Caching Strategy for API-Heavy Workflows

Building robust sales automation often means connecting multiple APIs to create seamless data flows. But here’s the problem: without a proper caching strategy api workflows, you’ll quickly run into rate limits, inflated costs, and sluggish performance that frustrates your team.

I’ve seen companies burn through their API quotas by noon because they were making redundant calls to fetch the same data repeatedly. Others have faced system timeouts during critical sales processes because they didn’t cache frequently accessed information.

This guide shows you how to implement smart caching strategies that keep your API-heavy workflows running smoothly, reduce costs, and improve reliability.

Why Caching Matters for API Workflows

API calls are expensive – not just in terms of money, but also time and reliability. When your workflow makes the same API call multiple times within minutes, you’re wasting resources and creating unnecessary points of failure.

Consider this scenario: Your lead scoring workflow needs to enrich contact data from three different APIs – Clearbit for company information, ZoomInfo for contact details, and your CRM for existing interaction history. Without caching, each lead triggers nine separate API calls (three services × three data points). With 100 leads per hour, that’s 900 API calls.

Smart caching reduces this to maybe 200-300 calls by storing recently fetched data and reusing it for similar requests.

Core Caching Strategies

1. Time-Based Caching (TTL)

Time-To-Live caching stores API responses for a predetermined period. This works well for data that doesn’t change frequently.

Implementation Example:

// Pseudo-code for TTL caching in workflow
const CACHE_DURATION = 3600000; // 1 hour in milliseconds

function getCachedCompanyData(domain) {
  const cacheKey = `company_${domain}`;
  const cached = storage.get(cacheKey);
  
  if (cached && (Date.now() - cached.timestamp) < CACHE_DURATION) {
    return cached.data;
  }
  
  // Fetch fresh data
  const freshData = await clearbitAPI.getCompany(domain);
  storage.set(cacheKey, {
    data: freshData,
    timestamp: Date.now()
  });
  
  return freshData;
}

Best for: Company information, user profiles, configuration data TTL recommendations:

• Company data: 24 hours
• User profiles: 1-4 hours
• Product catalogs: 6-12 hours

2. Conditional Caching

This strategy caches based on specific conditions or data characteristics rather than just time.

function getCachedLeadScore(leadId, lastActivity) {
  const cacheKey = `lead_score_${leadId}`;
  const cached = storage.get(cacheKey);
  
  // Only use cache if no new activity since last calculation
  if (cached && cached.lastActivityDate >= lastActivity) {
    return cached.score;
  }
  
  const newScore = await calculateLeadScore(leadId);
  storage.set(cacheKey, {
    score: newScore,
    lastActivityDate: lastActivity
  });
  
  return newScore;
}

Best for: Calculated values, scores, aggregated metrics

3. Hierarchical Caching

Cache at multiple levels to maximize efficiency. Start with the most specific cache and fall back to broader caches.

function getCachedData(userId, companyId, dataType) {
  // Level 1: User-specific cache
  const userCache = storage.get(`${userId}_${dataType}`);
  if (userCache && isValid(userCache)) return userCache.data;
  
  // Level 2: Company-wide cache
  const companyCache = storage.get(`${companyId}_${dataType}`);
  if (companyCache && isValid(companyCache)) return companyCache.data;
  
  // Level 3: Fetch fresh data
  return await fetchFreshData(userId, companyId, dataType);
}

Platform-Specific Implementation

Zapier Caching Techniques

Zapier doesn’t have built-in caching, but you can create effective caching using storage apps:

Method 1: Google Sheets as Cache

// Step 1: Check cache (Google Sheets lookup)
const cacheCheck = {
  search_column: "A", // Domain column
  search_value: "{{lead_domain}}",
  return_column: "B" // Cached data column
};

// Step 2: Only proceed if cache miss
const filter = {
  condition: "{{cache_result}} is empty"
};

// Step 3: Store fresh data back to cache
const cacheStore = {
  domain: "{{lead_domain}}",
  data: "{{api_response}}",
  timestamp: "{{current_timestamp}}"
};

Method 2: Airtable as Smart Cache Create an Airtable base with these fields:

• cache_key (Single line text)
• cached_data (Long text)
• expiry_date (Date/time)
• hit_count (Number)

Make.com Advanced Caching

Make.com’s data stores provide more sophisticated caching options:

// Custom caching module
const cacheModule = {
  get: function(key) {
    return DataStore.get(key);
  },
  
  set: function(key, value, ttl = 3600) {
    return DataStore.set(key, {
      value: value,
      expires: Date.now() + (ttl * 1000)
    });
  },
  
  isValid: function(cached) {
    return cached && Date.now() < cached.expires;
  }
};

Real-World War Stories

The HubSpot Rate Limit Crisis

A client’s lead enrichment workflow was hitting HubSpot’s API limits every day by 2 PM. They were making contact property calls for every single lead, even when processing leads from the same company repeatedly.

The problem: 50 leads from Salesforce Corp meant 50 identical API calls to fetch the same company data.

The solution: Implemented company-level caching with 6-hour TTL:

• Reduced API calls by 73%
• Eliminated rate limit issues
• Improved workflow speed by 2.5x

// Before: Individual contact enrichment
leads.forEach(lead => {
  const contactData = hubspotAPI.getContact(lead.email);
  const companyData = hubspotAPI.getCompany(contactData.company);
});

// After: Cached company lookups
const companyCache = new Map();
leads.forEach(lead => {
  const contactData = hubspotAPI.getContact(lead.email);
  
  if (!companyCache.has(contactData.company)) {
    companyCache.set(contactData.company, 
      hubspotAPI.getCompany(contactData.company)
    );
  }
  
  const companyData = companyCache.get(contactData.company);
});

The Clearbit Cost Explosion

Another client saw their Clearbit bill jump from $400 to $2,800 in one month due to a poorly configured enrichment workflow that was re-enriching the same prospects multiple times.

Root cause: No deduplication or caching meant the same email addresses were being enriched 5-10 times per week.

Fix: Added intelligent caching based on email domain and data freshness:

function shouldEnrich(email, lastEnriched) {
  const domainCache = getCachedDomainData(extractDomain(email));
  
  // Skip if domain enriched recently
  if (domainCache && isRecent(domainCache.timestamp, 7)) {
    return false;
  }
  
  // Skip if contact enriched recently  
  if (lastEnriched && isRecent(lastEnriched, 30)) {
    return false;
  }
  
  return true;
}

Results:

• 87% reduction in Clearbit API calls
• Monthly costs dropped to $510
• Data quality improved (fewer stale overwrites)

Cache Invalidation Strategies

Knowing when to clear your cache is as important as knowing when to use it.

Event-Based Invalidation

Clear cache when specific events occur:

// Clear user cache when profile updated
webhook.on('user.updated', (userId) => {
  cache.delete(`user_${userId}`);
  cache.delete(`user_preferences_${userId}`);
});

// Clear company cache on significant changes
webhook.on('company.updated', (companyId, changes) => {
  const significantFields = ['industry', 'size', 'location'];
  
  if (changes.some(field => significantFields.includes(field))) {
    cache.deletePattern(`company_${companyId}_*`);
  }
});

Probabilistic Invalidation

Randomly refresh cache entries to prevent thundering herd problems:

function shouldRefreshCache(cacheAge, ttl) {
  const ageRatio = cacheAge / ttl;
  const refreshProbability = Math.min(ageRatio * 0.1, 0.8);
  
  return Math.random() < refreshProbability;
}

Performance Monitoring

Track these metrics to optimize your caching strategy:

Cache Hit Ratio

const cacheStats = {
  hits: 0,
  misses: 0,
  
  recordHit() { this.hits++; },
  recordMiss() { this.misses++; },
  
  getHitRatio() {
    return this.hits / (this.hits + this.misses);
  }
};

Aim for 70-90% hit ratio depending on your use case.

API Cost Savings

function calculateSavings(originalCalls, cachedCalls, costPerCall) {
  const callsSaved = originalCalls - cachedCalls;
  const costSaved = callsSaved * costPerCall;
  const reductionPercent = (callsSaved / originalCalls) * 100;
  
  return {
    callsSaved,
    costSaved,
    reductionPercent
  };
}

Best Practices and Common Pitfalls

Do’s:

• Start with longer TTLs and adjust down based on data staleness tolerance
• Cache at the right granularity – not too broad, not too specific
• Monitor cache performance with hit ratios and cost metrics
• Plan for cache failures with graceful fallbacks to API calls

Don’ts:

• Don’t cache user-specific sensitive data without proper security
• Don’t set infinite TTLs – even stable data changes eventually
• Don’t cache error responses (or cache them very briefly)
• Don’t forget to handle cache corruption scenarios

Common Pitfall: Over-Caching

I’ve seen workflows cache everything, including rapidly changing data like stock prices or real-time inventory. This created more problems than it solved.

Rule of thumb: If data changes more than once per hour, consider conditional caching instead of time-based caching.

Advanced Techniques

Cache Warming

Pre-populate caches with frequently needed data:

// Daily cache warming for top companies
async function warmCompanyCache() {
  const topCompanies = await getTopCompanies(100);
  
  for (const company of topCompanies) {
    if (!cache.has(`company_${company.id}`)) {
      const data = await enrichCompany(company.domain);
      cache.set(`company_${company.id}`, data, 86400); // 24h TTL
    }
  }
}

Intelligent Prefetching

Predict and cache data before it’s needed:

// When enriching a contact, prefetch common related data
async function enrichContact(email) {
  const contact = await getContactData(email);
  
  // Prefetch likely-needed company data
  if (contact.company && !cache.has(`company_${contact.company}`)) {
    setImmediate(() => {
      getCompanyData(contact.company);
    });
  }
  
  return contact;
}

FAQ

Q: How do I determine the right TTL for my cached data?

A: Start by categorizing your data by change frequency:

• Static data (company industry): 24+ hours
• Semi-static (contact info): 4-12 hours
• Dynamic (lead scores): 30 minutes - 2 hours
• Volatile (inventory, pricing): 5-15 minutes

Monitor your workflows and adjust based on how often you encounter stale data issues.

Q: What’s the best storage option for caching in no-code tools?

A: Depends on your needs:

• Google Sheets: Simple, visual, good for small datasets
• Airtable: Better for structured data with relationships
• Redis/External DB: Best performance for large-scale operations
• Built-in storage: Use platform-native options when available

Q: How do I handle cache corruption or bad data?

A: Implement cache versioning and validation:

const cacheEntry = {
  version: "1.0",
  data: actualData,
  checksum: generateChecksum(actualData),
  timestamp: Date.now()
};

function validateCache(entry) {
  return entry.checksum === generateChecksum(entry.data);
}

Q: Should I cache API error responses?

A: Cache temporary errors (rate limits, timeouts) for 5-15 minutes to avoid retry storms. Don’t cache permanent errors (404, authentication failures) as these need immediate attention.

Q: How do I prevent cache stampedes when TTL expires?

A: Use probabilistic early expiration:

function shouldRefresh(cacheAge, ttl) {
  if (cacheAge < ttl * 0.8) return false;
  
  const probability = (cacheAge - ttl * 0.8) / (ttl * 0.2);
  return Math.random() < probability;
}

This spreads cache refreshes over time instead of all at once.

Q: What’s the ROI of implementing caching?

A: Most clients see:

• 60-80% reduction in API costs
• 40-60% improvement in workflow speed
• 90%+ reduction in rate limit issues
• Implementation pays for itself within 2-4 weeks

The exact ROI depends on your API usage volume and current inefficiencies.