The best AI databases to use for AI search in 2026

Vectorize notes that real-time pipelines can process uploads “almost immediately,” making new content available to models right away. Common patterns for freshness:

• Streaming/real-time ingestion: write-through pipelines take new documents/embeddings and push them into the index as they arrive (so queries see updates immediately).
• Optional graph layer: Vectorize extracts entities and writes to Neo4j automatically, with semantic de-duplication to avoid duplicate relations — you can enable or skip graph lookups per query to trade off latency vs. richer results.
• Low-latency indexing engines: vendors like ApertureDB report sub-10ms service latencies and high KNN throughput, which helps maintain immediate query freshness at scale.

Tip: tune whether to include graph or hybrid steps per query to balance freshness and latency.

xpander.ai highlights the tradeoff clearly. Cloud-managed AI databases hide infra and orchestration: the vendor handles control/data planes, high availability, rollbacks and scaling so teams “forget about infrastructure” and focus on agent behavior. Self-hosted gives you full control but means you must replicate a lot of stack pieces—vector DBs, memory DBs, gateways, auth and scale logic—which many large teams find the hardest part to build.

• Pros of managed: faster setup, built-in scaling and resilience.
• Pros of self-hosted: more customization, data locality and potential performance wins (e.g., ApertureDB reports 2–10x KNN throughput and sub-10ms latencies).

Choose managed for speed and ops simplicity; choose self-hosted if you need fine-grained control or top-tier throughput.