--- title: Weaviate Reviews meta_title: 'Weaviate Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 45 reviews by the users' company size, role or industry to find out how Weaviate works for a business like yours. aggregate_rating: rating_value: 4.4 review_count: 45 scale: '5' date_modified: '2026-08-13' parent_category: name: Database Software url: https://www.g2.com/categories/database-software --- # Weaviate Reviews **Vendor:** Weaviate **Category:** [Vector Database Software](https://www.g2.com/categories/vector-database) **Average Rating:** 4.4/5.0 **Total Reviews:** 45 ## About Weaviate Weaviate is an open-source vector database that helps developers build and scale AI applications such as semantic search, retrieval-augmented generation (RAG), recommendation, and AI agents. It stores data objects together with their vector embeddings, allowing applications to combine vector similarity with keyword and structured filters (hybrid search) to retrieve relevant results across billions of objects. Weaviate is built for developers and teams working with modern AI workloads, from individual projects to enterprise production systems. It integrates across the AI stack with pre-built modules for common embedding and large language model (LLM) providers, including OpenAI, Anthropic, Cohere, Google, AWS Bedrock, and Hugging Face, so teams can bring their own models or use Weaviate Embeddings. Key capabilities include: - Vector and hybrid search that combines semantic similarity with keyword matching and metadata filters - Built-in vectorization and pluggable embedding and LLM providers, so models can be changed without re-architecting - Native multi-tenancy with data isolation, and role-based access control (RBAC) - Query Agent, which converts natural-language questions into database queries and returns answers with source citations - Engram, a managed memory service that gives AI agents persistent, personalized memory Weaviate can be self-hosted under the open-source BSD-3-Clause license or run as a fully managed service through Weaviate Cloud on AWS, Google Cloud, and Azure, with a Bring Your Own Cloud (BYOC) option that runs Weaviate inside a customer's own cloud environment. Weaviate Cloud includes a free tier with no credit card required and no expiration, along with paid tiers for teams moving into production. For organizations with compliance requirements, Weaviate is SOC 2 Type II certified, with HIPAA compliance available for regulated workloads. ## Weaviate Reviews ### 1. Weaviate’s Powerful Vector Search with a Developer-Friendly, Scalable API **Rating:** 4.5/5.0 stars **Reviewed by:** Atharva S. | SRE, Mid-Market (51-1000 emp.) **Reviewed Date:** August 04, 2026 **What do you like best about Weaviate?** What I like best about Weaviate is its powerful vector search capabilities combined with a flexible, developer-friendly architecture for building AI-powered applications. The platform makes it easy to store, index, and retrieve embeddings while supporting hybrid search, semantic search, and integrations with popular AI frameworks. I also appreciate its scalability, intuitive API, and open-source foundation, which provide both flexibility and transparency for production deployments. Overall, Weaviate simplifies the development of intelligent search and retrieval systems, accelerates AI application development, and delivers excellent performance for large-scale vector data. **What do you dislike about Weaviate?** One area where Weaviate could improve is offering more advanced monitoring, performance analytics, and cluster management tools for large-scale production deployments. While the platform is highly flexible and feature-rich, optimizing indexes and tuning retrieval performance for complex workloads can require additional experimentation. I'd also like to see broader integrations with more developer and observability tools, richer documentation for advanced use cases, and more granular cost and resource management capabilities. Overall, the experience has been very positive, but enhanced observability, deeper operational tooling, and expanded enterprise features would make Weaviate even more valuable. **What problems is Weaviate solving and how is that benefiting you?** Weaviate solves the challenge of storing, indexing, and retrieving vector embeddings for AI applications, making semantic search and retrieval-augmented generation (RAG) significantly easier to implement at scale. Instead of building and managing custom vector search infrastructure, it provides a scalable database with hybrid search, filtering, and AI integrations in a single platform. This has simplified the development of intelligent search systems, improved the relevance of AI-powered results, reduced infrastructure complexity, and accelerated the deployment of production-ready AI applications. As a result, it has increased development efficiency, improved search quality, and enabled faster delivery of AI-powered features. ### 2. Fast, Intuitive Vector + Semantic Search with a Strong Developer Experience **Rating:** 4.5/5.0 stars **Reviewed by:** Nikita J. | Programmer, Information Technology and Services, Enterprise (> 1000 emp.) **Reviewed Date:** July 30, 2026 **What do you like best about Weaviate?** What I like most about Weaviate is how it combines vector search with AI-powered semantic search in a way that’s easy to integrate into modern applications. The API feels well designed, so it’s straightforward to build intelligent search and retrieval features without spending a lot of time dealing with complex infrastructure. The user interface is clean and intuitive, which makes it simple to manage collections, inspect data, and experiment with different search queries. Performance has been consistently fast, even when working with large datasets, and the results are highly relevant because they rely on semantic understanding rather than basic keyword matching. Another major strength is the integration ecosystem. Connecting Weaviate with embedding models, LLMs, and popular development frameworks is smooth, and it helps speed up AI application development. That flexibility also made it easier for me to prototype and deploy retrieval-augmented generation (RAG) workflows. From a business perspective, Weaviate has reduced development time by offering built-in capabilities for vector indexing, hybrid search, and AI-powered retrieval, instead of forcing me to stitch together multiple separate tools. The documentation and onboarding experience are well structured, so new users can become productive quickly. When I needed guidance, both the documentation and community resources were genuinely helpful. Overall, Weaviate delivers strong performance, a great developer experience, and powerful AI capabilities that make building intelligent search applications faster and more efficient. **What do you dislike about Weaviate?** My overall experience with Weaviate has been positive, but there are a few areas where it could be stronger. The user interface is functional, yet it would benefit from better visibility into index health, query performance, and cluster status—ideally through more detailed dashboards and monitoring tools. In addition, some advanced configuration options still require frequent trips to the documentation, which can slow down newer users. Weaviate integrates well with many AI models and frameworks, but setting up more advanced integrations or migrating between embedding models can take extra effort. More built-in templates, guided configuration, and integration wizards would make the setup process smoother and reduce friction. Performance is generally excellent; however, large-scale indexing or complex hybrid search workloads may require careful resource tuning to get the best results. More automatic optimisation, along with clearer scaling recommendations, would help reduce operational overhead. On the pricing side, costs can rise as datasets and infrastructure needs grow. Additional cost-management tools and better usage insights would help organisations forecast and optimise spending more effectively. The documentation is comprehensive, but beginners may still find some advanced topics difficult to navigate. More step-by-step tutorials, end-to-end implementation examples, and practical troubleshooting guides would make onboarding easier. Finally, while the AI capabilities are powerful, more built-in evaluation tools, explainability features for search results, and simpler model management would make it easier to optimise AI applications and understand retrieval quality. Overall, these improvements would further strengthen an already capable platform. **What problems is Weaviate solving and how is that benefiting you?** Weaviate addresses the challenge of efficiently storing, indexing, and searching unstructured data with vector embeddings, which makes it much easier to build AI-powered applications. Rather than relying on traditional keyword-based search, it supports semantic search that surfaces more relevant results based on meaning and context. In my work, this has cut down the time needed to develop intelligent search and retrieval features. It has also streamlined retrieval-augmented generation (RAG) workflows by combining vector search with large language models, which improves the accuracy and relevance of AI-generated responses. On top of that, its scalable architecture and fast query performance have helped keep the user experience responsive as datasets grow. Overall, Weaviate has boosted my development productivity, reduced the complexity of managing AI search infrastructure, and made it easier to deliver accurate, context-aware applications with less engineering effort. ### 3. Gold-Standard Hybrid Search for Highly Accurate RAG Retrieval **Rating:** 5.0/5.0 stars **Reviewed by:** Nirmal K. | Manager, E-Learning, Small-Business (50 or fewer emp.) **Reviewed Date:** August 12, 2026 **What do you like best about Weaviate?** It natively supports hybrid search, allowing developers to combine dense vector search (for semantic meaning) with traditional keyword search (BM25). This is widely considered the gold standard for retrieving highly accurate context in RAG applications. **What do you dislike about Weaviate?** Instead of using standard SQL, Weaviate's primary query language is a custom implementation of GraphQL. While powerful for complex graph relationships, developers accustomed to traditional relational databases often report a steep learning curve. **What problems is Weaviate solving and how is that benefiting you?** Beyond just storing isolated vectors, Weaviate allows you to define cross-references and relationships between data objects (similar to a graph database), making it easier to represent highly complex, interconnected enterprise data. ### 4. Weaviate’s Hybrid Search Makes Semantic Video Discovery Effortless **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.) **Reviewed Date:** August 10, 2026 **What do you like best about Weaviate?** What stands out most about weaviate is its native hybrid search and multi-tenancy capabilities, which make combining BM25 keyword matching with dense vector search effortless. It allows us to deliver ultra-fast semantic video discovery and personalized viewer recommendations across huge OTT metadata catalogs. **What do you dislike about Weaviate?** Setting up self hosted clusters and tuning HNSW index memory parmeters for large scale video catalogs requires significant infrastructure overhead. Additionally, breaking SDK changes between major version updates can require unexpected maintenance for our automated OTT metadata ingestion pipelines. **What problems is Weaviate solving and how is that benefiting you?** Weaviate solves the challenge of organizing and searching millions of unstructured video transcripts, viewer logs, and show metadata in real time. It benefits our OTT platform my powering instant, highly accurate semantic search and personalized content recommendations, which keeps subscribers engaged longer. ### 5. Weaviate Makes Semantic + Traditional Search Fast, Scalable, and Developer-Friendly **Rating:** 4.5/5.0 stars **Reviewed by:** Nanthakumar M. | Information Technology Analyst, Mid-Market (51-1000 emp.) **Reviewed Date:** June 23, 2026 **What do you like best about Weaviate?** I like Weaviate's ability to combine semantic vector search with traditional search capabilities in a scalable, developer-friendly platform. It makes building AI and retrieval-augmented applications much faster and more effective. **What do you dislike about Weaviate?** The main drawback is the initial learning curve. Understanding vector search concepts, embeddings, and configuration can take time for new users, although it becomes easier with experience. **What problems is Weaviate solving and how is that benefiting you?** Weaviate solves the problem of finding relevant information in large amounts of unstructured data by using semantic search instead of relying only on exact keyword matches. This helps retrieve more accurate and context-aware results. For me, the benefit is faster access to relevant information, improved search quality, and the ability to build AI-powered applications such as knowledge bases, chatbots, and retrieval-augmented generation (RAG) systems more efficiently. ### 6. Efficient Vector Searches with Easy Integration **Rating:** 5.0/5.0 stars **Reviewed by:** Tayyab N. | Lead Machine Learning Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** July 21, 2026 **What do you like best about Weaviate?** I really like Weaviate for its fast and efficient vector search capabilities. The ease of use and ease of data import and querying are impressive, thanks to its extensive Python SDK, which is crucial for my application integrations. It's great for the custom Python applications I build, especially when using FastAPI. **What do you dislike about Weaviate?** I find the initial learning curve a bit steep, but it's worth the effort. **What problems is Weaviate solving and how is that benefiting you?** I use Weaviate for fast and efficient vector search, and its ease of use, including data import and querying, is a big plus. The extensive Python SDK is crucial for integrating Weaviate functionalities into my custom applications. ### 7. Outstanding RAG and support for customer & community **Rating:** 5.0/5.0 stars **Reviewed by:** Carlos F. | ハッカー, Small-Business (50 or fewer emp.) **Reviewed Date:** June 10, 2025 **What do you like best about Weaviate?** Weaviate stores the data objects as vectors in multidimensional space, so you can search and find relationships between the data based on semantic meaning, resulting in great and stable accuracy. Their customer support is impeccable, and there's a great community environment too in Slack. **What do you dislike about Weaviate?** Could focus more on AI docs for direct API access. **What problems is Weaviate solving and how is that benefiting you?** Weaviate is creating embeddings, storing them in a vector DB and retrieving them when performing a semantic search for generative augmentation, together as self-contained RAG in Weaviate. I've also used their transformation agent and I was impressed about the quality of the answers, even though I made some mistakes in the setup at the time. I subscribe to their cloud instance so that I don't have to deal with user data on my servers, and a great deal of RAG infra moving parts in general. It has reduced cost at scale, and it's easy to provision and configure. ### 8. Clean Interface and Straightforward Setup Make Vector Database Implementation Simple **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Computer Software | Small-Business (50 or fewer emp.) **Reviewed Date:** May 28, 2025 **What do you like best about Weaviate?** The interface is impressively clean and intuitive, making it easy to navigate even as a newcomer to vector databases. The setup and testing process is refreshingly straightforward - you can get up and running quickly without wrestling through complex configuration steps. What really stands out is their commitment to continuous improvement; they're consistently rolling out new products and features that genuinely make the developer experience easier. Their free office hours, workshops, and events are incredibly valuable for newcomers - having direct access to experts who can answer questions and provide guidance makes the learning curve much more manageable. The integration process feels well-thought-out, and the documentation guides you through implementation without unnecessary complexity. **What do you dislike about Weaviate?** As someone just getting started, it's hard to identify major pain points yet. The learning curve for vector database concepts and who to use them themselves can be steep if you're new to the space, though that's more about the technology category than Weaviate specifically. **What problems is Weaviate solving and how is that benefiting you?** As someone just beginning to explore vector databases, I'm still in the early stages of understanding how Weaviate will fit into my application architecture. From what I've learned so far, Weaviate appears to solve the challenge of efficiently storing and retrieving high-dimensional data for AI applications - particularly for semantic search, recommendation systems, and RAG (Retrieval Augmented Generation) implementations. While I haven't yet implemented a full production use case, the benefit I'm already seeing is how Weaviate makes vector database concepts more accessible to developers like me who are new to this space. Their clean interface and educational resources (office hours, workshops) are helping me understand not just how to use their product, but how vector databases can enhance applications with more intelligent search and data retrieval capabilities. I'm exploring use cases around improving search functionality in my applications and potentially implementing AI-powered features, but I'm still in the learning phase of understanding where vector databases provide the most value compared to traditional databases. ### 9. Easy to use and amazing customer support **Rating:** 5.0/5.0 stars **Reviewed by:** Katerina T. | CEO, Small-Business (50 or fewer emp.) **Reviewed Date:** April 01, 2025 **What do you like best about Weaviate?** Weaviate was so easy to integrate and use. The documentation is easy to follow, the Weaviate AI is super helpful for navigating common problems, and their customer support is next level! Facing a challenge is somehow a pleasant experience - you get a swift response and an expert perspective on your problem. **What do you dislike about Weaviate?** It would've been great to have PHP instructions in the docs, or just simple HTTP requests. **What problems is Weaviate solving and how is that benefiting you?** We're fully replacing our keyword searches to find relevant data for given criteria, with a smart semantic search. Weaviate returns the closest matches and you can further tune them using their RAG functionality by passing the results through Generative AI. It reduces hours of manual work and improves our internal processes immensely. ### 10. A very good product with a great support team **Rating:** 4.5/5.0 stars **Reviewed by:** Zahir L. | Head of Development and Architecture, Enterprise (> 1000 emp.) **Reviewed Date:** February 06, 2025 **What do you like best about Weaviate?** The responsiveness of the support team and the ability to speak to real people about issues you may be having. The product has great functionality and enables quick wins in terms of integrating to our systems **What do you dislike about Weaviate?** Release process is usually smooth but there have been some "undocumented" gotchas. But team helped to resolve. **What problems is Weaviate solving and how is that benefiting you?** Ability to connect to all the major LLM platforms for embedding as well as genai without needed additional frameworks where the use cases are simple. Well supported by the other frameworks for more complex cases. ### 11. great product, even better tech support **Rating:** 5.0/5.0 stars **Reviewed by:** Keith S. | Sr Systems Administrator, Small-Business (50 or fewer emp.) **Reviewed Date:** May 15, 2025 **What do you like best about Weaviate?** The tech support is fantastic: ticket ownership, fast turn-around times, professional, personable, and proactively willing share product knowledge with the end user to better help them understand the Weaviate product. Thank you. **What do you dislike about Weaviate?** Nothing. We had one issue with our serverless cloud and Weaviate support assigned four engineers to quickly resolve the issue. **What problems is Weaviate solving and how is that benefiting you?** Vector database tied to our AI workloads. ### 12. A great DB solution for the AI Era **Rating:** 5.0/5.0 stars **Reviewed by:** Ziwar M. | Associate Partner, Technology, Small-Business (50 or fewer emp.) **Reviewed Date:** February 05, 2025 **What do you like best about Weaviate?** I like the API first/multi tenancy and ease of use of Weaviate. I use it not only as a vector database, I made a decision to use it as the core database for the entire app. **What do you dislike about Weaviate?** There are some features they should enable in the cloud console **What problems is Weaviate solving and how is that benefiting you?** The automatic embedding/batch operations/multi tenancy ### 13. Great Vector db **Rating:** 5.0/5.0 stars **Reviewed by:** Hari M. | Senior Engineering Manager, Enterprise (> 1000 emp.) **Reviewed Date:** February 27, 2025 **What do you like best about Weaviate?** Participated in workshop by Weaviate in Dallas and these guys know what they are doing and built an amazing product. The hand holding we had during the session is amazing. **What do you dislike about Weaviate?** Cant think of any!, It was both great education and we will explore of feasibility. **What problems is Weaviate solving and how is that benefiting you?** We are not using it yet. I will discuss with out data science team if there is a scope and inclination. ### 14. AI Bootcamp (Dallas): GenAI in Production **Rating:** 5.0/5.0 stars **Reviewed by:** Jeanine K. | Disability Examiner-Workforce of Absence, Small-Business (50 or fewer emp.) **Reviewed Date:** February 27, 2025 **What do you like best about Weaviate?** I really enjoyed learning about Query agents, transformation agent and personalized agent **What do you dislike about Weaviate?** Nothing, everything was spectacular I enjoyed all the guest speakers **What problems is Weaviate solving and how is that benefiting you?** I am new with Weaviate but hope to use all the knowledge shared to further advance my learning within AI ### 15. I really like idea os saas vector db **Rating:** 5.0/5.0 stars **Reviewed by:** Anton I. | IT 24/7, Enterprise (> 1000 emp.) **Reviewed Date:** December 02, 2024 **What do you like best about Weaviate?** I just started to investigate. Thanks for a cool solution **What do you dislike about Weaviate?** Nothing but i will find :-). I promise to do that **What problems is Weaviate solving and how is that benefiting you?** Commercial ai base solution ### 16. Empowering AI with Versatility **Rating:** 5.0/5.0 stars **Reviewed by:** Rajesh M. | Mid-Market (51-1000 emp.) **Reviewed Date:** November 24, 2023 **What do you like best about Weaviate?** Weaviate proves to be user-friendly, with a well-designed interface that facilitates easy navigation. The platform's intuitive nature makes it accessible for both beginners and experienced users. Weaviate's customer support is responsive and helpful. The support team is quick to address queries, and the community forums provide an additional resource for collaborative problem-solving. It becomes an integral part of our workflow, especially for projects that demand advanced AI capabilities. Its reliability and consistent performance contribute to its frequent use in our AI development projects. The platform's flexibility ensures compatibility with a wide range of applications and use cases. The implementation process is smooth. **What do you dislike about Weaviate?** While Weaviate excels in many aspects, there's room for improvement in terms of documentation clarity. Some aspects of implementation might be clearer with more detailed examples and use cases. **What problems is Weaviate solving and how is that benefiting you?** Weaviate is a pivotal tool in addressing the complexities associated with unstructured data, fostering innovation in AI applications, and contributing to more effective and data-driven decision-making within the business context. ### 17. Easy to use and powerful vector database **Rating:** 5.0/5.0 stars **Reviewed by:** Beato B. | AppDev Team Lead, Small-Business (50 or fewer emp.) **Reviewed Date:** November 23, 2023 **What do you like best about Weaviate?** Weaviate is such a joy to use. I got a cluster set up in a couple of hours. Very good documentation, performant, a good level of abstraction where I don't feel like things are hand-wavy. It's flexible enough that I get to use my IR and ML knowledge and feel quite in control of how the search performs. I can use my own embedding model, reranker, and tune hybrid search to suit my usecase. **What do you dislike about Weaviate?** It could be cheaper! But it's cheaper than another competitor I tried. **What problems is Weaviate solving and how is that benefiting you?** I don't have to 1) set everything up. In the past I had to spin up an Elasticsearch instance, manage my own embeddings, do ANN and combine them myself. 2) tweak the code so searches run very quickly, which can take a while 3) manage my embeddings and index when my data changes I'd much rather focus on the other logic because weaviate got things right. ### 18. Advanced Open Source Vector Database **Rating:** 4.5/5.0 stars **Reviewed by:** Ronit K. | Small-Business (50 or fewer emp.) **Reviewed Date:** November 21, 2023 **What do you like best about Weaviate?** Setting up an AI client is a complex task. Weaviate makes it possible to try different LLMs combined with hybrid search. This way we get the best of both worlds. We can make inferences based on more traditional search and help the model churn out better results. Weaviate can be run locally, on-premise or in the cloud. This is really useful for a large number of use-cases. It also provides a clear pathway in case we want to move away from Weaviate Cloud. **What do you dislike about Weaviate?** So far our greatest challenge has been to create a chat like interface with Weaviate. I am sure it's possible but there are no official guides around it. Maybe something on the lines of Assistants API provided by OpenAI would be really useful. **What problems is Weaviate solving and how is that benefiting you?** We have a large corpus of ancient Hindu scriptures. Weaviate helps us to 'see-through' this data and find interesting results, which were simply not possible before. ### 19. Out of the box solution of Vector Databases **Rating:** 4.5/5.0 stars **Reviewed by:** Maxime H. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** November 21, 2023 **What do you like best about Weaviate?** I appreciate Weaviate's efficiency as a Vector Database, offering seamless integration with LLM models. Its fast computation, coupled with the capabilities for semantic search and RAGs, is admirable. Moreover, its out-of-the-box computing service is not only efficient but also reasonably priced, making it an excellent choice for various applications. It's encouraging to see its continuous development and the growing, supportive community that accompanies it, with a very competent support team. **What do you dislike about Weaviate?** A challenge of using Weaviate is its steep learning curve, especially for those new to the field, requiring a fair amount of technical programming skills to fully utilize its features. Once you reach it, the possibilities are endless! **What problems is Weaviate solving and how is that benefiting you?** Weaviate addresses the need for efficient data management and retrieval in large-scale applications. By utilizing vector databases, it enables rapid, semantic-based search and integrates smoothly with large language models, enhancing data interaction capabilities. This has been immensely beneficial for me in terms of time efficiency and accuracy in data handling, particularly in complex queries where context and nuance are crucial. ### 20. Weaviate Cloud Services is the best vector store option for both novices and advanced users. **Rating:** 5.0/5.0 stars **Reviewed by:** Ron P. | Owner, Small-Business (50 or fewer emp.) **Reviewed Date:** November 20, 2023 **What do you like best about Weaviate?** As a novice only a few short months ago, I needed a way to impement embeddings which was easy and relatively inexpensive. WCS came through on both of those for me. In addition, as I began to gain more experience, I was able to appreciate more the vast array of embed and retrieval options WCS had to offer. Now, as a grizzled veteran now deploying my 3rd RAG system, I am extremely happy with the decision I made early on to go with WCS. Integrating into my code as very easy. Their support is great and timely, and they appear to spend a good amount of effort continuously improving an already superior system. In RAG, your answers are only ever going to be as good as the documents your model is given to analyze, and I have to say that WCS cosine similarity searches have consistently given me back the best documents for the best answers. **What do you dislike about Weaviate?** I find it necessary to do queries in the WCS dashbord to both design processing code as well as troubleshoot -- and just to see what's in my vector store. The query syntax, while not terribly difficult, isn't the most intuitive. It sometimes takes a few minutes to go back and research how to construct the specific queries you need. It would be helpful if the queries you create are stored in the dashboard. Unfortunately WCS has a bad habit of deletiing them, forcing you to have to go back and re-create them every time you need them. That's actually my biggest pet peeve. **What problems is Weaviate solving and how is that benefiting you?** The biggest problem in RAG is retrieving the documents in your vector store which are most relevant to your prompt. In my experience so far, WCS excels at this. I am working with 3 different datasets which require slightly differenc configurations, and I am able to accomodate them all with WCS. And, again, I have to point out that one of the big hurdles starting out was the cost. The next best competitor was way more expensive than I could afford, especially in the beginning. WCS pricing made this option affordable, thus making it possible to explore and build upon. ### 21. Ease of use - Was up and running in a few hours. **Rating:** 5.0/5.0 stars **Reviewed by:** Babu M. | Chief Executive Officer, Small-Business (50 or fewer emp.) **Reviewed Date:** November 20, 2023 **What do you like best about Weaviate?** Weaviate is the cost-effective and fastest way to get your Vector Indexing and Vector Similarity search implemented for your platform! We were up and running in a few hours. The support team on Slack is very helpful. A 10/10 for the product and people behind it **What do you dislike about Weaviate?** Automatic updates would be helpful atleast on the Weaviate managed cloud instances. **What problems is Weaviate solving and how is that benefiting you?** Whautomate - our platform empowers small and medium businesses to provide omnichannel customer experiences through our custom GPT-powered AI chatbot, ensuring seamless AI-to-human transfer across WhatsApp, Telegram, Instagram, Messenger, and Live Chat, all at an affordable price. We owe our efficiency to Weaviate's multi-tenanted vector database, which seamlessly processes and searches embeddings, making our services possible. ### 22. Great vector database **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Marketing and Advertising | Small-Business (50 or fewer emp.) **Reviewed Date:** November 20, 2023 **What do you like best about Weaviate?** It's performant, easy to use, and has lots of great features. It's also a huge plus that it has cloud hosting to get started but is also open source and can be self-hosted. The cloud hosting was easy to set up and integrate with and includes various handy tools like the ability to run GraphQL queries against the data directly from the cloud console. Docs are also excellent, and aside from one issue it has been very robust and reliable. **What do you dislike about Weaviate?** It definitely feels like a young product (which it is), especially the cloud hosting which feels a little barebones. We were also affected by one substantial bug which resulted in broken keyword search (which admittedly is a secondary feature for most Weaviate users). On the plus side, I had some fairly technical questions about how to recover from the bug and received excellent support through Slack. **What problems is Weaviate solving and how is that benefiting you?** We use Weaviate as the database for a RAG/content generation app. ### 23. Fantastic vector database **Rating:** 5.0/5.0 stars **Reviewed by:** David W. | Small-Business (50 or fewer emp.) **Reviewed Date:** November 20, 2023 **What do you like best about Weaviate?** It was very easy to get started with weaviate, the pricing is simple and they take care of importing models from huggingface for us and provisioning the db instances so we can concentrate on building our product. We were able to quickly integrate weaviate with our existing stack using the python libraries. The documentation is good and we have been impressed by the support we received for our more unusual use cases. There is an active slack community and we've met several people from the team and they've all been very helpful. What's more it's all open source so we feel safe choosing Weaviate for a cricical part of our tech stack. **What do you dislike about Weaviate?** The only drawback I can think of is that recall speed can be slow with some queries (e.g. meta filters). I believe this is being worked on though. **What problems is Weaviate solving and how is that benefiting you?** Weaviate makes it possible for us to retrieve embedded products at scale (10s of millions of items). ### 24. Ease of use and reliability **Rating:** 5.0/5.0 stars **Reviewed by:** Justin M. | Small-Business (50 or fewer emp.) **Reviewed Date:** November 17, 2023 **What do you like best about Weaviate?** Our team has been using Weabiate for about six months. It was very easy to implement a development and production environment and integrate into our AI application. Their customer success team reached out to us to check in but we did not have any issues to address. Weaviate DB is used in our production application daily and has not had any uptime or reliability issues. **What do you dislike about Weaviate?** The billing system is a little bit clunky and it is not easy to see past invoices via the website. **What problems is Weaviate solving and how is that benefiting you?** Weaviate allows us to quicky find and rank related documents that are used to answer/analyze a user's questions. ### 25. Efficient Vector Storage **Rating:** 5.0/5.0 stars **Reviewed by:** Brayden L. | Small-Business (50 or fewer emp.) **Reviewed Date:** November 17, 2023 **What do you like best about Weaviate?** I highly recommend this platform as a fully capable vector database solution. I have used Weaviate for everything from RAG to simple data storage, and find it easy to use. **What do you dislike about Weaviate?** It does not have a very modern API/interface, but I feel this is balanced by its capability. **What problems is Weaviate solving and how is that benefiting you?** I am utilizing Weaviate for data storage and RAG ### 26. Easy to get up and running and loaded with convenient features **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Small-Business (50 or fewer emp.) **Reviewed Date:** November 20, 2023 **What do you like best about Weaviate?** * Simple to get a local dev env working with docker * Their new v4 python sdk makes things way easier to work with * Built-in hybrid search with the options to tailor it to exactly what you need is a huge plus * Reliable, been using it in production for 6 months without a hiccup **What do you dislike about Weaviate?** * It's a fast moving product, so documentation could use some more frequent updating. **What problems is Weaviate solving and how is that benefiting you?** We needed a hybrid vector search that came out of the box and was quick to implement. ### 27. We love it at Clirnet **Rating:** 5.0/5.0 stars **Reviewed by:** Deborishi G. | Business Analyst, Mid-Market (51-1000 emp.) **Reviewed Date:** November 17, 2023 **What do you like best about Weaviate?** Ability to create knowledge graphs and ease of handling embeddings **What do you dislike about Weaviate?** Pricing could be a little more economical **What problems is Weaviate solving and how is that benefiting you?** We are a healthcare company, we use weaviate to embed MeSH keywords ### 28. We moved to Weaviate from Pinecone **Rating:** 5.0/5.0 stars **Reviewed by:** Siva S. | Founding Member, Small-Business (50 or fewer emp.) **Reviewed Date:** November 17, 2023 **What do you like best about Weaviate?** We moved to Weaviate because of the ease of integration with our core platform and the performance of indexes and the retrieval latency. And the ease of implementation using Lyzr SDKs has been a hit with enterprise customers. **What do you dislike about Weaviate?** The Weaviate Cloud UI needs an overhaul. **What problems is Weaviate solving and how is that benefiting you?** Index and search vectors faster and at scale. ### 29. One of the best open source vector db **Rating:** 4.5/5.0 stars **Reviewed by:** Rajan G. | Machine Learning Engineer II, Mid-Market (51-1000 emp.) **Reviewed Date:** October 12, 2023 **What do you like best about Weaviate?** 1. It's an open source vector db 2. Good community support 3. Allowing to integrate with various embeddings models **What do you dislike about Weaviate?** 1. Vector search criterias can be improved 2. Custom embedding image addition is complex **What problems is Weaviate solving and how is that benefiting you?** 1. It is open source and free 2. It can handle vector size of more than 5-10 million - [View Weaviate pricing details and edition comparison](https://www.g2.com/products/weaviate/reviews?filters%5Bnps_score%5D%5B%5D=5§ion=pricing&secure%5Bexpires_at%5D=2026-08-13+11%3A50%3A50+-0500&secure%5Bsession_id%5D=fb0d5917-df18-4dac-91ef-81bd8992d2f1&secure%5Btoken%5D=fcb5bff0a28d2e3e38a8e190617aa7821f200aafb1160dca302d4767677b80ea&format=llm_user) ## Weaviate Integrations - [LlamaIndex](https://www.g2.com/products/llamaindex/reviews) - [OpenAI SDK](https://www.g2.com/products/openai-sdk/reviews) - [Python](https://www.g2.com/products/python/reviews) ## Weaviate Features **Additional Functionality** - Tagging - Natural Language Processing - Data Extraction - Multi-Language - Predictive Analytics - Drag & Drop - Speech Recognition - Reporting/Analytics - Data Storage Management - Virtual Personal Assistant (VPA) - AI Copilot - Customer Segmentation - Collaboration Tools - Data Import/Export - Generative AI - For eCommerce - Role-Based Permissions - Customizable Branding - Search/Filter - Monitoring - Document Management - API - Data Visualization - Trend Analysis - Machine Learning - Access Controls/Permissions - Alerts/Escalation - Performance Metrics - Real-Time Data - Third-Party Integrations - Mobile App - Multiple Data Sources - For Sales Teams/Organizations - Sentiment Analysis - Activity Dashboard - Chatbot - Workflow Automation **Additional Functionality** - Code Generation - Text to Image - Generative AI - API - Natural Language Processing - Virtual Characters and Avatars - Content Generation - Personalization and Recommendation - Conditional Generation - Transformer Model - Automated Image & Video Editing - Interactive and Co-Creative Systems - Text Summarization - Data Augmentation - Variation Autoencoder Models - Adversarial Training - Transfer Learning and Fine-tuning - Simulation and Scenario Generation - Creative Design - AI Copilot - Prompt Engineering - Foundation Model **Data Indexing** - Semantic Search - Indexing Data **Retrieval intelligence - AI Search & Retrieval Infrastructure Platforms** - Advanced relevance tuning - Query understanding & expansion - Multistage retrieval & re-ranking - Context-aware & personalized search **Embedding & model management - AI Search & Retrieval Infrastructure Platforms** - Embedding versioning & lifecycle management - Multimodal search support - Pluggable embedding & LLM providers **Filters** - Accurate Search - Single Stage Filtering - Vector Database **LLM retrieval & RAG optimization - AI Search & Retrieval Infrastructure Platforms** - Retrieval pipeline orchestration - LLM-aware retrieval optimization - Hybrid retrieval strategy optimization **Data Enrichment & Index Intelligence - AI Search & Retrieval Infrastructure Platforms** - Incremental & streaming index updates - Built-in data enrichment **Security & governance - AI Search & Retrieval Infrastructure Platforms** - Fine-grained access controls - Data residency & retention policies - Audit logs & retrieval traceability **Operations, observability & reliability - AI Search & Retrieval Infrastructure Platforms** - Search analytics & relevance debugging - High availability & disaster recovery ## Top Weaviate Alternatives - [Pinecone](https://www.g2.com/products/pinecone/reviews) - 4.5/5.0 (56 reviews) - [Algolia](https://www.g2.com/products/algolia/reviews) - 4.5/5.0 (430 reviews) - [Supabase](https://www.g2.com/products/supabase-supabase/reviews) - 4.6/5.0 (173 reviews)