Autonomous FinOps Sentinel: Orchestrating TimescaleDB Anomaly Detection with Google Gemini AI in Kestra

A developer submitted an official community blueprint to Kestra called the Autonomous TimescaleDB + Gemini AI FinOps Sentinel, which monitors cloud spending timeseries in TimescaleDB continuous aggregates, flags statistical spending spikes via Z-score anomaly detection, and uses Google Gemini 1.5 Flash to produce structured root-cause diagnoses and remediation steps. The event-driven Kestra workflow runs hourly, routing CRITICAL or HIGH severity incidents to Slack Block Kit alerts while silently logging nominal status to cut alert fatigue.

What I Built For this week's Hacktoberfest Open-Source AI & Database Challenge, I built and submitted an official community blueprint to Kestra : the Autonomous TimescaleDB + Gemini AI FinOps Sentinel. It creates a real-time, automated financial surveillance system that monitors cloud spending timeseries in TimescaleDB Continuous Aggregates CAGGs , detects statistical spending spikes, invokes Google Gemini 1.5 Flash for contextual root-cause diagnosis, and orchestrates remediation alerts directly into Slack. 🐙 Upstream Pull Request: kestra-io/blueprints Timescale + Gemini Sentinel 🐯 Database Engine: TimescaleDB / Tiger Cloud 🤖 AI Engine: Google Gemini 1.5 Flash API ⚡ Workflow Orchestrator: Kestra The Problem: The "Silent Cloud Bill Surge" Every engineering team has experienced the dreaded end-of-month cloud billing surprise: Uncoordinated Provisioning: A developer spins up on-demand GPU clusters p4d.24xlarge or an unthrottled LLM embedding pipeline, forgetting to terminate it on Friday night. Static Alert Fatigue: Traditional alerts rely on rigid threshold rules e.g., "notify if spend $1,000" . When baseline traffic naturally grows, alerts cry wolf and engineers ignore them. Missing Context: A raw metric alert like ALERT Spend is $3,200 doesn't explain why it spiked or what to do, forcing on-call engineers to spend hours triaging raw AWS/GCP dashboards. The Architecture TimescaleDB Hypertables & CAGGs │ ▼ Hourly Rollups Statistical Z-Score Anomaly Isolation │ ▼ Anomaly JSON Payload Google Gemini 1.5 Flash Root-Cause Diagnosis │ ▼ Structured JSON: Severity + Remedy Kestra Declarative Flow Control If Severity = HIGH ┌─────┴─────┐ ▼ ▼ CRITICAL/HIGH NOMINAL │ │ ├─► Slack Block Kit Alert └─► Kestra Audit Trail KV Store How It Works in Kestra The entire pipeline is orchestrated as an event-driven declarative workflow in Kestra timescale-gemini-financial-anomaly-sentinel.yaml : The workflow triggers at the top of every hour io.kestra.plugin.core.trigger.Schedule . It calculates rolling standard deviations over recent hourly buckets from Timescale continuous aggregates: for cat in categories: mean = sum baseline spends / len baseline spends variance = sum x - mean 2 for x in baseline spends / len baseline spends std dev = math.sqrt variance if variance 0 else 1.0 z score = current spend - mean / std dev if z score = threshold: anomalies.append { "category": cat, "current spend usd": round current spend, 2 , "historical mean usd": round mean, 2 , "z score": round z score, 2 , "percentage spike": round current spend - mean / mean 100, 1 , "vendor hints": "AWS US-East GPU instances", "Overlapping on-demand EC2 clusters" } Rather than sending a cryptic number to Slack, the anomaly vector is dispatched to Google Gemini 1.5 Flash. Gemini evaluates historical variance, correlates vendor hints, and outputs a structured incident response: json { "severity": "CRITICAL", "summary": "Cloud Infrastructure spend surged by 470% Z-Score: 4.2 due to uncoordinated on-demand GPU instance provisioning.", "remediation action": "Audit active AWS us-east EC2 clusters and terminate orphaned p4d.24xlarge on-demand instances immediately.", "urgency score": 9 } Kestra's io.kestra.plugin.core.flow.If evaluates the urgency returned by Gemini: If CRITICAL or HIGH: It formats an interactive Slack Block Kit alert directed at finops-alerts with the exact remediation steps, and commits the incident ID into Kestra's persistent KV store. If LOW or MODERATE: It silently logs nominal status, completely eliminating alert fatigue. yaml Key Lessons & Hacktoberfest Takeaways Continuous Aggregates in Timescale + AI = Real-Time FinOps: Calculating rolling metrics on raw transaction tables takes hundreds of milliseconds; Timescale's continuous aggregates pre-compute the time buckets, enabling sub-millisecond anomaly feeds into Gemini. AI-Enriched Alerts Change Incident Response: An on-call engineer receiving a pre-diagnosed Slack notification with immediate action items can resolve incidents in minutes rather than spending hours chasing dashboard charts. Open-Source Power: By packaging this as an upstream Kestra Blueprint, any team running Kestra and PostgreSQL can deploy this automated sentinel in under 5 minutes. Built with TimescaleDB, Google Gemini, and Kestra for Hacktoberfest 2026