Perplexity Cost Tuning

Perplexity Cost Tuning

Overview

Reduce Perplexity AI search API costs by choosing the right model per query, caching search results, and limiting token output. Perplexity charges per query with significant cost differences between models: sonar (lightweight, cheaper) vs sonar-pro (deeper research, 3-5x more expensive).

Prerequisites

• Perplexity API account with usage dashboard
• Understanding of query patterns in your application
• Cache infrastructure for search results

Instructions

Step 1: Route Queries to the Right Model

// Smart model selection based on query complexity
function selectModel(query: string, context: string): 'sonar' | 'sonar-pro' {
  // Simple factual queries -> sonar (cheap)
  const simplePatterns = [/^what is/i, /^define/i, /^who is/i, /^when did/i, /current price of/i];
  if (simplePatterns.some(p => p.test(query))) return 'sonar';

  // Complex research queries -> sonar-pro (expensive but worth it)
  const complexPatterns = [/compare.*vs/i, /analysis of/i, /comprehensive.*overview/i, /pros and cons/i];
  if (complexPatterns.some(p => p.test(query))) return 'sonar-pro';

  return 'sonar'; // Default to cheaper model
}
// Impact: 60-70% of queries can use sonar, saving 3-5x per query

Step 2: Cache Search Results

import { LRUCache } from 'lru-cache';

const searchCache = new LRUCache<string, any>({
  max: 10000,  # 10000: 10 seconds in ms
  ttl: 4 * 3600_000, // 4-hour TTL (search results go stale)
});

async function cachedQuery(query: string, model: string) {
  const key = `${model}:${query.toLowerCase().trim()}`;
  const cached = searchCache.get(key);
  if (cached) return cached;

  const result = await perplexity.chat.completions.create({
    model, messages: [{ role: 'user', content: query }],
  });
  searchCache.set(key, result);
  return result;
}

Step 3: Limit Token Output

set -euo pipefail
# Set max_tokens to reduce output (and cost) for simple queries
# Factual queries need ~100 tokens, not 4096
curl -X POST https://api.perplexity.ai/chat/completions \
  -H "Authorization: Bearer $PPLX_API_KEY" \
  -d '{
    "model": "sonar",
    "messages": [{"role": "user", "content": "What is the current population of Tokyo?"}],
    "max_tokens": 100
  }'
# Saves tokens on output; Perplexity charges for both input and output tokens

Step 4: Use Domain Filters to Reduce Processing

set -euo pipefail
# Restricting search domains means less content to process = faster + cheaper
curl -X POST https://api.perplexity.ai/chat/completions \
  -H "Authorization: Bearer $PPLX_API_KEY" \
  -d '{
    "model": "sonar",
    "messages": [{"role": "user", "content": "latest Python 3.13 release notes"}],
    "search_domain_filter": ["python.org", "docs.python.org"],
    "max_tokens": 500  # HTTP 500 Internal Server Error
  }'

Step 5: Monitor and Budget

set -euo pipefail
# Track daily spend and project monthly cost
curl -s https://api.perplexity.ai/v1/usage \
  -H "Authorization: Bearer $PPLX_API_KEY" | \
  jq '{
    queries_today: .queries_today,
    cost_today: .cost_today_usd,
    monthly_projection: (.cost_today_usd * 30),
    budget_remaining: .budget_remaining_usd,
    avg_cost_per_query: (.cost_today_usd / (.queries_today + 0.01))
  }'

Error Handling

Examples

Basic usage: Apply perplexity cost tuning to a standard project setup with default configuration options.

Advanced scenario: Customize perplexity cost tuning for production environments with multiple constraints and team-specific requirements.

Output

• Configuration files or code changes applied to the project
• Validation report confirming correct implementation
• Summary of changes made and their rationale

Resources

• Official monitoring documentation
• Community best practices and patterns
• Related skills in this plugin pack