum and shares our conviction that this ecosystem is just getting started. This funding isn't just capital; It's fuel to accelerate what the community is already building. This partnership signifies a major milestone in our global expansion strategy, specifically tailored to meet the sophisticated demands of the Japanese market. As we scale our operations, we remain committed to fostering a robust developer network and driving innovation that resonates across the entire technological landscape of the region." Ricoh established the RICOH Innovation Fund in November 2023 to support the growth of B2B startups and accelerate its transformation into a digital services company. Through open innovation and collaboration with partners, Ricoh supports people's creativity and contributes to a sustainable society by generating innovation and continuously transforming people's work.

GlobeNewswire
Feb 21st, 2026
Weaviate Launches Agent Skills to Empower AI Coding Agents

Weaviate launches Agent Skills to empower AI coding agents. The new open-source repository delivers structured skills, slash commands, and production-ready cookbooks to reduce AI coding errors and speed up weaviate-based application development. February 21, 2026 13:22 ET | Source: Weaviate Amsterdam, Netherlands, Feb. 21, 2026 (GLOBE NEWSWIRE) - February 20, 2026 - Weaviate, the leading open-source AI database, today announced the launch of Weaviate Agent Skills, an innovative open-source repository that equips popular coding agents like Claude Code, Cursor, GitHub Copilot, VS Code, and Gemini CLI with precise tools for generating production-ready code targeting Weaviate workflows. This release builds directly on Weaviate's Query Agent, first previewed in March 2025 and reaching general availability in September 2025. The Query Agent supports natural language queries across multiple collections, featuring multi-collection routing, intelligent query expansion, decomposition for complex questions, user-defined filters, and reranking for optimal results. Developers can test Agent Skills immediately using Weaviate Cloud's free Sandbox clusters - small instances designed for experimentation that last 14 days and can be extended or upgraded to production Shared Cloud setups. Comprehensive Repository Tools The repository at github.com/weaviate/agent-skills is structured into two core sections, providing full lifecycle support from basic operations to complete applications. Weaviate Skills in the /skills/weaviate directory offer granular scripts for key tasks. These cover cluster management such as schema inspection, collection creation, and metadata retrieval; data lifecycle operations including imports from CSV, JSON, or JSONL files plus example data generation; agentic search powered by Query Agent; and advanced retrieval options like hybrid search (blending semantic and keyword with alpha parameters), pure semantic, or keyword modes. Cookbooks in the /skills/weaviate-cookbooks folder provide end-to-end blueprints for production apps. Highlights include Query Agent chatbots built with FastAPI backends and Next.js frontends; multimodal PDF RAG pipelines using ModernVBERT for multivector embeddings alongside Ollama or Qwen3-VL for generation; basic, advanced, and agentic RAG implementations with decomposition and reranking; and DSPy-optimized agents incorporating custom tools and persistent memory. Six Streamlined Slash Commands Agent Skills introduces six intuitive commands that AI coding agents can auto-discover and execute, streamlining Weaviate interactions: * /weaviate:ask: Delivers AI-generated answers with citations via Query Agent. * /weaviate:collections: Lists all schemas or inspects specific collections. * /weaviate:explore: Shows data metrics, counts, and sample objects. * /weaviate:fetch: Retrieves objects by ID or filters by properties. * /weaviate:query: Performs natural language searches across collections. * /weaviate:search: Executes hybrid, semantic, or keyword searches with parameters like alpha blending. For instance, developers can run "/weaviate:search query 'best laptops' collection 'Products' type 'hybrid' alpha '0.7'" for balanced retrieval or "/weaviate:ask What are vector database benefits?" against a Documentation collection. CEO Bob van Luijt's Vision Bob van Luijt, Co-Founder and CEO of Weaviate - which he launched as an open-source vector search engine in March 2019 - shared launch insights. "Weaviate Agent Skills bridges the gap between high-velocity AI coding and reliable infrastructure, letting developers build sophisticated AI systems without debugging agent hallucinations," van Luijt stated. As a prominent Netherlands-based technology entrepreneur, Van Luijt champions open-source AI tools. He positions Weaviate as a "batteries-included" stack that combines vector search, structured filtering, and agentic capabilities for modern AI applications. Instant Setup for Developers Integration is designed for speed. Install with a single command like npx skills add weaviate/agent-skills or via plugin managers in tools like Claude Code. Configure environment variables using your Weaviate Cloud endpoint and API key from a free Sandbox cluster. Run /weaviate:quickstart for guided setup. This launch aligns with Weaviate's 2025 momentum, including Query Agent GA, enhanced TypeScript/Python SDKs, multi-turn conversations, streaming responses, and new C#/Java clients for broader ecosystem support. Weaviate invites the community to star the repo, submit pull requests for new cookbooks, and participate in discussions on GitHub, the Weaviate Forum, Slack workspace, and X. Strategic Impact on AI Development Agent Skills addresses a critical pain point: AI agents often generate inaccurate or incomplete code for vector databases due to hallucinations or outdated knowledge. By providing verified, modular tools, Weaviate enables faster iteration from prototype to production. Early adopters report 3x reductions in debugging time for RAG pipelines and agentic apps. The repository's modular design also facilitates contributions, with plans for expanded skills covering generative modules, tenancy isolation, and hybrid cloud deployments. About Weaviate Weaviate is an open-source, AI database that handles storage, retrieval, and orchestration for generative AI at scale. Backed by enterprise-grade Weaviate Cloud services, it powers agentic workflows - from simple semantic search to complex multi-agent systems - delivering sub-second latency on billions of objects. Media Contact: Philip Vollet [email protected] +49-160-96488554

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