# Vector Databases are not the only solution

Yingjun Wu, in this terrific article, advises against investing in vector databases, especially for those looking to enter the field in mid-2023. 

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He outlines several reasons for this, focusing on the technology, applications, and market landscape of vector databases. Here are the key points:

### <mark style="color:purple;">**Technology of Vector Databases**</mark>

* Vector databases are designed to store and process unstructured data (like images, audio, text) by converting them into vector features using machine learning algorithms.
* These databases use data indexing techniques (like inverted indexing and vector quantization) for efficient similarity searches and to reduce storage and computational requirements.
* Existing OLAP databases with columnar storage (like ClickHouse, Apache Pinot, and Apache Druid) already demonstrate impressive data compression rates and <mark style="color:yellow;">can integrate vector search functionalities.</mark>

### <mark style="color:purple;">**Vector Databases and AI Models**</mark>

* The rise of vector databases is linked to the need for managing the vast amounts of data used by large-scale generative AI models.
* They enable accurate similarity searches and support multimodal data processing, crucial for AI applications.

<mark style="color:green;">**Market Saturation and Advice Against Investment**</mark>

* Wu <mark style="color:yellow;">advises against new investments in vector databases due to market saturation</mark>, as many products already exist in this space.
* For companies with heavy workloads requiring advanced vector search, specialized vector databases are recommended. However, for <mark style="color:yellow;">most other use cases, existing commercial databases or open-source databases like PostgreSQL (with pgvector functionality) are sufficient</mark>.

### <mark style="color:purple;">**Existing Databases Incorporating Vector Search**</mark>

* Many commercial databases are enhancing their capabilities by incorporating vector search functionalities.
* PostgreSQL and other open-source databases like OpenSearch, ClickHouse, and Cassandra have implemented vector search features, reducing the need for specialised vector databases.

### <mark style="color:purple;">**Competitive Landscape**</mark>

* The vector database market is already crowded with established players, making it challenging for new entrants.
* Wu suggests focusing on enhancing existing databases with vector capabilities rather than investing in new vector database projects.