The case against vector databases

Learn why vector databases are not the silver bullet in AI and whether you should use one in your project.

Key takeaways:

What's all the fuss about?

Vector search is not a new concept. Facebook released Faiss, library for similarity search, back in 2017.

After a meteoric rise of ChatGPT, investors' attention was directed towards vector databases - supposedly "picks and shovels" for AI.

Lots of sponsored content blurs the image for people new to AI. Companies hire evangelists or developer advocates to promote the use of vector databases, but they don't have the incentive to explain when to use one, whether you actually need it at all.

These and many other questions engineers need to answer before deciding on the implementation. Watch the slides to better understand what the main caveats to consider are.

About the author

Dariusz Semba
Dariusz is a seasoned ML engineer and entrepreneur. He specializes in LLMs, information retrieval, and search. As the founder of Softwise.AI, his mission is to guide others in leveraging AI components in their companies and projects.