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During this session we’ll discuss ElastiFlow’s Edge Observability strategy, which includes an OTel native edge processing node with local DuckDB storage for all OTel signals and an agentic AI system that is model agnostic (we often run it with OpenAI’s gpt-oss-20b), exposing its tools through an MCP server.
Instead of just forwarding OTel signals from various Edge collectors, the signals are analyzed and routing decisions are made. Alerts are sent to the Observability Platform right away, while logs are stored locally and analyzed for patterns. Instead of forwarding all logs, we might only care about a few conditions of interest, often correlated with other signals, and send these to the Observability Platform, while less interesting logs can be aggressively aggregated.
With this approach, we were able to reduce the storage and ingest cost of Observability Platforms by half while actually decreasing the mean time to insight.
Alex has been building Automation and Observability products for 10+ years and has been advocating to break down silos between operations teams ever since.
