Swiftask connects your AI agents to Tinybird to monitor and tune your performance in real-time, ensuring lightning-fast queries.
Result:
Reduce API latency and optimize computing costs automatically, without complex manual intervention.
The complexity of Tinybird tuning slows your teams down
Maintaining optimal performance on massive real-time data streams is a constant challenge. Queries become inefficient, latency spikes, and identifying bottlenecks consumes precious engineering time.
Main negative impacts:
Swiftask deploys AI agents that continuously analyze your Tinybird metrics. They identify slow queries and suggest or automatically apply the necessary optimizations.
BEFORE / AFTER
What changes with Swiftask
Traditional manual tuning
An engineer must manually inspect query logs, identify unindexed columns or expensive joins, test alternatives, and redeploy. Meanwhile, performance remains degraded.
Continuous optimization with Swiftask
The Swiftask AI agent monitors your Tinybird endpoints 24/7. As soon as a performance anomaly is detected, it analyzes the execution plan and proposes targeted optimizations or executes automatic adjustments.
Improve your Tinybird queries in 4 steps
STEP 1 : Connect Tinybird to your Swiftask agent
Use Tinybird APIs to link your project to a Swiftask agent. No complex infrastructure required.
STEP 2 : Configure performance thresholds
Define key indicators: maximum response time, error rates, or resource consumption, to trigger the analysis.
STEP 3 : Let the AI analyze your queries
The agent inspects the context of slow queries and compares execution plans to isolate inefficiencies.
STEP 4 : Apply optimizations
Validate the agent's recommendations to update your schemas or optimize your SQL queries in one click.
AI optimization features for Tinybird
The agent analyzes execution plans, data distribution, column cardinality, and index usage to provide precise recommendations.
Each action is contextualized and executed automatically at the right time.
Each Swiftask agent uses a dedicated identity (e.g. agent-tinybird@swiftask.ai ). You keep full visibility on every action and every sent message.
Key takeaway: The agent automates repetitive decisions and leaves high-value actions to your teams.
Strategic benefits of automated tuning
1. Drastic latency reduction
Ensure constant API responsiveness, regardless of data volume.
2. Cost optimization
More efficient queries consume fewer Tinybird compute resources.
3. Saved engineering time
Free your teams from repetitive tuning tasks to focus on product innovation.
4. Proactive governance
Maintain your performance standards with continuous, automated monitoring.
5. Continuous improvement
The agent learns from your data patterns to refine its recommendations over time.
Data security and integrity
Swiftask applies enterprise-grade security standards for your tinybird automations.
To learn more about compliance, visit the Swiftask governance page for detailed security architecture information.
RESULTS
Measurable impact on your data
| Metric | Before | After |
|---|---|---|
| Average query latency | High and unstable | Reduced by 40% on average |
| Anomaly detection time | Several days | Within seconds |
| Engineer time spent tuning | 10+ hours/week | Less than one hour |
| Compute costs | Limited manual optimization | Optimized reduction |
Take action with tinybird
Reduce API latency and optimize computing costs automatically, without complex manual intervention.