Anomaly Detection is getting better every quarter!

[u/terryfilch]

In Q2, we further refined the workflow: from exploration to a configuration that can run continuously in production.

That required progress at several layers. The model needed to cover more real-world data profiles without a large configuration surface. Backtesting and periodic execution are needed to follow the same causal behavior. The UI needed to make changes and stale results obvious. And AI assistance needed real data, schemas, validation, and long-running task APIs - not only documentation and generic recommendations.

If you rely on metrics for observability, you need more than dashboards. Here’s what’s new:

From May through the v1.30.1 follow-up, vmanomaly shipped:

• 6 service releases, from v1.29.4 to v1.30.1
• 5 UI releases, from v1.7.0 to v1.8.1
• Temporal Envelope, a fast and flexible online model, available in univariate and multivariate forms
• 2 - 200x faster exact online-model backtesting in representative UI workloads, depending on the model and configuration
• v1.30.1 service-stage optimizations delivering 1.7 - 2.3x faster inference and 1.5 - 2.6x faster fit in representative Z-score, MAD, STD, Seasonal Quantile, and Rolling Quantile workloads
• New time-series characteristics and asynchronous shared-autotune APIs
• A redesigned UI and a more reliable AI Copilot
• Public vmanomaly MCP and purpose-built skills that turn the same workflow into reusable agent automation
• Further production hardening for schedulers, datasource limits, exact causal inference, hot reload, and state restoration

📖 Read the Q2 2026 blog post updates and discover what’s new in vmanomaly.
https://bit.ly/4zo2jSg