• Pricing
Book a demo

Optimize Fauna performance with intelligent index management

Swiftask allows your AI agents to monitor and manage your Fauna indexes. Ensure fast response times without constant manual intervention.

Result:

Boost development velocity and optimize query costs through dynamic indexing.

Manual index maintenance slows your growth

Managing indexes in a distributed database like Fauna is complex. Forgetting an index degrades query performance, while creating too many increases costs. Engineering teams lose valuable time manually adjusting these configurations.

Main negative impacts:

  • Increased application latency: Without optimal indexing, your queries become inefficient, directly impacting the end-user experience.
  • High operational complexity: Constant index tuning requires deep technical expertise and distracts engineers from their core missions.
  • Uncontrolled query costs: A poor indexing strategy leads to excessive consumption of Fauna resources, unnecessarily inflating your bill.

Swiftask deploys AI agents capable of analyzing your query needs and managing your Fauna indexes automatically. You stay in control while benefiting from continuous optimization.

BEFORE / AFTER

What changes with Swiftask

Manual approach

A developer identifies a slow query. They must manually analyze the execution plan, design the appropriate index, test its impact, then deploy it. This cycle takes hours and risks being ignored until a performance crisis occurs.

Automation with Swiftask

Your AI agent monitors your Fauna query usage. It automatically detects bottlenecks and proposes or applies optimal indexes based on real access patterns, instantly.

Deploy your indexing strategy in 4 steps

STEP 1 : Define rules

Configure performance thresholds and indexing policies within the Swiftask agent.

STEP 2 : Connect Fauna

Establish a secure link with your Fauna instance via API to allow the agent to audit schemas.

STEP 3 : Intelligent analysis

The agent examines query logs and identifies optimization opportunities through indexing.

STEP 4 : Execution and monitoring

The agent applies recommended changes and monitors the impact on performance in real time.

AI-driven control for Fauna

The agent analyzes read/write patterns, the frequency of complex queries, and the growth of your collections.

  • Target connector: The agent performs the right actions in fauna based on event context.
  • Automated actions: Automatic index creation, deletion of unused indexes, optimization of search terms, security audit of data access.
  • Native governance: All actions are logged in a full audit trail for complete traceability.

Each action is contextualized and executed automatically at the right time.

Each Swiftask agent uses a dedicated identity (e.g. agent-fauna@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.

Why automate with Swiftask

1. Consistent performance

Your queries remain fast even with significant data scaling.

2. Productivity gains

Free your technical team from repetitive database administration tasks.

3. Cost optimization

Reduce resource consumption by eliminating redundant or inefficient indexes.

4. Enhanced security

Strict governance of access and modifications to your database schema.

5. Agile adaptability

Instantly adapt your indexing strategy to new application usage patterns.

Security and data integrity

Swiftask applies enterprise-grade security standards for your fauna automations.

  • Secure access: Use of restricted API keys following the principle of least privilege.
  • Change validation: Option to enable 'human approval' mode before any structural modification.
  • Full audit: Exhaustive history of all indexing operations for compliance.
  • Environmental isolation: Separate configuration for your staging and production environments.

To learn more about compliance, visit the Swiftask governance page for detailed security architecture information.

RESULTS

Impact on your technical metrics

MetricBeforeAfter
Average query latencyHigh (variable)Optimized (-40% on average)
Administration timeSeveral hours/weekAutomated (0 intervention)
Query costsUnoptimizedSignificant reduction
Bottleneck detectionReactive (after incident)Proactive (real-time)

Take action with fauna

Boost development velocity and optimize query costs through dynamic indexing.

Drive your business with live KPIs from Fauna

Next use case