AI applications often end up with fragmented data layers spread across separate databases, caches, vector stores, search engines, streaming platforms, and edge databases, each adding latency, duplication, operational overhead, cost, and security surface. A framework is offered for deciding what to consolidate: start with caching and session state (high frequency, low differentiation), move to vector and full-text search consolidation next, consider real-time and edge only at scale, and keep specialized analytical or graph engines separate. Couchbase Capella is presented as a multi-model DBaaS that can replace Redis/Memcached, Pinecone/Weaviate/Milvus, and Elasticsearch-style search engines while running across AWS, Google Cloud, and Azure.
Table of contents
What AI technology consolidation actually meansThe real cost of a fragmented AI stackA framework for deciding what to consolidate and whenWhat Capella replaces when you consolidateWhy a multicloud DBaaS is the right consolidation vehicleAI stack consolidation FAQs165 Impressions