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):
- Hourly Timeseries Anomaly Detection via TimescaleDB
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:
Extract from detect_timeseries_anomalies task
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"]
})
- Contextual Root-Cause Diagnosis via Google Gemini AI
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
}
- Declarative Flow Control & Slack Alerting
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
- id: evaluate_severity type: io.kestra.plugin.core.flow.If condition: "{{ outputs.gemini_root_cause_analysis.vars.severity == 'CRITICAL' || outputs.gemini_root_cause_analysis.vars.severity == 'HIGH' }}" then:
- id: record_incident_in_kv type: io.kestra.plugin.core.kv.Set key: "incident_{{ execution.id }}" value: "{{ outputs.gemini_root_cause_analysis.vars.summary }}"
- id: notify_slack_channel type: io.kestra.plugin.notifications.slack.SlackIncomingWebhook url: "{{ secret('SLACK_WEBHOOK_URL') }}"
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!#devchallenge #tigerdata #hf26challenge #ai #software #coding #development #engineering #inclusive #community
Autonomous FinOps Sentinel: Orchestrating TimescaleDB Anomaly Detection with Google Gemini AI in Kestra