Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/exa-cost-tuning/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill exa-cost-tuning. Copyright stays with the author (MIT).

Exa Cost Tuning

Prerequisites

• An aggregate usage/cost baseline, budget owner, approved data policy, and quality/latency SLO.
• Sanitized evaluation queries and a reversible change record.

Output

• A measured cost optimization with owner, policy/quality guardrails, and rollback threshold.

Examples

Compare aggregate request volume, model/search options, latency, quality, and error data in staging, change one approved caching or request-shaping parameter, and observe the result. Revert on quality, policy, or reliability regression; do not reduce privacy, citation, or source-validation safeguards merely to lower spend.

Overview

Reduce Exa API costs through strategic search type selection, result caching, query deduplication, and usage monitoring. Exa charges per search request with costs varying by search type and content retrieval options.

Cost Drivers

Instructions

Step 1: Match Search Config to Use Case

import Exa from "exa-js";

const exa = new Exa(process.env.EXA_API_KEY);

// Define cost tiers per use case
const SEARCH_PROFILES = {
  // Cheapest: metadata-only keyword search
  "autocomplete": { type: "instant" as const, numResults: 3 },

  // Low cost: fast search with minimal content
  "quick-lookup": { type: "fast" as const, numResults: 3 },

  // Medium: balanced search for RAG
  "rag-context": {
    type: "auto" as const,
    numResults: 5,
    text: { maxCharacters: 1000 },
  },

  // Higher cost: deep research
  "deep-research": {
    type: "neural" as const,
    numResults: 10,
    text: { maxCharacters: 3000 },
    highlights: { maxCharacters: 500 },
  },
};

async function costAwareSearch(
  query: string,
  profile: keyof typeof SEARCH_PROFILES
) {
  const config = SEARCH_PROFILES[profile];
  if ("text" in config || "highlights" in config) {
    return exa.searchAndContents(query, config);
  }
  return exa.search(query, config);
}

Step 2: Query-Level Caching (40-60% Cost Reduction)

import { LRUCache } from "lru-cache";

const searchCache = new LRUCache<string, any>({
  max: 5000,
  ttl: 3600 * 1000, // 1-hour TTL
});

async function cachedSearch(query: string, opts: any) {
  const key = `${query.toLowerCase().trim()}:${opts.type}:${opts.numResults}`;
  const cached = searchCache.get(key);
  if (cached) return cached;

  const results = await exa.searchAndContents(query, opts);
  searchCache.set(key, results);
  return results;
}
// Typical RAG cache hit rate: 40-60%, directly cutting costs in half

Step 3: Query Deduplication for Batch Jobs

function deduplicateQueries(queries: string[]): string[] {
  const seen = new Set<string>();
  return queries.filter(q => {
    const normalized = q.toLowerCase().trim().replace(/\s+/g, " ");
    if (seen.has(normalized)) return false;
    seen.add(normalized);
    return true;
  });
}

// Before batch processing, deduplicate
const uniqueQueries = deduplicateQueries(allQueries);
console.log(`Deduped: ${allQueries.length} → ${uniqueQueries.length} queries`);
// Typical dedup rate: 20-40% for batch processing

Step 4: Use Keyword Search When Appropriate

// Neural search: best for semantic/conceptual queries (more expensive)
// Keyword search: best for specific terms/names (cheaper, faster)

function selectCostEffectiveType(query: string): "neural" | "keyword" | "auto" {
  // Use keyword for exact lookups
  if (query.match(/^https?:\/\//)) return "keyword";     // URL lookup
  if (query.match(/^[A-Z][a-z]+ [A-Z]/)) return "keyword"; // Proper nouns
  if (query.includes('"')) return "keyword";               // Quoted terms

  // Use neural for conceptual queries
  if (query.split(" ").length > 5) return "neural";
  return "auto"; // Let Exa decide for ambiguous queries
}

Step 5: Monitor Usage and Set Budget Alerts

set -euo pipefail
# Check API key usage
curl -s https://api.exa.ai/v1/usage \
  -H "x-api-key: $EXA_API_KEY" | \
  python3 -c "
import json, sys
d = json.load(sys.stdin)
print(f'Searches today: {d.get(\"searches_today\", \"N/A\")}')
print(f'Monthly total: {d.get(\"searches_this_month\", \"N/A\")}')
print(f'Monthly limit: {d.get(\"monthly_limit\", \"N/A\")}')
" 2>/dev/null || echo "Usage endpoint not available"

// Application-level budget tracking
class ExaBudgetTracker {
  private searchCount = 0;
  private dailyLimit: number;

  constructor(dailyLimit = 1000) {
    this.dailyLimit = dailyLimit;
  }

  async search(exa: Exa, query: string, opts: any) {
    if (this.searchCount >= this.dailyLimit) {
      throw new Error(`Daily Exa budget exceeded (${this.dailyLimit} searches)`);
    }
    this.searchCount++;
    return exa.search(query, opts);
  }

  getUsage() {
    return {
      used: this.searchCount,
      remaining: this.dailyLimit - this.searchCount,
      utilization: `${((this.searchCount / this.dailyLimit) * 100).toFixed(1)}%`,
    };
  }
}

Cost Optimization Checklist

• Use keyword or fast for exact lookups instead of neural
• Reduce numResults to 3-5 for most use cases (default is 10)
• Use highlights instead of full text when snippets suffice
• Implement query-level caching (LRU or Redis)
• Deduplicate queries in batch pipelines
• Set application-level budget limits
• Monitor daily/monthly usage against budget

Error Handling

Resources

• Exa Pricing
• Exa API Usage
• Exa Search Types

Next Steps

For performance optimization, see exa-performance-tuning. For reliability, see exa-reliability-patterns.