> Bron: https://neuralex.nl/en/blog/heb-je-een-vectordatabase-nodig > A vector database is not essential for every RAG project. Learn about simpler options, what to consider when choosing, and when a dedicated database may be useful. [Back to Insights](/en/blog) RAG ·6 October 2026 ·5 min read # Do you actually need a vector database? A vector database is not essential for every RAG project. Learn about simpler options, what to consider when choosing, and when a dedicated database may be useful. For a small or medium-sized document collection, you often do not need a separate vector database. An existing database or search index with vector support may be enough; for a limited use case, even a simple file index can work. The right choice depends on your documents, search questions and operational needs. A dedicated vector database becomes more interesting when your collection grows, many people search at the same time, or you have strict requirements for speed and filtering. There is no single threshold that applies to every project. Start with an approach you can understand and manage, then test it with questions from your own work. [RAG explained](/en/blog/rag-uitgelegd) describes how document retrieval and a language model work together to produce an answer. ## What does a vector database do? A vector database stores embeddings: numerical representations of text that help capture meaning and context. When someone asks a question, the system converts that question into a representation too. The database then finds document passages that are close in meaning. This can help it find relevant text even when the question uses different words from the document. That does not mean the database understands what is true or evaluates an answer. It helps retrieve suitable passages; a language model can then use them to formulate a response. Quality also depends on the source files, how you split the text and the embeddings. See also [embeddings for Dutch text](/en/blog/embeddings-nederlandse-tekst). ## Vectors are not the only way to search Semantic search is useful, but it can miss exact terms. A user might enter a document number, product name or specific phrase. Keyword search often finds these exact matches better. Hybrid search combines vector retrieval with traditional word search. You can also rerank the results before sending them to the language model. The best combination varies by collection and question. In documentation with fixed terminology, keywords may matter most; for questions in everyday language, semantic search can help. A hybrid approach does not guarantee good answers, but it gives you more ways to find relevant sources. Check whether the results fit the question, not just whether they share words with it. ## What are your options? You can add vector support to a database you already use. This may be convenient when your data is already stored there and you want to keep management in one system. A search index can combine keywords, filters and vector retrieval. This may suit you when your main goal is to make documents and text searchable. A dedicated vector database focuses on storing and searching embeddings. It may make sense when vector search becomes a central part of your application, but it also adds another service to configure, secure, monitor and back up. For a small collection, you may be able to use a simple file index in memory. That approach is easy to understand, but may become unsuitable as the collection, user count or availability requirements grow. The number of documents is one factor, but it does not tell the whole story. Document structure and length, the number of passages after splitting, and the kinds of questions also matter. Read [how many documents you need for RAG](/en/blog/hoeveel-documenten-voor-rag) and see how [metadata filters for RAG](/en/blog/metadata-filters-rag) can limit results to the right department, period or document type. ## What should guide your choice? Consider the size of your collection and how quickly it is growing. Also ask how often documents change: an index updated occasionally has different needs from one that must reflect changes quickly. If users need to filter by department, date, case file or access rights, check that your setup can apply those filters reliably. Think about the number of simultaneous users, the response time you need and the work required to maintain the system. Who handles updates, access rights, monitoring and recovery? How will you back up the documents, index and configuration, and can you restore them? An additional service may be technically suitable but still be a poor fit if no one can manage it. Choose based on what your organization can maintain as well as on the features. ## A simple start may be enough You do not have to predict the perfect architecture in advance. Start with the simplest option that meets your requirements. Gather representative questions from users, check which sources the system finds, and assess whether the answers are accurate, sufficiently complete and linked to the right documents. Test everyday-language queries as well as queries with exact terms. Also check cases where the right source is missing. If results are too slow, filters are missing or the index becomes impractical to manage, you can change the setup. Migration is easier if you keep documents, metadata and indexing settings separate and well organized. Moving to another database may require you to rebuild embeddings. The effort and resources involved depend on your data and the systems you choose. **Quick decision list:** - **Start small:** use existing database or search features if they answer your questions well. - **Combine search methods:** test vector search with keywords when exact terms matter. - **Check filters:** make sure metadata and access rights are applied correctly. - **Include maintenance:** account for backups, updates, monitoring and recovery. - **Scale when needed:** choose a dedicated vector database if growth, concurrent use, or speed and filtering requirements justify it. RAG ## Which search setup fits your documents? We help you review the size, filtering and maintenance of your knowledge base — practical and without invented claims. Curious what that means for your setup? [Ask your question](/contact) [Read how RAG works](/blog/rag-uitgelegd) --- Volledige (opgemaakte) versie: https://neuralex.nl/en/blog/heb-je-een-vectordatabase-nodig