Case Study: Metaphor achieves scalable semantic search and lower complexity with MongoDB Atlas Vector Search

Metaphor boosts productivity 2x-3x with MongoDB

Metaphor, a data search and discovery platform, faced challenges with its initial tech stack using DocumentDB and Elasticsearch, which involved complex data pipelines and maintenance. They later experimented with Pinecone for vector search but found it difficult to scale and cost-prohibitive for their multi-tenant model. These issues led them to seek a more integrated and efficient solution from MongoDB.

By adopting MongoDB Atlas Vector Search, Metaphor consolidated its database and search needs onto a single platform. This eliminated the need for separate data pipelines, reduced latency, and simplified their architecture. The solution enabled powerful semantic search capabilities, allowing non-technical users to ask natural language questions. MongoDB's solution increased engineering productivity by 2-3 times and provided a financially viable way to offer dedicated, secure clusters for each customer.

Metaphor