Beyond Monitoring: AI-Driven Observability with VAST DataBase

Observability today is fragmented: metrics live in one tool, logs in another, traces in a third, security telemetry in a fourth. Teams stitch signals together across vendors because no single platform can ingest, store, and query the full firehose at the volumes modern systems produce. The result is blind spots, delayed root-cause analysis, and runaway tooling cost.

In this session, we explore how VAST Database's streaming ingest and scale make it practical to consolidate observability, logs, metrics, traces, and security events, onto a single data platform. We will show how high-throughput streaming, real-time queryability, and effectively unlimited retention let teams replace a sprawl of point tools with one consolidated store, without sampling or tiering data away.

We will also demonstrate how this consolidation unlocks workflows that fragmented stacks cannot — correlating signals across pillars, querying months of raw telemetry in real time, and applying analytics and AI directly to the consolidated dataset.

Key Takeaways

• Why traditional observability stacks fragment as data volumes grow

• How VAST Database's streaming ingest and scale enable consolidation onto one platform

• Real-time queryability over the full dataset — no sampling, no cold tiers

• What becomes possible when every observability signal lives in one queryable store

Looking for more? This session is part of From Data to AI: VAST Analytics Explained, a free 8-part technical series on building modern analytics and AI systems on VAST AI OS. Register for more sessions here.