From Information Retrieval to AI Infrastructure: The Next Phase of Search Engines

Search engines are transitioning from pure information‑retrieval tools to the backbone of AI infrastructure that connects enterprise data with AI capabilities. This trend is reinforced by two recent signals: a PPT presented at the 17th China Database Technology Conference (DTCC) titled “New Positioning of Search Engines in the AI Era – From Tool to AI Infra,” and the AI Search Technology Conference co‑hosted by Tencent Cloud and Elastic, both emphasizing the same direction.

Elastic’s actions in version 9.5 serve as a concrete declaration of this shift. Three key developments are highlighted:

Columnar Mode : Introduces a column‑store index that stores each field once, reduces storage, speeds analytical queries, and can coexist with existing inverted indexes without API changes.

VectorDB Auto‑Calibration : Turns vector search from a highly tunable component into an out‑of‑the‑box feature, lowering the barrier that previously stalled many RAG implementations.

PromQL Native Support : Directly supports PromQL, positioning Elastic to capture the observability market and enable Grafana/Prometheus users to migrate workloads seamlessly.

These moves clarify Elastic’s roadmap toward supporting AI agents as a foundational platform.

Easysearch’s assessment aligns with this direction but focuses on practical enterprise concerns. It argues that the next competitive edge is not raw full‑text speed but the ability to handle high‑frequency AI workloads reliably and cost‑effectively. Easysearch proposes three practices:

Native Integration, No Middleware : Recommend using elasticsearch‑py directly instead of adding a LangChain VectorStore abstraction, which would introduce uncontrolled performance overhead.

AIOps Scenarios First : Deploy diagnostics such as LogDiag (log anomaly detection) and SlowQueryDiag (slow‑query self‑diagnosis) as AI‑callable tools, providing tangible value beyond conceptual promises.

Hybrid Retrieval Without Hype : Continue using BM25 where it excels, and employ vector search only where semantic recall is needed, avoiding unnecessary vectorization.

In summary, Elastic follows a high‑end license + commercial subscription model, while Easysearch pursues a domestic, service‑oriented approach without locking capabilities behind a license. Despite different business models, both are converging toward the AI Infra paradigm.