AI Company Profile · Research updated 21 August 2026

Weaviate

An open-source, AI-first database company built around vector search, retrieval, RAG, agentic applications and increasingly managed AI services. Weaviate began as a semantic-search project and evolved into infrastructure for production AI applications.

AI infrastructureVector databaseOpen sourceRAGAI agents
2019Company founded
AmsterdamHeadquarters
90+Employees currently listed by company
15M+Downloads reported by Weaviate

1. Company Overview

FieldVerified finding
CompanyWeaviate (formerly SeMI Technologies).
IndustryAI infrastructure, database software, developer tools.
Founded2019 as SeMI Technologies; the Weaviate project predates the company and its roots are described by the company as beginning with earlier experiments in semantic search.
HeadquartersAmsterdam, Netherlands.
TypePrivate, venture-backed company.
Official websiteweaviate.io
Primary productWeaviate, an open-source AI-native vector database.
Current positioning“The AI database developers love” is the current homepage positioning; the company describes itself as creating open-source, AI-first infrastructure.
Mission / operating thesis.
Weaviate has explicitly described its goal as creating a profitable, AI-first, open-source company around its vector database. Its current company statement is: “We create open source, AI-first infrastructure.” [6][1]
Vision.
No separate formal “vision statement” is consistently published. The strongest verified formulation is the belief that the next wave of software infrastructure is AI-first and that open-source communities are a basis for high-quality software. [1]
Scope note: This profile uses public information available through 21 August 2026. Private financials, founder personal details and internal architecture that the company does not disclose are marked “Not Publicly Available.”

2. Founders

Authoritative Weaviate sources identify Bob van Luijt and Etienne Dilocker as co-founders. [34][35]

Bob van Luijt — Co-founder & CEO

  • Education: ArtEZ in the Netherlands and Berklee College of Music in the United States; he also references Harvard Business School executive education. [36]
  • Career before Weaviate: Started a small software/web business at age 15 and later ran software consulting/design work. He became interested in machine learning and open-source software business models before Weaviate. [36]
  • Expertise: Open-source business strategy, AI infrastructure, vector databases, software entrepreneurship and communication.
  • Role: Co-founder and CEO. [34]
  • Date/place of birth: Public biographies report 15 November 1985 and Bergen op Zoom, Netherlands; these details are not stated on Weaviate's own executive page, so they should be treated as secondary-source biography rather than a company-verified corporate fact.
  • Nationality: Dutch, according to public biographical sources.
  • Net worth: Not Publicly Available.

Etienne Dilocker — Co-founder & CTO

  • Background: Deep-tech engineer focused on distributed systems, cloud technology, auto-scaling databases and vector-index research. [35]
  • Career before Weaviate: Freelance software engineer, full-stack engineer and technical lead across European startups, SMEs and large enterprises in finance, retail, wholesale, e-commerce, broadcasting and software. [37]
  • Key contribution: Weaviate's founding story credits Etienne with proposing the idea of an end-to-end database in which vector embeddings were first-class data and with taking over hands-on development of the first product. [36][5]
  • Role: Co-founder and CTO.
  • Date/place of birth, nationality, education: Not Publicly Available from the authoritative sources reviewed.
  • Net worth: Not Publicly Available.
Image verification note: The founder portrait shown below was supplied by the user. It is included as a visual reference only and is not used here to independently establish or verify the identity of the person pictured.
Founder portrait supplied by the user

Founder portrait — user-provided reference

This image is integrated into the profile as supplied. Identity, date of birth, nationality and other biographical attributes are verified independently through the cited written sources above rather than inferred from the photograph.

3. Founding Story

The story begins before the company. Bob van Luijt describes encountering word embeddings and GloVe around 2015–2016. The key insight was that machine-learning embeddings could represent semantic relationships numerically: if meaning could be represented as vectors, software could search by meaning rather than only by exact words. [4][36]

The early concept was influenced by semantic-web ideas, graph representations and the practical problem of naming and finding objects across software systems. The team tested whether context could be represented through embeddings and whether data objects could be stored and retrieved directly in vector space. [4]

By late 2018, the project entered a Dutch startup accelerator. In 2019, SeMI Technologies was formed around the open-source project. The team initially explored a broader graph/semantic-data concept, then deliberately doubled down on NLP, embeddings and vector storage as the technology's value became clearer. [4]

The difficult early problem was not merely engineering. It was market education: the founders had to convince potential customers that embeddings and vector search were a new infrastructure primitive worth adopting. The company later described a period when it had a strong belief in the product but few customers or users asking specifically for vector search. [5]

A major turning point came as transformer models and then generative AI made embeddings, semantic retrieval and RAG mainstream. Weaviate's open-source community and developer adoption gave the company a distribution base, while managed cloud services supplied a monetization path. [5]

4. Company Timeline

2015–2016

Bob describes becoming interested in word embeddings and the ability of vector representations to capture semantic meaning. [4]

Late 2018

The Weaviate project entered a Dutch startup accelerator, helping the team form around the open-source project and move toward production. [4]

2019

SeMI Technologies was founded around Weaviate. The company later consistently described 2019 as its founding year. [1]

2020

Weaviate/SeMI raised a reported $1.2M seed round from Zetta Venture Partners and ING Ventures. [8]

2021

Weaviate 1.2 introduced transformer support including DistilBERT, BERT, RoBERTa and Sentence-BERT integrations. The project also published its history and architecture publicly. [4]

2022

Series A of $16M was announced, led by NEA and Cortical Ventures. Weaviate expanded cloud and enterprise ambitions and launched a pay-as-you-grow cloud pricing model later that year. [8][12]

January 2023

SeMI Technologies changed its company name to Weaviate because the product brand had become more recognized than the corporate name. [7]

April 2023

Weaviate raised a $50M Series B led by Index Ventures, with Battery Ventures and existing investors participating. The round valued the company at $200M according to contemporaneous company/market reporting. [9][10]

2023

Native multi-tenancy capable of scaling to millions of tenants was introduced; generative-search and RAG capabilities became central to the product story. [5]

2024

Applied research expanded around binary quantization, filtered vector search and multimodal embeddings. ACORN was introduced in Weaviate 1.27 in late 2024. [22]

2025

Major releases added multi-vector search, MUVERA, quantization, BlockMax WAND, RBAC improvements and a generally available Query Agent. [18][17]

2026

Weaviate released v1.36, v1.37 and v1.38; introduced/advanced HFresh, built-in MCP, agent capabilities and Engram; and made Weaviate Cloud free to start. [14][15][13]

5. Products & Services

Core database

Weaviate Database

Purpose: Store objects plus vector embeddings and retrieve them using semantic, keyword, hybrid and filtered search. It can be self-hosted or consumed as a managed service. [19]

Target users: AI/ML engineers, application developers, platform teams and enterprises.

Key features: HNSW/Flat/Dynamic/HFresh indexes, hybrid search, BM25 keyword search, filtering, multi-tenancy, replication, backups, compression, multimodal search and RAG integrations. [21]

Pros: Open source, broad AI ecosystem integrations, flexible deployment, rich filtering and developer tooling.

Cons: Self-hosting still creates operational responsibility; vector indexing and high-scale workloads require careful memory/storage planning.

Managed cloud

Weaviate Cloud

Purpose: Managed hosting that removes cluster administration while retaining the core Weaviate database.

Current pricing: Free tier; Flex from $45/month; Premium from $400/month on the current pricing page. Billing is based on vector dimensions, storage and backups, with regional variation. [11]

Deployment: Shared and Dedicated Cloud; current documentation lists AWS, GCP and Azure availability, with different region coverage by plan. [11]

Agentic query

Weaviate Query Agent

Purpose: Let users ask complex questions in natural language and have foundation models translate the question into database operations and retrieval steps.

Status: General availability was announced in September 2025 after an earlier alpha preview. [17]

Pricing: Free tier of 1,000 requests/month; paid plan $30/month with 4,000 requests included plus usage-based additional requests, according to the current product page. [11]

AI memory

Engram

Purpose: A managed memory and context service designed for agentic applications.

Status: General availability announced June 3, 2026. [16]

Pricing: The sources reviewed establish the free Cloud entry point and product availability but do not provide a separately itemized public enterprise price for Engram.

Model service

Weaviate Embeddings

Purpose: Managed embedding generation inside Weaviate Cloud, reducing the need to operate a separate embedding service.

Current examples: Snowflake Arctic Embed models and ModernVBERT ColModernVBERT are listed on the pricing page, with usage-based token pricing. [11]

Ecosystem

Open-source + integrations

Weaviate integrates with model providers and cloud platforms including OpenAI, Cohere, Hugging Face, Google/Vertex AI and AWS Bedrock, while supporting self-hosted deployments. [31][32]

6. Technology Stack

Core architecture

  • Language: The database core is written in Go. [20]
  • Storage: Shards contain an object store, inverted index and vector-index store; object/inverted storage uses an LSM-tree design. [20]
  • Vector indexing: HNSW is the principal large-scale index; Flat, Dynamic and HFresh are also supported. [21]
  • Filtering: Structured filters are combined with vector search using an inverted index and allow-list approach. ACORN became the default filtering strategy for new collections in v1.34. [22]
  • APIs: REST, GraphQL and gRPC; official clients include Python, TypeScript/JavaScript, Go and Java. [19]

AI/model layer

  • Weaviate is model-agnostic: it can work with externally hosted or locally hosted embedding, reranking and generative models.
  • Documented integrations include OpenAI, Cohere, Hugging Face, Google/Vertex AI, AWS Bedrock and other providers. [31][32]
  • Historical transformer integrations included BERT-family and Sentence-BERT models. [14]
  • Current product direction includes multimodal embeddings, agentic querying, MCP and persistent agent memory. [15][16]
  • Not publicly disclosed: a single proprietary “Weaviate foundation model” or a single private model-training stack. Weaviate's role is primarily infrastructure and orchestration around models selected by users or integrated into the platform.
Security and deployment: Weaviate supports self-hosting, cloud deployment, customer VPC patterns and enterprise controls. Current Cloud materials list RBAC, SSO/SAML on higher tiers, SOC 2 auditing and HIPAA compliance on Enterprise Cloud in AWS. [11]

7. How Weaviate Works

The simplest mental model is: turn data into vectors → index those vectors → retrieve semantically relevant data → optionally combine it with keyword/structured filtering → pass the retrieved context to an LLM or application.

Input data
Embedding / vectorization
Index + metadata
Semantic / hybrid retrieval
RAG / agent reasoning
Business action

Example: customer-support RAG

1. Support articles are chunked. 2. An embedding model turns each chunk into a vector. 3. Weaviate stores the vector and source metadata. 4. A user's question is vectorized. 5. Weaviate retrieves semantically similar passages, optionally with filters such as product, region or document date. 6. The retrieved context is passed to an LLM. 7. The application returns an answer with citations or other grounding information.

Why this differs from ordinary databases

A conventional keyword index primarily matches words. A vector database compares representations of meaning. Weaviate can combine both approaches: semantic similarity for conceptual relevance, BM25/keyword search for exact terms, and structured filters for constraints. [23][24]

8. Business Model

Open-source distribution

The core database is open source, lowering experimentation and adoption friction. Developers can self-host without paying Weaviate. [13]

Managed cloud

Weaviate monetizes operational convenience, reliability, support, enterprise security and managed AI services through Cloud plans.

AI services

Embeddings, Query Agent and Engram extend monetization beyond database hosting into AI-native services. [11]

Strategic advantage: The model creates a developer funnel. Open source can generate usage and community adoption; successful applications can move to managed cloud when teams need reliability, scale, compliance or less operational work. This is a strategic analysis based on Weaviate's public open-source and cloud positioning, not a disclosed internal revenue formula.

9. Funding History

DateRoundAmountInvestors / leadValuation
Aug 2020Seed$1.2MZetta Venture Partners; ING VenturesNot Publicly Available
Feb 2022Series A$16MNEA and Cortical Ventures co-led; other participation reportedNot Publicly Available
Apr 2023Series B$50MIndex Ventures led; Battery Ventures and existing investors participated$200M reported at the time

The three publicly reported core rounds above total $67.2M. Commercial databases sometimes show slightly different totals because of additional/secondary records. No current valuation has been publicly verified. [8][9][10]

IPO: No IPO or public-market listing is disclosed. Weaviate remains private.

10. Leadership Team

RolePerson / statusBackground
CEOBob van LuijtCo-founder; software entrepreneur and open-source/AI infrastructure strategist. [34]
CTOEtienne DilockerCo-founder; distributed systems, cloud engineering and vector-index specialist. [35]
VP EngineeringPaul de GrijpCurrent company team page lists him as VP of Engineering. [1]
VP ProductAlvin RichardsCurrent Weaviate author/team materials list Alvin Richards as VP of Product. [38]
Head of Weaviate LabsCharles PierseLeads research/development of AI-native services; Query Agent work is among his public contributions. [17]
Director of Applied ResearchJohn TrengroveLeads applied research; public work includes vector-search research such as ACORN. [1]
Head of MarketingAlea AbedListed in Weaviate's public author/team materials.
VP of SalesBen SabrinListed in Weaviate's public author/team materials.
Director of Security & ComplianceSpiros AndreouPublicly listed in company materials; involved in enterprise security/compliance content.
CFO / COONot Publicly AvailableNo current public appointment was verified in the sources reviewed.
BoardNot fully disclosed on the accessible company pageWeaviate links to a Board & Advisors organizational chart; this profile does not infer a current roster beyond sources that clearly identify it.

11. Financial Information

MetricFinding
RevenueNot Publicly Available.
Profit / lossNot Publicly Available.
ARRNot Publicly Available; no reliable company-disclosed ARR was found.
Market capNot applicable; private company.
Valuation$200M reported in connection with the April 2023 Series B; no current valuation verified.
Employees90+ on the current company About page. Earlier career material has shown different counts as the company grew. [1]
Downloads15M+ reported by the company on its current About page. [1]
Community4,000+ community members reported on the current About page. [1]
No attempt has been made to turn employee counts, downloads or third-party database estimates into revenue estimates. They are operating indicators, not financial statements.

12. Competitors

PlatformPositionDeploymentPricing signalKey differentiation
WeaviateOpen-source AI-first vector databaseSelf-hosted + managed cloudFree; Flex from $45/mo; Premium from $400/moOpen source, hybrid search, filtering, multi-tenancy, broad model integrations, agent/memory services.
PineconeManaged vector database / AI developer platformManaged; BYOC optionsFree Starter; Builder $20/mo; Standard $50/mo minimum; Enterprise $500/mo minimumStrong managed experience, serverless architecture and mature enterprise offering. [40]
QdrantOpen-source vector search engineSelf-hosted + CloudFree tier; usage/resource-based paid plansStrong open-source/vector-search positioning and flexible infrastructure. [41]
Milvus / ZillizOpen-source vector database + managed cloudSelf-hosted + Zilliz CloudUsage-based / cluster-basedLarge-scale vector search heritage and broad cloud options. [42]
Postgres + pgvectorGeneral database extended for vectorsSelf-hosted + many managed Postgres servicesDepends on Postgres providerStrong when teams want relational data and vector search in one familiar database.
Elasticsearch / OpenSearchSearch and observability platforms with vector capabilitiesSelf-hosted + managedProvider dependentDeep traditional search, filtering and operational ecosystems.
Competitive interpretation: Weaviate's strongest differentiation is not simply “vector search.” It is the combination of open-source distribution, AI-native retrieval primitives, structured filtering, multimodal support, flexible deployment and a growing layer of managed agent/memory services. This positioning also puts it into direct competition with general databases and search engines as those platforms add vector capabilities.

13. SWOT Analysis

Strengths

  • Open-source core and developer community.
  • Strong AI-native retrieval feature set.
  • Flexible self-hosted and cloud deployment.
  • Deep research/engineering focus on vector indexes, filtering and compression.
  • Integration ecosystem across model and cloud providers.

Weaknesses

  • Vector databases remain a technically specialized category that requires architecture expertise at scale.
  • Self-hosting can create operational complexity.
  • Private financial performance is not transparent to outsiders.
  • Competitive differentiation is harder as major databases add vector features.

Opportunities

  • Agentic AI and persistent memory.
  • RAG becoming a standard enterprise application pattern.
  • Multimodal search and retrieval.
  • Cloud-native database modernization.
  • Enterprise demand for private/VPC AI infrastructure.

Threats

  • Hyperscalers and incumbent databases integrating vector search.
  • Managed vector databases competing on simplicity and price.
  • Rapid changes in embedding and LLM architectures.
  • AI workloads becoming commoditized at the database layer.
  • Security/compliance requirements increasing enterprise sales friction.

14. AI & Innovation

  • Vector-first infrastructure: Weaviate treats embeddings as first-class data and provides indexes optimized for approximate nearest-neighbor retrieval.
  • Hybrid search: combines semantic/vector retrieval with lexical BM25-style ranking, useful when both meaning and exact terminology matter. [24]
  • Filtered vector search: ACORN is a major engineering innovation in the platform; Weaviate's implementation is inspired by the ACORN research paper and was customized for its HNSW architecture. [22]
  • Compression: Weaviate has invested in binary/rotational quantization and memory-efficient vector indexes to reduce resource requirements.
  • Multivector retrieval: newer releases support multi-vector embeddings and MUVERA-style encoding. [18]
  • Agentic database interaction: Query Agent, MCP and Engram represent a move from “database as storage” toward “database as an AI-accessible context layer.” [17][15][16]
  • Research: Weaviate contributors have published work such as StructuredRAG, evaluating structured-output reliability of LLMs in RAG systems. [39]
  • Patents: No material patent portfolio was verified in the authoritative sources reviewed; do not assume the company has none.

15. Partnerships & Ecosystem

AWS

Weaviate is available through AWS Marketplace and integrates with SageMaker and Bedrock. Its AWS materials describe scalable deployment in the customer's AWS environment. [31]

Google Cloud

Weaviate Cloud is built on Google Cloud and integrates with Vertex AI and Google AI models. [32]

Model providers

Public documentation lists integrations with OpenAI, Cohere, Hugging Face, Google and others, supporting embedding, reranking and generation workflows.

Enterprise examples

Weaviate's published case studies include Morningstar, Instabase, DocsBot, Kapa, Stack AI, Loti AI, Neople, MetaBuddy and other AI application companies. These are customer case-study disclosures, not a complete customer list. [25]

16. Global Presence

Weaviate describes itself as a global remote-first startup with team members across many parts of the world. Its 2023 Series B announcement described Amsterdam headquarters with offices in the U.S. and Australia. Current public materials emphasize a distributed workforce rather than a fixed office network. [1][33]

Localization: The product supports multilingual and multimodal model integrations; the developer community and documentation are primarily English-language. Global cloud availability varies by provider and plan.

17. Marketing Strategy

Developer-led distribution

  • Open-source product creates a low-friction trial mechanism.
  • Extensive documentation, tutorials, recipes, release notes and technical blogs attract developers through search and learning intent.
  • GitHub and community participation reinforce credibility.
  • Weaviate Academy, webinars, events and a forum deepen adoption.

Content as product education

  • Technical explainers demystify vector databases, RAG, filtering and indexing.
  • Research content demonstrates engineering depth rather than relying only on brand advertising.
  • Customer case studies translate technical capabilities into business outcomes.
  • Cloud and enterprise services provide a natural conversion path from experimentation to production.

This strategy is best described as developer education + open-source community + bottom-up adoption + enterprise expansion. The exact CAC, conversion rates and paid acquisition mix are not public.

18. Company Culture

Be Kind

Empathy, respect and generosity are explicit company values. [1]

Work together as one

Shared responsibility, learning and teamwork are emphasized.

Strive for excellence

Ownership, ambition and customer/community impact are emphasized.

Encourage transparency

Weaviate explicitly advocates transparency by default, including sharing knowledge with its open-source community.

Inspire trust

The company emphasizes autonomy, flexibility and trust.

Remote-first

All roles are described as fully remote, with regional constraints for some positions. Benefits include equipment, flexible remote work and company trips. [33]

Hiring process

The published process includes an introduction chat, hiring-manager discussion, a Weaviate-related challenge followed by a review, and a culture-fit conversation. Offers are contingent on a background check. [33]

19. Awards & Recognition

  • Weaviate has been described by its own career materials as one of Forbes' Top 50 AI startups. [34]
  • Forbes' company profile has listed Weaviate among AI/vector-database infrastructure companies and recorded its $200M valuation following the 2023 Series B. [10]
  • The company has participated in open-source programs such as Google Season of Docs and Google Summer of Code-related activities.

No comprehensive independent awards/certification registry was found. Security certifications should be distinguished from awards: Weaviate currently states that its Cloud service is SOC 2 audited, with HIPAA compliance available on Enterprise Cloud in AWS. [11]

20. Challenges & Controversies

Documented challenges

  • Market education: In its early years, the company had to explain why vector embeddings and semantic search were important before customers were actively requesting them. [5]
  • Operational scale: Large vector collections create memory, indexing, filtering and query-performance challenges; Weaviate's engineering roadmap repeatedly addresses these issues.
  • Resource efficiency: Vector indexes can be memory-intensive. Quantization and HFresh are examples of the company's response. [14]
  • AI quality: RAG systems depend on retrieval quality as well as the generation model; poor retrieval can lead to incomplete or incorrect answers. This is a structural challenge of the category, not a unique Weaviate failure.

Legal / controversy review

No major public legal case or regulatory controversy involving Weaviate was verified in the authoritative sources reviewed for this profile. This should not be interpreted as a claim that no disputes have ever existed; it means no material case was found that met the verification threshold used here.

21. Future Roadmap

Confirmed direction: Weaviate's 2025 review and 2026 releases show a clear focus on reliable infrastructure for agentic systems, better search quality, lower resource consumption, easier developer interaction and managed AI services. [18]

Agentic AI

Query Agent, MCP and agent skills point toward databases becoming directly usable by AI agents rather than only by application code. [15]

Memory

Engram's GA launch suggests persistent context/memory will become a product layer alongside the database. [16]

Efficiency

HFresh, compression, quantization and improved indexing indicate continued optimization for very large workloads. [14]

Prediction, not company guidance: If current trends continue, Weaviate is likely to compete increasingly as an “AI data platform” rather than only as a vector database. The main risks are commoditization of vector search, cloud-provider competition and the possibility that general-purpose databases absorb enough AI-native functionality to reduce the need for a specialist layer.

22. Key Facts — 50 Useful & Lesser-Known Insights

  1. Weaviate is an AI infrastructure/database company, not an end-user chatbot.
  2. The company was founded in 2019 as SeMI Technologies.
  3. The Weaviate project itself has roots earlier than the corporate founding.
  4. Amsterdam is the company's headquarters.
  5. Bob van Luijt is co-founder and CEO.
  6. Etienne Dilocker is co-founder and CTO.
  7. Bob started a software business at 15.
  8. Bob studied music rather than computer science.
  9. He studied at ArtEZ and Berklee College of Music.
  10. Bob later completed Harvard Business School executive education.
  11. Etienne's background is distributed systems and cloud technology.
  12. Etienne has worked across finance, retail, wholesale and e-commerce software.
  13. The early concept was inspired by semantic representations of data.
  14. Word embeddings were an important intellectual trigger.
  15. The team explored semantic graphs before focusing more strongly on vector storage.
  16. Weaviate is open source.
  17. The company monetizes through managed services and enterprise capabilities.
  18. The company changed its corporate name from SeMI Technologies to Weaviate in January 2023.
  19. The rename happened because the product brand had become better known than the corporate name.
  20. The company raised $1.2M seed financing in 2020.
  21. It raised $16M in Series A in 2022.
  22. It raised $50M in Series B in 2023.
  23. The 2023 Series B was led by Index Ventures.
  24. Battery Ventures participated in the Series B.
  25. The reported 2023 valuation was $200M.
  26. No current valuation is publicly verified.
  27. Weaviate supports REST APIs.
  28. Weaviate supports GraphQL search.
  29. Weaviate supports gRPC for high-performance operations.
  30. The database core is written in Go.
  31. Weaviate uses HNSW for large-scale vector search.
  32. It also supports Flat and Dynamic vector indexes.
  33. HFresh is a newer disk-oriented vector index.
  34. Weaviate combines vector and inverted indexes.
  35. Hybrid search combines semantic and keyword signals.
  36. RAG is a core solution pattern.
  37. Weaviate can use third-party embedding models.
  38. Weaviate integrates with OpenAI, Cohere, Hugging Face and Google model ecosystems.
  39. AWS Bedrock and SageMaker integrations are publicly documented.
  40. Google Cloud and Vertex AI integrations are publicly documented.
  41. Multi-tenancy is a major enterprise capability.
  42. Weaviate's 2023 work targeted millions of tenants.
  43. ACORN improves filtered HNSW retrieval in difficult filter/query combinations.
  44. ACORN became the default filter strategy for new collections in v1.34.
  45. Query Agent reached general availability in 2025.
  46. Engram reached general availability in 2026.
  47. MCP support became part of the 2026 product direction.
  48. Weaviate Cloud became free to start in June 2026.
  49. The current pricing page lists Flex from $45/month.
  50. The current pricing page lists Premium from $400/month.
  51. Weaviate currently reports 15M+ downloads, 16.3K+ GitHub stars, 90+ employees and 4K+ community members.

23. Lessons for Entrepreneurs

Product

  • Build around a fundamental infrastructure shift, not a thin AI wrapper.
  • Keep the core primitive useful even when model providers change.
  • Invest early in performance, reliability and developer experience.
  • Let open source validate demand before forcing a commercial sales motion.

Go-to-market

  • Developer education can be a distribution channel.
  • Technical content can both attract users and demonstrate competence.
  • Open source and cloud services can form a natural funnel from experimentation to production.

Leadership

  • Founders may need to change roles as the company scales.
  • Technical founders can create leverage by combining product and engineering judgment early.
  • Transparency becomes more important, not less, when an open-source community grows.

Strategy

  • Specialist infrastructure can win when general-purpose systems create too much complexity.
  • But specialists must keep innovating as incumbents add the same features.
  • In AI, the durable moat is often system-level performance, community and ecosystem rather than a single model.

24. References & Verification

The report prioritizes first-party company materials and technical documentation, with reputable third-party sources used for historical funding and biography cross-checks.

  1. Weaviate — About Us
  2. Weaviate — Official homepage
  3. Weaviate — Company Investors
  4. Weaviate — History of Weaviate
  5. Weaviate — Fourth Birthday / founding story
  6. Weaviate — Mission
  7. Weaviate — Company rename
  8. TechCrunch — Series A / company background
  9. Weaviate — Series B press release
  10. Forbes — Weaviate profile
  11. Weaviate — Pricing
  12. Weaviate — 2025 pricing model
  13. Weaviate — Cloud free tier
  14. Weaviate — 1.38 release
  15. Weaviate — 1.37 release
  16. Weaviate — Engram GA
  17. Weaviate — Query Agent GA
  18. Weaviate — 2025 in review
  19. Weaviate — APIs
  20. Weaviate — Architecture
  21. Weaviate — Indexing
  22. Weaviate — Filtering / ACORN
  23. Weaviate — RAG
  24. Weaviate — Search
  25. Weaviate — Case studies
  26. Weaviate — Instabase case study
  27. Weaviate — DocsBot case study
  28. Weaviate — Loti case study
  29. Weaviate — Morningstar case study
  30. Weaviate — Kapa case study
  31. Weaviate — AWS partnership
  32. Weaviate — Google Cloud partnership
  33. Weaviate — Careers / hiring
  34. Weaviate — Co-founder Bob bio
  35. Weaviate — Co-founder Etienne bio
  36. Microsoft — Bob van Luijt interview
  37. FOSDEM — Etienne Dilocker
  38. Weaviate — Research authors
  39. arXiv — StructuredRAG
  40. Pinecone — Pricing
  41. Qdrant — Pricing
  42. Zilliz — Cost

Research Notes & Confidence Labels

  • Confirmed: Company name, founding year, headquarters, founders' current roles, product capabilities, current pricing, major releases and disclosed funding rounds are supported by company documentation or strong contemporaneous reporting.
  • Estimated / third-party: The 2023 $200M valuation is a contemporaneous reported valuation, not a current market valuation. Third-party employee/revenue databases were not used to manufacture current financial figures.
  • Not Publicly Available: Current revenue, ARR, profit/loss, current valuation, founder net worth, complete board roster, detailed private infrastructure, and several personal biographical fields.
  • Analysis: Competitive conclusions, SWOT, strategic interpretation and future predictions are analytical judgments derived from the documented product and market trajectory and are explicitly separated from facts.