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Vector databases - a look at the AI database market with a comprehensive comparison matrix
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et_pb_button_1_tb_header_wrapper et_pb_button_alignment_center et_pb_module "> <a class="et_pb_button et_pb_button_1_tb_header et_pb_bg_layout_dark" href="https://objectbox.io/offline-first-mobile-database/">Get started</a> </div> </div> </div> </div> </div> </header> <div id="et-main-area"> <div id="main-content"> <div class="container"> <div id="content-area" class="clearfix"> <div id="left-area"> <article id="post-226084" class="et_pb_post post-226084 post type-post status-publish format-standard has-post-thumbnail hentry category-ai category-open-source category-vector-database tag-open-source tag-vector-database"> <div class="entry-content"> <div class="et-l et-l--post"> <div class="et_builder_inner_content et_pb_gutters3"><div class="et_pb_section et_pb_section_0 et_pb_fullwidth_section et_section_regular" > <section class="et_pb_module et_pb_fullwidth_header et_pb_fullwidth_header_0 et_pb_text_align_left et_pb_bg_layout_dark"> <div class="et_pb_fullwidth_header_container left"> <div class="header-content-container center"> <div class="header-content"> <h1 class="et_pb_module_header">Vector databases - a look at the AI database market </h1> <span class="et_pb_fullwidth_header_subhead">⭐ What are vector databases? ⭐ What do you need them for? ⭐ Who is in the market? (Updated Oct 2024)</span> <div class="et_pb_header_content_wrapper"><p>Includes a comparison matrix of vector database options like Pinecone, Milvus, Vespa, Vald, Chroma, Marqo AI, Weaviate, and Qdrant</p> <p><quillbot-extension-portal></quillbot-extension-portal></p></div> </div> </div> </div> <div class="et_pb_fullwidth_header_overlay"></div> <div class="et_pb_fullwidth_header_scroll"></div> </section> </div><div class="et_pb_section et_pb_section_1 et_section_regular" > <div class="et_pb_row et_pb_row_0"> <div class="et_pb_column et_pb_column_4_4 et_pb_column_0 et_pb_css_mix_blend_mode_passthrough et-last-child"> <div class="et_pb_module et_pb_text et_pb_text_0 et_pb_text_align_left et_pb_bg_layout_light"> <div class="et_pb_text_inner"><p><span style="font-weight: 400;">In 2023 we saw record fundings of vector database players vector database. Since then almost every general purpose database (like MongoDB, elastic, Orcale MySQL etc.) have added a Vector Search and related features, basically making all of the vector databases too. There is an ongoing discussion if pure players are superior, but as always, the right answer is: “it depends”. Any ways, the vector database market is stilly very hot in Q4 of 2024 🔥</span></p> <p><span style="font-weight: 400;">Of courese, everyone, not just investors, is interested in the booming AI market. While AI applications have dominated the news for quite some time, the infrastructure software that supports these applications, such as vector databases, has finally gained more spotlight. In the following, we’ll have a look at why vector databases are gaining attention and compare current vector database alternatives.</span><span style="font-weight: 400;"></span></p> <p><span style="font-weight: 400;"></span></p> <h2><span style="font-weight: 400;">What is a vector database? </span><quillbot-extension-portal></quillbot-extension-portal></h2></div> </div> </div> </div><div class="et_pb_row et_pb_row_1"> <div class="et_pb_column et_pb_column_4_4 et_pb_column_1 et_pb_css_mix_blend_mode_passthrough et-last-child"> <div class="et_pb_module et_pb_text et_pb_text_1 et_pb_text_align_left et_pb_bg_layout_light"> <div class="et_pb_text_inner"><p><span style="font-weight: 400;">A vector database stores vectors, or more precisely vector embeddings. A vector database therefore is a specialised type of database designed to store and manage large sets of vectors efficiently. However, the challenge and value are not derived from simply being able to store vectors. The value is created by the type of computations that can be run over the stored vector data and the speed with which these computations can be run, e.g. similarity searches.<span> </span></span></p> <p>Vector databases are essentially an important piece of the AI tech stack. <span style="font-weight: 400;">They can be used e.g. to give LLMs (Large Language Models) – or more broadly speaking, AI applications – a long-term memory and faster search and querying capabilities. Another important use case is RAG (Retrieval-Augmented Generation).</span></p></div> </div> </div> </div><div class="et_pb_row et_pb_row_2"> <div class="et_pb_column et_pb_column_4_4 et_pb_column_2 et_pb_css_mix_blend_mode_passthrough et-last-child"> <div class="et_pb_module et_pb_text et_pb_text_2 et_pb_text_align_left et_pb_bg_layout_light"> <div class="et_pb_text_inner"><p><span style="font-weight: 400;">To give some context: The most traditional databases, SQL databases, store data in rows and columns; graph databases store graphs and <a href="https://db-engines.com/en/ranking/object+oriented+dbms" target="_blank" rel="noopener">object databases store objects</a>.</span></p> <p><span style="font-weight: 400;">Because Large Language Models and AI applications rely on vector embeddings, vector databases are especially apt at supporting AI applications. </span></p> <p><span style="font-weight: 400;">Accordingly, vector databases are becoming a critical layer in the AI tech stack; they are sometimes also called “AI databases”. However, databases tend to converge over time, meaning that many databases support several different database models.</span></p> <p><quillbot-extension-portal></quillbot-extension-portal></p></div> </div><div class="et_pb_module et_pb_toggle et_pb_toggle_0 et_pb_toggle_item et_pb_toggle_close"> <h5 class="et_pb_toggle_title">What is a vector embedding?</h5> <div class="et_pb_toggle_content clearfix"><p><span style="font-weight: 400;">A vector embedding is a list of numbers that represent objects and relationships, allowing unstructured data (such as images) to be searched and used. Typically, Large Language Models (more precisely the underlying Machine Learning (ML) algorithms) are used to create these vectors. The ML algorithms analyse large amounts of data to learn how to represent complex / unstructured data in a lower dimensional space (as vectors).</span></p></div> </div><div class="et_pb_module et_pb_toggle et_pb_toggle_1 et_pb_toggle_item et_pb_toggle_close"> <h5 class="et_pb_toggle_title">What do vector databases have to do with nearest neighbour search?</h5> <div class="et_pb_toggle_content clearfix"><p><span style="font-weight: 400;">Searchability (making unstructured data usable) is at the heart of this concept. The nearest neighbour search is therefore a key concept in vector databases. The distance between vector embeddings expresses the similarity of the vectors (and thus the represented objects). Therefore, as you are searching for the most similar data, the so-called “nearest neighbour search” is a key concept and the time required to find the nearest neighbours is essential. </span></p></div> </div><div class="et_pb_module et_pb_toggle et_pb_toggle_2 et_pb_toggle_item et_pb_toggle_close"> <h5 class="et_pb_toggle_title">Do we need special vector databases?</h5> <div class="et_pb_toggle_content clearfix"><p><span style="font-weight: 400;">There is already a discussion going on about whether special vector databases are needed or do not warrant a new category in the database landscape. Instead, vector extensions of traditional databases could be supporting the AI market. Both are reasonable expectations, and time will tell. Notable databases that have already added a vector extension include e.g. redis and elasticsearch. <a href="https://objectbox.io/vector-embeddings-support-local-java-database/" target="_blank" rel="noopener">Additionally, more and more databases now allow storing vector types</a>.<br /></span></p></div> </div> </div> </div><div class="et_pb_row et_pb_row_3"> <div class="et_pb_column et_pb_column_4_4 et_pb_column_3 et_pb_css_mix_blend_mode_passthrough et-last-child"> <div class="et_pb_module et_pb_text et_pb_text_3 et_pb_text_align_left et_pb_bg_layout_light"> <div class="et_pb_text_inner"><h2><span style="font-weight: 400;">How does the vector database landscape look like?</span></h2> <p><span style="font-weight: 400;">To have a look at the current market situation, we are comparing the choices with the most traction, but excluding established players that have added vector capabilities to their existing database offering. Generally speaking we see a lot of very young companies, some companies that did pivot from their original specialization, and massive fundings. Please note: the table is not optimized to be readable on mobile or small screens (there just is a trade-off between providing the information and making it readable on every device).</span></p> <p><span style="font-weight: 400;">If you’re on mobile,<a href="https://objectbox.io/VectorDatabases2024-ForWeb.htm" target="_blank" rel="noopener" title="Vector Database Comparison Matrix"> use this link </a>to view a version that is readable on mobile.</span></p></div> </div> </div> </div> </div><div class="et_pb_section et_pb_section_2 et_pb_fullwidth_section et_section_regular" > <div class="et_pb_module et_pb_fullwidth_code et_pb_fullwidth_code_0"> <div class="et_pb_code_inner"><html xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:x="urn:schemas-microsoft-com:office:excel" xmlns="http://www.w3.org/TR/REC-html40"> <head> <meta http-equiv=Content-Type content="text/html; charset=windows-1252"> <meta name=ProgId content=Excel.Sheet> <meta name=Generator content="Microsoft Excel 15"> 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information was generated by Microsoft Excel's Publish as Web Page wizard.--> <!--If the same item is republished from Excel, all information between the DIV tags will be replaced.--> <!-----------------------------> <!--START OF OUTPUT FROM EXCEL PUBLISH AS WEB PAGE WIZARD --> <!-----------------------------> <div id="Vector Databases_3387" align=center x:publishsource="Excel"> <table border=0 cellpadding=0 cellspacing=0 width=2116 style='border-collapse: collapse;table-layout:fixed;width:1590pt'> <col width=30 style='mso-width-source:userset;mso-width-alt:1097;width:23pt'> <col width=130 style='mso-width-source:userset;mso-width-alt:4754;width:98pt'> <col width=49 style='mso-width-source:userset;mso-width-alt:1792;width:37pt'> <col width=71 style='mso-width-source:userset;mso-width-alt:2596;width:53pt'> <col width=82 style='mso-width-source:userset;mso-width-alt:2998;width:62pt'> <col width=88 style='width:66pt'> <col width=155 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width=49 style='width:37pt'></td> <td class=xl153387 width=71 style='width:53pt'></td> <td class=xl153387 width=82 style='width:62pt'></td> <td class=xl153387 width=88 style='width:66pt'></td> <td class=xl153387 width=155 style='width:116pt'></td> <td class=xl153387 width=116 style='width:87pt'></td> <td class=xl153387 width=206 style='width:155pt'></td> <td class=xl153387 width=221 style='width:166pt'></td> <td class=xl153387 width=88 style='width:66pt'></td> <td class=xl153387 width=88 style='width:66pt'></td> <td class=xl153387 width=88 style='width:66pt'></td> <td class=xl153387 width=154 style='width:116pt'></td> <td class=xl153387 width=185 style='width:139pt'></td> <td class=xl153387 width=88 style='width:66pt'></td> <td class=xl153387 width=88 style='width:66pt'></td> <td class=xl153387 width=99 style='width:74pt'></td> <td class=xl153387 width=66 style='width:50pt'></td> <td class=xl153387 width=24 style='width:18pt'></td> </tr> <tr height=86 style='height:64.5pt'> <td height=86 class=xl153387 style='height:64.5pt'></td> <td class=xl923387 width=130 style='width:98pt'>Name</td> <td class=xl923387 width=49 style='width:37pt'>Open Source</td> <td class=xl933387 width=71 style='border-left:none;width:53pt'>License</td> <td class=xl933387 width=82 style='border-left:none;width:62pt'>GitHub stars<span style='mso-spacerun:yes'> </span></td> <td class=xl933387 width=88 style='border-left:none;width:66pt'>Developed in (language)</td> <td class=xl933387 width=155 style='border-left:none;width:116pt'>Summary</td> <td class=xl933387 width=116 style='border-left:none;width:87pt'>Business Model</td> <td class=xl933387 width=206 style='border-left:none;width:155pt'>Embeds / Uses</td> <td class=xl933387 width=221 style='border-left:none;width:166pt'>founding date / first released date</td> <td class=xl933387 width=88 style='border-left:none;width:66pt'>In-memory Unterstützung</td> <td class=xl943387 width=88 style='border-left:none;width:66pt'>Sharding</td> <td class=xl943387 width=88 style='border-left:none;width:66pt'>Index Types</td> <td class=xl933387 width=154 style='border-left:none;width:116pt'>Consistency Model</td> <td class=xl933387 width=185 style='border-left:none;width:139pt'>Benchmarks (Performance?)</td> <td class=xl933387 width=88 style='border-left:none;width:66pt'>Approximate Nearest Neighbor (ANN) Vector Databases</td> <td class=xl933387 width=88 style='border-left:none;width:66pt'>Funding</td> <td class=xl933387 width=99 style='border-left:none;width:74pt'>Who's behind it</td> <td class=xl953387 width=66 style='border-left:none;width:50pt'>HQ in<span style='mso-spacerun:yes'> </span></td> <td class=xl663387 width=24 style='width:18pt'></td> </tr> <tr height=137 style='height:102.75pt'> <td height=137 class=xl153387 style='height:102.75pt'></td> <td class=xl853387 align=left width=130 style='width:98pt'><a href="https://objectbox.io/"><span style='color:blue;font-size:15.0pt; font-family:Roboto;mso-generic-font-family:auto;mso-font-charset:0'>ObjectBox</span></a></td> <td class=xl823387 width=49 style='width:37pt'>Y</td> <td class=xl723387 width=71 style='border-left:none;width:53pt'>Apache-2.0</td> <td class=xl723387 width=82 style='border-left:none;width:62pt'> </td> <td class=xl733387 align=left width=88 style='border-left:none;width:66pt'>C++, supports native language APIs in Java, Flutter / Dart, Swift, Python, GoLang, and C++</td> <td class=xl733387 align=left width=155 style='border-left:none;width:116pt'>ObjectBox is an on-device vector database for Edge AI on Mobile, IoT, Embedded and other commodity devices</td> <td class=xl733387 align=left width=116 style='border-left:none;width:87pt'>Free to use; paid <a href="https://objectbox.io/sync/">Data Sync</a></td> <td class=xl723387 width=206 style='border-left:none;width:155pt'>HNSW built and optimized from scratch for efficiency / speed on devices with limited resources</td> <td class=xl723387 width=221 style='border-left:none;width:166pt'>development of the initial on-device database started in 2015; released the vector search to become the first on-device vector database for productive use early in 2024</td> <td class=xl723387 width=88 style='border-left:none;width:66pt'>Y</td> <td class=xl973387 width=88 style='border-left:none;width:66pt'>N</td> <td class=xl743387 align=left width=88 style='border-left:none;width:66pt'>HNSW</td> <td class=xl733387 align=left width=154 style='border-left:none;width:116pt'>Transactionally safe, ACID</td> <td class=xl1053387 width=185 style='border-left:none;width:139pt'><u style='visibility:hidden;mso-ignore:visibility'> </u></td> <td class=xl723387 width=88 style='border-left:none;width:66pt'>Y</td> <td class=xl733387 align=left width=88 style='border-left:none;width:66pt'>Seed in 2018</td> <td class=xl733387 align=left width=99 style='border-left:none;width:74pt'>ObjectBox</td> <td class=xl1023387 width=66 style='border-left:none;width:50pt'>🇪🇺</td> <td class=xl663387 width=24 style='width:18pt'></td> </tr> <tr height=68 style='height:51.0pt'> <td height=68 class=xl153387 style='height:51.0pt'></td> <td class=xl843387 align=left width=130 style='width:98pt'><a href="https://www.marqo.ai/"><span style='color:blue;font-size:15.0pt; font-family:Roboto;mso-generic-font-family:auto;mso-font-charset:0'>Marqo AI</span></a></td> <td class=xl883387 width=49 style='width:37pt'>Y</td> <td class=xl893387 width=71 style='border-left:none;width:53pt'>Apache-2.0</td> <td class=xl893387 width=82 style='border-left:none;width:62pt'>2.8k ⭐</td> <td class=xl903387 align=left width=88 style='border-left:none;width:66pt'>Python</td> <td class=xl903387 align=left width=155 style='border-left:none;width:116pt'>A tensor-based cloud-native commercial Open Source search and analytics engine.</td> <td class=xl903387 align=left width=116 style='border-left:none;width:87pt'>Open SaaS</td> <td class=xl893387 width=206 style='border-left:none;width:155pt'>Tensor-based</td> <td class=xl893387 width=221 style='border-left:none;width:166pt'>❔</td> <td class=xl893387 width=88 style='border-left:none;width:66pt'> </td> <td class=xl963387 width=88 style='border-left:none;width:66pt'>Y</td> <td class=xl913387 align=left width=88 style='border-left:none;width:66pt'>HNSW</td> <td class=xl903387 width=154 style='border-left:none;width:116pt'> </td> <td class=xl903387 align=left width=185 style='border-left:none;width:139pt'>-</td> <td class=xl893387 width=88 style='border-left:none;width:66pt'> </td> <td class=xl903387 align=left width=88 style='border-left:none;width:66pt'>undisclosed preseed in May 2022</td> <td class=xl903387 align=left width=99 style='border-left:none;width:74pt'>S2Search Australia Pty Ltd</td> <td class=xl1013387 width=66 style='border-left:none;width:50pt'>🇦🇺</td> <td class=xl683387 width=24 style='width:18pt'></td> </tr> <tr height=102 style='height:76.5pt'> <td height=102 class=xl153387 style='height:76.5pt'></td> <td class=xl853387 align=left width=130 style='border-top:none;width:98pt'><a href="https://weaviate.io/"><span style='color:blue;font-size:15.0pt; font-family:Roboto;mso-generic-font-family:auto;mso-font-charset:0'>Weaviate</span></a></td> <td class=xl823387 width=49 style='border-top:none;width:37pt'>Y</td> <td class=xl723387 width=71 style='border-top:none;border-left:none; width:53pt'>BSD</td> <td class=xl723387 width=82 style='border-top:none;border-left:none; width:62pt'>5.6k ⭐</td> <td class=xl733387 align=left width=88 style='border-top:none;border-left: none;width:66pt'>Assembly, C++, GoLang</td> <td class=xl733387 align=left width=155 style='border-top:none;border-left: none;width:116pt'>Weaviate is a commercial Open Source cloud-native vector database that stores both objects and vectors.</td> <td class=xl733387 align=left width=116 style='border-top:none;border-left: none;width:87pt'>Open SaaS</td> <td class=xl723387 width=206 style='border-top:none;border-left:none; width:155pt'>❔</td> <td class=xl723387 width=221 style='border-top:none;border-left:none; width:166pt'>started in 2018 as a traditional graph database, first released in 2019</td> <td class=xl723387 width=88 style='border-top:none;border-left:none; width:66pt'>N</td> <td class=xl973387 width=88 style='border-top:none;border-left:none; width:66pt'>Y, static sharding</td> <td class=xl743387 align=left width=88 style='border-top:none;border-left: none;width:66pt'><a href="https://weaviate.io/blog/ann-algorithms-hnsw-pq"><span style='color:blue'>a custom <font class="font53387">HNSW PQ</font><font class="font03387"> algorithm that supports CRUD</font></span></a></td> <td class=xl733387 align=left width=154 style='border-top:none;border-left: none;width:116pt'>Eventual Consistency</td> <td class=xl1053387 align=left width=185 style='border-top:none;border-left: none;width:139pt'><a href="https://weaviate.io/developers/weaviate/benchmarks">not comparative, just evaluating their own performance<span style='mso-spacerun:yes'> </span></a></td> <td class=xl723387 width=88 style='border-top:none;border-left:none; width:66pt'>Y (multiple ANN algorithms as long as they support full CRUD)</td> <td class=xl733387 align=left width=88 style='border-top:none;border-left: none;width:66pt'>67.7M USD, series B</td> <td class=xl733387 align=left width=99 style='border-top:none;border-left: none;width:74pt'>SeMI Technologies</td> <td class=xl1023387 width=66 style='border-top:none;border-left:none; width:50pt'>🇪🇺</td> <td class=xl683387 width=24 style='width:18pt'></td> </tr> <tr height=85 style='height:63.75pt'> <td height=85 class=xl153387 style='height:63.75pt'></td> <td class=xl863387 align=left width=130 style='border-top:none;width:98pt'><a href="https://docs.trychroma.com/"><span style='color:blue;font-size:15.0pt; font-family:Roboto;mso-generic-font-family:auto;mso-font-charset:0'>Chroma</span></a></td> <td class=xl813387 width=49 style='border-top:none;width:37pt'>Y</td> <td class=xl693387 width=71 style='border-top:none;border-left:none; width:53pt'>Apache-2.0</td> <td class=xl693387 width=82 style='border-top:none;border-left:none; width:62pt'>4.4k ⭐</td> <td class=xl703387 align=left width=88 style='border-top:none;border-left: none;width:66pt'>Python & Typescript</td> <td class=xl703387 align=left width=155 style='border-top:none;border-left: none;width:116pt'>Chroma is a Commercial Open Source vector database</td> <td class=xl703387 align=left width=116 style='border-top:none;border-left: none;width:87pt'>Preparing a (Partly Open) SaaS model* [Commercial Open Source]</td> <td class=xl693387 width=206 style='border-top:none;border-left:none; width:155pt'>HNSW lib, DuckDB; based on ClickHouse</td> <td class=xl693387 width=221 style='border-top:none;border-left:none; width:166pt'>looks like 2022</td> <td class=xl983387 style='border-top:none;border-left:none'>N</td> <td class=xl993387 width=88 style='border-top:none;border-left:none; width:66pt'>Dynamic segment placement</td> <td class=xl763387 style='border-top:none;border-left:none'> </td> <td class=xl753387 style='border-top:none;border-left:none'> </td> <td class=xl703387 width=185 style='border-top:none;border-left:none; width:139pt'> </td> <td class=xl983387 style='border-top:none;border-left:none'>Y</td> <td class=xl703387 align=left width=88 style='border-top:none;border-left: none;width:66pt'>20.3M USD, seed</td> <td class=xl703387 align=left width=99 style='border-top:none;border-left: none;width:74pt'>Chroma Inc.</td> <td class=xl1033387 width=66 style='border-top:none;border-left:none; width:50pt'>🇺🇸</td> <td class=xl683387 width=24 style='width:18pt'></td> </tr> <tr height=68 style='height:51.0pt'> <td height=68 class=xl153387 style='height:51.0pt'></td> <td class=xl853387 align=left width=130 style='border-top:none;width:98pt'><a href="https://qdrant.tech/"><span style='color:blue;font-size:15.0pt; font-family:Roboto;mso-generic-font-family:auto;mso-font-charset:0'>Qdrant</span></a></td> <td class=xl823387 width=49 style='border-top:none;width:37pt'>Y</td> <td class=xl723387 width=71 style='border-top:none;border-left:none; width:53pt'>Apache-2.0</td> <td class=xl723387 width=82 style='border-top:none;border-left:none; width:62pt'>6.6k ⭐</td> <td class=xl733387 align=left width=88 style='border-top:none;border-left: none;width:66pt'>Rust</td> <td class=xl733387 align=left width=155 style='border-top:none;border-left: none;width:116pt'>Qdrant is a Commercial Open Source vector similarity search engine and vector database</td> <td class=xl733387 align=left width=116 style='border-top:none;border-left: none;width:87pt'>Open SaaS</td> <td class=xl723387 width=206 style='border-top:none;border-left:none; width:155pt'>RocksDB</td> <td class=xl723387 width=221 style='border-top:none;border-left:none; width:166pt'>first released: 2021</td> <td class=xl723387 width=88 style='border-top:none;border-left:none; width:66pt'>Y</td> <td class=xl973387 width=88 style='border-top:none;border-left:none; width:66pt'>Y, static sharding</td> <td class=xl743387 align=left width=88 style='border-top:none;border-left: none;width:66pt'><a href="https://qdrant.tech/articles/scalar-quantization/"><span style='color:blue'>HNSW (<font class="font53387">SQ</font><font class="font03387"> & </font><font class="font53387">PQ</font><font class="font03387">)</font></span></a></td> <td class=xl733387 align=left width=154 style='border-top:none;border-left: none;width:116pt'>Eventual Consistency, tunable consistency</td> <td class=xl1053387 align=left width=185 style='border-top:none;border-left: none;width:139pt'><a href="https://qdrant.tech/benchmarks/">compares to weaviate, milvus, elastic (note: redis took too long to complete)</a></td> <td class=xl723387 width=88 style='border-top:none;border-left:none; width:66pt'>Y</td> <td class=xl733387 align=left width=88 style='border-top:none;border-left: none;width:66pt'>9.8M €</td> <td class=xl733387 align=left width=99 style='border-top:none;border-left: none;width:74pt'>Qdrant Solutions GmbH</td> <td class=xl1023387 width=66 style='border-top:none;border-left:none; width:50pt'>🇪🇺</td> <td class=xl683387 width=24 style='width:18pt'></td> </tr> <tr height=158 style='mso-height-source:userset;height:119.1pt'> <td height=158 class=xl153387 style='height:119.1pt'></td> <td class=xl863387 align=left width=130 style='border-top:none;width:98pt'><a href="https://milvus.io/"><span style='color:blue;font-size:15.0pt; font-family:Roboto;mso-generic-font-family:auto;mso-font-charset:0'>Milvus</span></a></td> <td class=xl813387 width=49 style='border-top:none;width:37pt'>Y</td> <td class=xl693387 width=71 style='border-top:none;border-left:none; width:53pt'>Apache-2.0</td> <td class=xl693387 width=82 style='border-top:none;border-left:none; width:62pt'>18k ⭐</td> <td class=xl703387 align=left width=88 style='border-top:none;border-left: none;width:66pt'>GoLang & Python</td> <td class=xl703387 align=left width=155 style='border-top:none;border-left: none;width:116pt'>Milvus is a cloud-native Commercial Open Source vector database</td> <td class=xl703387 align=left width=116 style='border-top:none;border-left: none;width:87pt'>(Partly Open) SaaS* [Commercial Open Source]</td> <td class=xl693387 width=206 style='border-top:none;border-left:none; width:155pt'>Initial blog post from them said SQLite, but meanwhile they said RocksDB - exchanged?<br /> they also have a ChatGPT-Cache that is build on SQLite<br /> and say "Milvus uses SQLite or MySQL to manage metadata"</td> <td class=xl693387 width=221 style='border-top:none;border-left:none; width:166pt'>founded 2017, first released: 2019</td> <td class=xl693387 width=88 style='border-top:none;border-left:none; width:66pt'>N</td> <td class=xl993387 width=88 style='border-top:none;border-left:none; width:66pt'>Dynamic segment placement</td> <td class=xl773387 align=left width=88 style='border-top:none;border-left: none;width:66pt'><a href="https://milvus.io/docs/v1.1.1/index.md"><span style='color:blue'>ANNOY; HNSW; IVF_PQ; IVF_SQ(; IVF_FLAT; FLAT; IVF_SQ8_H; RNSG</span></a></td> <td class=xl703387 align=left width=154 style='border-top:none;border-left: none;width:116pt'>Strong, bounded staleness, session, and eventually. The default consistency level in Milvus is bounded staleness.<span style='mso-spacerun:yes'> </span></td> <td class=xl1063387 align=left width=185 style='border-top:none;border-left: none;width:139pt'><a href="https://milvus.io/docs/benchmark.md">not comparative</a></td> <td class=xl693387 width=88 style='border-top:none;border-left:none; width:66pt'>Y</td> <td class=xl703387 align=left width=88 style='border-top:none;border-left: none;width:66pt'>113M USD, series B</td> <td class=xl703387 align=left width=99 style='border-top:none;border-left: none;width:74pt'>Zilliz</td> <td class=xl1033387 width=66 style='border-top:none;border-left:none; width:50pt'>🇺🇸</td> <td class=xl683387 width=24 style='width:18pt'></td> </tr> <tr height=119 style='height:89.25pt'> <td height=119 class=xl153387 style='height:89.25pt'></td> <td class=xl853387 align=left width=130 style='border-top:none;width:98pt'><a href="https://vespa.ai/"><span style='color:blue;font-size:15.0pt;font-family: Roboto;mso-generic-font-family:auto;mso-font-charset:0'>Vespa</span></a></td> <td class=xl823387 width=49 style='border-top:none;width:37pt'>Y</td> <td class=xl723387 width=71 style='border-top:none;border-left:none; width:53pt'>Apache-2.0</td> <td class=xl723387 width=82 style='border-top:none;border-left:none; width:62pt'>4.4k ⭐</td> <td class=xl733387 align=left width=88 style='border-top:none;border-left: none;width:66pt'>Java & C++</td> <td class=xl733387 align=left width=155 style='border-top:none;border-left: none;width:116pt'>Vespa is a Commercial Open Source vector database by Yahoo! It is a search engine which supports vector search, lexical search, and search in structured data</td> <td class=xl733387 align=left width=116 style='border-top:none;border-left: none;width:87pt'>Open SaaS</td> <td class=xl723387 width=206 style='border-top:none;border-left:none; width:155pt'>❔</td> <td class=xl723387 width=221 style='border-top:none;border-left:none; width:166pt'>Originally a web search engine (alltheweb), acquired by Yahoo! in 2003 and later open sourced as Vespa in 2017; sinde Oct 2023 spinoff, raised series A in Nov 2023</td> <td class=xl723387 width=88 style='border-top:none;border-left:none; width:66pt'>maintains disk and memory structures for documents</td> <td class=xl973387 width=88 style='border-top:none;border-left:none; width:66pt'>Y</td> <td class=xl743387 align=left width=88 style='border-top:none;border-left: none;width:66pt'><a href="https://blog.vespa.ai/semantic-search-with-multi-vector-indexing/"><span style='color:blue'>Custom HNSW (<font class="font53387">Multi-vector</font><font class="font03387"> hybrid </font><font class="font53387">HNSW-IF</font><font class="font03387">)</font></span></a></td> <td class=xl733387 align=left width=154 style='border-top:none;border-left: none;width:116pt'>Eventual Consistency</td> <td class=xl1053387 align=left width=185 style='border-top:none;border-left: none;width:139pt'><a href="https://medium.com/vespa/billion-scale-vector-search-with-vespa-part-two-bac817b30286">not comparative<span style='mso-spacerun:yes'> </span></a></td> <td class=xl723387 width=88 style='border-top:none;border-left:none; width:66pt'>Y</td> <td class=xl733387 align=left width=88 style='border-top:none;border-left: none;width:66pt'>Spinoff from Yahoo! in Oct 2023, then raised a 31M USD series A</td> <td class=xl733387 align=left width=99 style='border-top:none;border-left: none;width:74pt'>Yahoo!</td> <td class=xl1023387 width=66 style='border-top:none;border-left:none; width:50pt'>🇺🇸</td> <td class=xl683387 width=24 style='width:18pt'></td> </tr> <tr height=142 style='mso-height-source:userset;height:107.1pt'> <td height=142 class=xl153387 style='height:107.1pt'></td> <td class=xl863387 align=left width=130 style='border-top:none;width:98pt'><a href="https://vald.vdaas.org/"><span style='color:blue;font-size:15.0pt; font-family:Roboto;mso-generic-font-family:auto;mso-font-charset:0'>Vald</span></a></td> <td class=xl813387 width=49 style='border-top:none;width:37pt'>Y</td> <td class=xl693387 width=71 style='border-top:none;border-left:none; width:53pt'>Apache-2.0</td> <td class=xl693387 width=82 style='border-top:none;border-left:none; width:62pt'>1.2k ⭐</td> <td class=xl703387 align=left width=88 style='border-top:none;border-left: none;width:66pt'>GoLang</td> <td class=xl703387 align=left width=155 style='border-top:none;border-left: none;width:116pt'>Vald is a cloud-native Open Source distributed approximate nearest neighbor (ANN) dense vector search engine</td> <td class=xl703387 align=left width=116 style='border-top:none;border-left: none;width:87pt'>Community project, currently looks like no commercial interests are pursued</td> <td class=xl693387 width=206 style='border-top:none;border-left:none; width:155pt'>uses the vector search engine NGT</td> <td class=xl693387 width=221 style='border-top:none;border-left:none; width:166pt'>Technology incubation at Yahoo! Japan Corporation, development was stared in 2019</td> <td class=xl693387 width=88 style='border-top:none;border-left:none; width:66pt'>❔</td> <td class=xl993387 width=88 style='border-top:none;border-left:none; width:66pt'>N/A</td> <td class=xl713387 align=left width=88 style='border-top:none;border-left: none;width:66pt'>N/A</td> <td class=xl703387 align=left width=154 style='border-top:none;border-left: none;width:116pt'>N/A</td> <td class=xl1063387 align=left width=185 style='border-top:none;border-left: none;width:139pt'><a href="https://speakerdeck.com/techverse_2022/vald-oss-ann-nearest-neighbor-dense-vector-search-engine-introduction-and-case-studies">not comparitive, but Vald performance only</a></td> <td class=xl693387 width=88 style='border-top:none;border-left:none; width:66pt'>Y (NGT)</td> <td class=xl703387 align=left width=88 style='border-top:none;border-left: none;width:66pt'>-</td> <td class=xl703387 align=left width=99 style='border-top:none;border-left: none;width:74pt'>Yusuke Kato (Yahoo Japan Corporation), Kiichiro Yukawa (Yahoo Japan Corporation)</td> <td class=xl1033387 width=66 style='border-top:none;border-left:none; width:50pt'>🇯🇵</td> <td class=xl683387 width=24 style='width:18pt'></td> </tr> <tr height=103 style='height:77.25pt'> <td height=103 class=xl153387 style='height:77.25pt'></td> <td class=xl873387 align=left width=130 style='border-top:none;width:98pt'><a href="https://www.pinecone.io/"><span style='color:blue;font-size:15.0pt; font-family:Roboto;mso-generic-font-family:auto;mso-font-charset:0'><font class="font63387">Pinecone</font><font class="font73387"><br /> </font></span></a></td> <td class=xl833387 width=49 style='border-top:none;width:37pt'>N</td> <td class=xl783387 width=71 style='border-top:none;border-left:none; width:53pt'>Proprietary</td> <td class=xl783387 width=82 style='border-top:none;border-left:none; width:62pt'>NA</td> <td class=xl793387 width=88 style='border-top:none;border-left:none; width:66pt'> </td> <td class=xl793387 align=left width=155 style='border-top:none;border-left: none;width:116pt'>Pinecone is a fully managed vector database that specializes in enabling semantic search capabilities</td> <td class=xl793387 align=left width=116 style='border-top:none;border-left: none;width:87pt'>SaaS</td> <td class=xl783387 width=206 style='border-top:none;border-left:none; width:155pt'>built on top of Faiss</td> <td class=xl783387 width=221 style='border-top:none;border-left:none; width:166pt'>first released in 2019</td> <td class=xl783387 width=88 style='border-top:none;border-left:none; width:66pt'>N</td> <td class=xl1003387 width=88 style='border-top:none;border-left:none; width:66pt'>Y</td> <td class=xl803387 align=left width=88 style='border-top:none;border-left: none;width:66pt'>proprietary</td> <td class=xl793387 align=left width=154 style='border-top:none;border-left: none;width:116pt'>Eventual Consistency</td> <td class=xl1073387 align=left width=185 style='border-top:none;border-left: none;width:139pt'><a href="https://www.pinecone.io/learn/rust-rewrite/">more programming language comparison for vector databases</a></td> <td class=xl783387 width=88 style='border-top:none;border-left:none; width:66pt'>Y (proprietary), plus KNN (with Faiss)</td> <td class=xl793387 align=left width=88 style='border-top:none;border-left: none;width:66pt'>138M, series B</td> <td class=xl793387 align=left width=99 style='border-top:none;border-left: none;width:74pt'>Pinecone Systems Inc</td> <td class=xl1043387 width=66 style='border-top:none;border-left:none; width:50pt'>🇺🇸</td> <td class=xl683387 width=24 style='width:18pt'></td> </tr> <tr height=17 style='height:12.75pt'> <td height=17 class=xl153387 style='height:12.75pt'></td> <td class=xl683387 width=130 style='width:98pt'></td> <td class=xl673387 width=49 style='width:37pt'></td> <td class=xl673387 width=71 style='width:53pt'></td> <td class=xl673387 width=82 style='width:62pt'></td> <td class=xl683387 width=88 style='width:66pt'></td> <td class=xl683387 width=155 style='width:116pt'></td> <td class=xl683387 width=116 style='width:87pt'></td> <td class=xl673387 width=206 style='width:155pt'></td> <td class=xl673387 width=221 style='width:166pt'></td> <td class=xl683387 width=88 style='width:66pt'></td> <td class=xl653387 width=88 style='width:66pt'></td> <td class=xl653387 width=88 style='width:66pt'></td> <td class=xl653387 width=154 style='width:116pt'></td> <td class=xl653387 width=185 style='width:139pt'></td> <td class=xl653387 width=88 style='width:66pt'></td> <td class=xl683387 width=88 style='width:66pt'></td> <td class=xl683387 width=99 style='width:74pt'></td> <td class=xl683387 width=66 style='width:50pt'></td> <td class=xl683387 width=24 style='width:18pt'></td> </tr> <![if supportMisalignedColumns]> <tr height=0 style='display:none'> <td width=30 style='width:23pt'></td> <td width=130 style='width:98pt'></td> <td width=49 style='width:37pt'></td> <td width=71 style='width:53pt'></td> <td width=82 style='width:62pt'></td> <td width=88 style='width:66pt'></td> <td width=155 style='width:116pt'></td> <td width=116 style='width:87pt'></td> <td width=206 style='width:155pt'></td> <td width=221 style='width:166pt'></td> <td width=88 style='width:66pt'></td> <td width=88 style='width:66pt'></td> <td width=88 style='width:66pt'></td> <td width=154 style='width:116pt'></td> <td width=185 style='width:139pt'></td> <td width=88 style='width:66pt'></td> <td width=88 style='width:66pt'></td> <td width=99 style='width:74pt'></td> <td width=66 style='width:50pt'></td> <td width=24 style='width:18pt'></td> </tr> <![endif]> </table> </div> <!-----------------------------> <!--END OF OUTPUT FROM EXCEL PUBLISH AS WEB PAGE WIZARD--> <!-----------------------------> </body> </html> </div> </div> </div><div class="et_pb_section et_pb_section_3 et_section_regular" > <div class="et_pb_row et_pb_row_4"> <div class="et_pb_column et_pb_column_4_4 et_pb_column_4 et_pb_css_mix_blend_mode_passthrough et-last-child"> <div class="et_pb_module et_pb_text et_pb_text_4 et_pb_text_align_left et_pb_bg_layout_light"> <div class="et_pb_text_inner"><h2>Want to know more about the vector database market?</h2> <p>Here are some more questions answered for anyone interested</p></div> </div><div class="et_pb_module et_pb_toggle et_pb_toggle_3 et_pb_toggle_item et_pb_toggle_close"> <h5 class="et_pb_toggle_title">What is an "Open SaaS" business model?</h5> <div class="et_pb_toggle_content clearfix"><p>Software as a service (SaaS) refers to software that is managed / hosted for the client and is essentially “rented.” The open in Open SaaS refers to the open source software that is being offered as such a service.</p> <p>This frequently implies that not all code is open source, particularly that which is part of the managed service / hosting and associated value-adding features. Note: The open source software offered in this manner may or may not be provided by the company providing the software as a service. This has caused some friction in the open source community, as original creators often struggle to make a living, and/or maintainers struggle to keep maintaining the software – while other companies profit. Most famously, huge cloud providers have taken advantage of this option, leading to new licenses that keep the source open but restrict others from hosting as a service without donating the whole source code back to the community.</p></div> </div><div class="et_pb_module et_pb_toggle et_pb_toggle_4 et_pb_toggle_item et_pb_toggle_close"> <h5 class="et_pb_toggle_title">Why should I care about index types?</h5> <div class="et_pb_toggle_content clearfix"><p>Indexes are essentially a way to speed up searching a database. There are several established index types for vector databases and they affect the performance of the database<span>, e.g. the time it takes a query to complete.</span></p></div> </div><div class="et_pb_module et_pb_toggle et_pb_toggle_5 et_pb_toggle_item et_pb_toggle_close"> <h5 class="et_pb_toggle_title">What about benchmarks?</h5> <div class="et_pb_toggle_content clearfix"><p>You will see, if you review the benchmarks given at the top, that results typically vary. <a href="https://objectbox.io/how-to-benchmark-database-performance-and-objectbox/">Benchmarks are difficult to do</a> and neutral benchmarks even more so. Certain use cases may favor certain solutions. Therefore, ideally you benchmark based on your specific use case…. but as a first evaluation, try to understand the basic influencing factors and have a look at a handful of benchmarks and explanations. Having said all this: <a href="https://ann-benchmarks.com/#:~:text=ANN%2DBenchmarks%20is%20a%20benchmarking,evaluated%20data%20sets%20and%20algorithms" target="_blank" rel="noopener">There is a benchmarking tool available for approximate nearest neighbor (ANN) algorithms search</a>. <span>If you use this, you can compare the performance of different databases (with regards to the ANN search) for the same setup, based on the same approach. </span>Also: The underlying libs often used by databases (like NGT and HNSW, see above) have already been benchmarked with it and you can compare to these directly.</p></div> </div><div class="et_pb_module et_pb_toggle et_pb_toggle_6 et_pb_toggle_item et_pb_toggle_close"> <h5 class="et_pb_toggle_title">Why is the market so hot, how can companies raise so much money?</h5> <div class="et_pb_toggle_content clearfix"><p>AI is hot, everyone agrees that data and its management will be key to future success, and the database market <span style="font-weight: 400;">is interesting: It is a long established market with many players, yet still demonstrating continually good growth (e.g. 17% in 2020). And the database market history shows that from time to time a new type of database comes up, and with it, the creation of a new market category. In such a market, typically the market creator “takes all” (not quite literally, but such a significant share, definetely the vast majority, that all other players are not attractive from a VC-perspective). Such a market could easily be worth 100M+ in ARR. Examples from the last 20 years: MongoDB (NoSQL databases), Cockroach (NewSQL databases), Neo4J (Graph databases), Influx (Time-Series databases). So, VCs are looking to find the next new type of database that can create a market… Maybe it will be vector databases? However, the database market has also shown to take 10 years+ for players to become profitable, so expect a longterm game. </span>The race is still on for Edge Databases we think 🙂</p></div> </div><div class="et_pb_module et_pb_toggle et_pb_toggle_7 et_pb_toggle_item et_pb_toggle_close"> <h5 class="et_pb_toggle_title">Want to know more about the database market?</h5> <div class="et_pb_toggle_content clearfix"><p>We recommend checking out <a href="https://db-engines.com/" target="_blank" rel="noopener">db-engines</a>. The website compares all relevant systems and has tons of data from the last 20 years. Note: They do only add databases once they have some traction and notability, not any hobby project. Accordingly not all databases of the above comparison have been added to the website yet.</p></div> </div> </div> </div> </div><div class="et_pb_section et_pb_section_4 et_pb_fullwidth_section et_section_regular et_pb_section--absolute" > <div class="et_pb_module et_pb_fullwidth_code et_pb_fullwidth_code_1 et_pb_text_align_center"> </div> </div> </div> </div> </div> <div class="et_post_meta_wrapper"> </div> </article> </div> </div> </div> </div> <script nitro-exclude> var heartbeatData = new FormData(); heartbeatData.append('nitroHeartbeat', '1'); fetch(location.href, {method: 'POST', body: heartbeatData, credentials: 'omit'}); </script> <script nitro-exclude> document.cookie = 'nitroCachedPage=' + (!window.NITROPACK_STATE ? '0' : '1') + '; path=/; SameSite=Lax'; </script> <span class="et_pb_scroll_top et-pb-icon"></span> <footer id="main-footer"> <div id="footer-bottom"> <div class="container clearfix"> <div id="footer-info">Ⓒ Copyright 2024 ObjectBox Limited. 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