Hex cost tuning

Hex Cost Tuning

Overview

Hex pricing combines per-seat licensing with compute-based charges for notebook runs and data connections. Each scheduled or ad-hoc notebook execution consumes compute credits proportional to query complexity and data volume processed. Organizations running dozens of notebooks on hourly schedules — many producing identical results from unchanged data — accumulate unnecessary compute costs. Caching run results, optimizing schedules, and consolidating redundant notebooks are the highest-leverage cost reduction strategies.

Cost Breakdown

Component	Cost Driver	Optimization
Seat licenses	Per-user/month (Team: $28/user)	Audit active editors quarterly; move viewers to free tier
Notebook runs	Compute per scheduled or manual execution	Cache results for unchanged data; extend run intervals
Data connections	Active warehouse/database connections	Consolidate overlapping connections; remove unused ones
Scheduled runs	Cron-triggered executions across all projects	Audit schedules — reduce frequency for stable data
API calls	Admin and Run API requests	Batch API operations; use cached results endpoint

API Call Reduction

class HexRunOptimizer {
  private resultCache = new Map<string, { data: any; timestamp: number }>();
  private dataHashes = new Map<string, string>();

  async runIfChanged(projectId: string, runFn: () => Promise<any>): Promise<any> {
    const currentHash = await this.getSourceDataHash(projectId);
    if (this.dataHashes.get(projectId) === currentHash) {
      const cached = this.resultCache.get(projectId);
      if (cached) return cached.data; // Source unchanged — serve cached result
    }
    const result = await runFn();
    this.resultCache.set(projectId, { data: result, timestamp: Date.now() });
    this.dataHashes.set(projectId, currentHash);
    return result;
  }

  private async getSourceDataHash(projectId: string): Promise<string> {
    const res = await fetch(`/api/v1/project/${projectId}/status`);
    return (await res.json()).sourceDataHash;
  }
}

Usage Monitoring

class HexCostMonitor {
  private runs = new Map<string, number[]>();
  private weeklyBudget = 500; // max runs per week

  recordRun(projectId: string): void {
    const timestamps = this.runs.get(projectId) || [];
    timestamps.push(Date.now());
    this.runs.set(projectId, timestamps);
  }

  getWeeklyReport(): { totalRuns: number; byProject: Record<string, number> } {
    const weekAgo = Date.now() - 7 * 24 * 60 * 60 * 1000;
    const byProject: Record<string, number> = {};
    let total = 0;
    for (const [id, stamps] of this.runs) {
      const count = stamps.filter(t => t > weekAgo).length;
      byProject[id] = count;
      total += count;
    }
    return { totalRuns: total, byProject };
  }
}

Cost Optimization Checklist

• Cache notebook results with updateCacheResult: true
• Skip re-runs when source data is unchanged
• Audit scheduled run frequencies — extend intervals for stable data
• Move read-only users from Team to free viewer tier
• Consolidate duplicate notebooks querying the same data
• Remove unused data connections
• Set weekly run budget alerts at 80% threshold
• Identify overrun projects (>20 runs/week) for schedule review

Error Handling

Issue	Cause	Fix
Compute costs spiking	Hourly schedules on notebooks with daily-changing data	Extend schedule to match data refresh cadence
Stale cached results	Source data changed but cache not invalidated	Use source data hash comparison before serving cache
API rate limit (429)	Too many concurrent run triggers	Queue runs with concurrency limit of 3
Unused notebooks accruing runs	Abandoned projects still on schedule	Audit and disable schedules for inactive projects
Connection pool exhausted	Too many simultaneous data source queries	Consolidate connections; stagger scheduled run times

Resources

• Hex Pricing
• Hex API Documentation

Next Steps

See hex-performance-tuning.