Apache Doris gives AI applications real-time access to fresh, trusted, and queryable enterprise data. Teams can build data-aware agents, improve RAG quality, monitor AI behavior, and run analytics and hybrid search across structured, semi-structured, and unstructured data at scale.
When the analytical foundation is fresh, hybrid, observable, and unified, five things shift at once for AI: decision quality, application context, retrieval relevance, agent visibility, and the cost of running it all.
AI agents need fresh business context, not yesterday’s batch data. Real-time analytics lets them query live operational data, detect changes, and act while the user interaction or business process is still happening.
Five things a modern AI-ready analytics platform has to do well, and the specific Apache Doris capabilities that meet each one.
AI applications and autonomous agents need to act on fresh data in real time. New events, CDC updates, and streaming data must become queryable within seconds, while analytical queries need to return in sub-second time—even under high-concurrency production traffic.
AI observability goes beyond logs, metrics, and traces. Teams need to analyze model calls, prompts, responses, tool executions, retrieval events, token usage, latency, cost, evaluation scores, and user feedback in one place. The serving layer must handle flexible schemas, fast search, and interactive aggregation across large-scale AI-native data.
AI-native applications generate constantly changing, semi-structured events from models, agents, tools, frameworks, and workflows. The platform must handle dynamic JSON, nested fields, schema evolution, fast filtering, and ad hoc SQL exploration without heavy ETL, so teams can analyze every agent step, tool call, retrieval event, and model response directly.
AI applications search across documents, logs, prompts, responses, feedback, knowledge bases, support tickets, and embeddings. SQL, keyword search, and vector search each solve part of the problem. AI-ready analytics combines structured filters, full-text search, semantic similarity, and relevance-aware ranking in one workflow.
AI applications rely on a fast-changing ecosystem of agent frameworks, LLMOps platforms, observability tools, MCP workflows, and custom pipelines. The platform must connect to that ecosystem so agents, copilots, RAG systems, and LLMOps tools can read trusted data and write AI signals through SQL, APIs, connectors, and MCP.
AI applications and customer-facing analytics share the same hard requirement: ingest events in seconds, serve queries in sub-second time, under high concurrency. Apache Doris meets both with streaming and CDC ingestion, real-time query visibility, and an MPP execution engine — so AI agents and user-facing dashboards can run on one analytical foundation instead of two.
