A small AI project quickly needs one thing that a normal database handles badly: similarity search over embeddings. Whether you are building a document question-answering tool, a recommendation feature, or a semantic search demo, you need to find the most similar vectors to a query, fast. Managed vector databases offer this, but their pricing assumes a funded startup, not a side project.

The good news is that small projects rarely need managed scale. A few thousand documents, a few users, and a modest query rate fit comfortably in free or self-hosted options. The trick is picking one that matches your project's size honestly, rather than the one with the most impressive marketing.

For a prototype with a few hundred documents, a simple approach works fine: store embeddings in a file or a standard database and compute similarity directly. At this scale the search is fast enough, the setup is trivial, and you avoid learning a new system before you know whether the project is worth it.

Many popular relational databases now include vector extensions that add similarity search to a database you may already run. This is often the best middle path for a small project, because it keeps your data in one place, uses familiar tooling, and scales further than most side projects ever need. Graduate to a dedicated system only when measurements show you must.

Dedicated open-source vector databases can run as a single container on a small server, giving you proper indexing, filtering, and metadata search at no software cost. They are the right choice when your collection grows past what a simple approach handles comfortably, or when you need features like hybrid search combining keywords and vectors.

Some managed providers also offer free tiers with small limits, which suit demos and prototypes that need zero operations. The trade-off is the usual one: free tiers can change, and migrating embeddings later is work. For a learning project, a free managed tier is perfect. For a project you expect to keep, self-hosting on your own server gives you control over that future.

Estimate honestly: how many documents, how many queries per day, and how fast must answers return? A personal tool with a thousand documents and ten queries a day needs almost nothing. A public demo with a hundred thousand documents needs an index and some memory. Most side projects sit firmly in the first category and overbuild for the second.

Start with the simplest option that meets your measurements, and add complexity only when slowness proves you need it. Vector search feels like advanced infrastructure, but for small projects it is a solved problem with free answers. Spend your effort on the product, not the database, until the product earns the upgrade.

Before choosing a vector database, build a small evaluation set: twenty questions your users might ask, with the documents that should rank highest for each. Run your search against this set and check the results by eye. This simple test tells you more about quality than any benchmark, because it uses your data and your questions.

Repeat the test whenever you change chunking, embedding models, or retrieval settings, and keep the scores in a note. Search quality drifts quietly as content grows, and a small evaluation set catches that drift before users complain. A side project with a measured, adequate search beats an ambitious project with an impressive database and untested results.