Rate Limits

The DRIP API implements rate limiting to ensure fair usage and maintain system performance for all users. This guide explains how rate limits work and how to handle them in your applications.

Rate Limit Overview

DRIP uses a sliding window rate limiting system that tracks requests over time periods. Different endpoints may have different limits based on their resource intensity.

Current Limits

Free

Endpoint	Requests per Minute	Requests per Month
Points	24	100,000

Pro

Endpoint	Requests per Minute	Requests per Month
Points	300	1,000,000
Quests	120	100,000
Twitter data	600	25,000

Enterprise

Endpoint	Requests per Minute	Requests per Month
Points	500	5,000,000
Quests	200	1,000,000
Twitter data	1,200	250,000

Rate limits are applied per Realm, not per API key.

Rate Limit Headers

Every API response includes headers that show your current rate limit status:

X-RateLimit-Limit: 100
X-RateLimit-Remaining: 87
X-RateLimit-Reset: 1642694400
X-RateLimit-Window: 60

Header Descriptions

Header	Description
`X-RateLimit-Limit`	Maximum requests allowed in the current window
`X-RateLimit-Remaining`	Requests remaining in the current window
`X-RateLimit-Reset`	Unix timestamp when the window resets
`X-RateLimit-Window`	Window duration in seconds

Handling Rate Limits

429 Too Many Requests

When you exceed the rate limit, the API returns a 429 Too Many Requests status with a Retry-After header:

{
  "error": "Rate limit exceeded",
  "message": "Too many requests. Try again in 30 seconds.",
  "retryAfter": 30
}

Implementation Examples

class DripClient {
  constructor(apiKey) {
    this.apiKey = apiKey;
    this.baseUrl = 'https://api.drip.re/api/v1';
  }

  async request(method, endpoint, data = null, retries = 3) {
    const url = `${this.baseUrl}${endpoint}`;
    
    for (let attempt = 0; attempt <= retries; attempt++) {
      try {
        const response = await fetch(url, {
          method,
          headers: {
            'Authorization': `Bearer ${this.apiKey}`,
            'Content-Type': 'application/json'
          },
          body: data ? JSON.stringify(data) : null
        });

        // Log rate limit info
        console.log(`Rate limit: ${response.headers.get('X-RateLimit-Remaining')}/${response.headers.get('X-RateLimit-Limit')}`);

        if (response.status === 429) {
          const retryAfter = parseInt(response.headers.get('Retry-After') || '60');
          
          if (attempt < retries) {
            console.log(`Rate limited. Retrying in ${retryAfter} seconds...`);
            await this.sleep(retryAfter * 1000);
            continue;
          }
          
          throw new Error(`Rate limit exceeded after ${retries} retries`);
        }

        if (!response.ok) {
          throw new Error(`HTTP ${response.status}: ${response.statusText}`);
        }

        return response.json();
      } catch (error) {
        if (attempt === retries) throw error;
        
        // Exponential backoff for other errors
        const delay = Math.pow(2, attempt) * 1000;
        await this.sleep(delay);
      }
    }
  }

  sleep(ms) {
    return new Promise(resolve => setTimeout(resolve, ms));
  }
}

Best Practices

1. Monitor Rate Limit Headers

Always check the rate limit headers in your responses to avoid hitting limits:

function checkRateLimit(response) {
  const remaining = parseInt(response.headers.get('X-RateLimit-Remaining'));
  const limit = parseInt(response.headers.get('X-RateLimit-Limit'));
  
  if (remaining < limit * 0.1) { // Less than 10% remaining
    console.warn('Approaching rate limit. Consider slowing down requests.');
  }
}

2. Implement Exponential Backoff

Use exponential backoff for retries to avoid thundering herd problems:

async function exponentialBackoff(attempt, maxDelay = 60000) {
  const delay = Math.min(Math.pow(2, attempt) * 1000, maxDelay);
  const jitter = Math.random() * 0.1 * delay; // Add 10% jitter
  
  await new Promise(resolve => setTimeout(resolve, delay + jitter));
}

3. Batch Operations

Use batch endpoints when available to reduce API calls:

// Instead of multiple individual updates
for (const member of members) {
  await updateMemberBalance(realmId, member.id, 10); // 100 API calls
}

// Use batch update
await batchUpdateBalances(realmId, members.map(m => ({
  memberId: m.id,
  tokens: 10
}))); // 1 API call

4. Cache Frequently Accessed Data

Implement caching to reduce redundant API calls:

class CachedDripClient extends DripClient {
  constructor(apiKey, cacheTtl = 300000) { // 5 minutes
    super(apiKey);
    this.cache = new Map();
    this.cacheTtl = cacheTtl;
  }

  async getRealm(realmId) {
    const cacheKey = `realm:${realmId}`;
    const cached = this.cache.get(cacheKey);
    
    if (cached && Date.now() - cached.timestamp < this.cacheTtl) {
      return cached.data;
    }

    const realm = await this.request('GET', `/realms/${realmId}`);
    this.cache.set(cacheKey, {
      data: realm,
      timestamp: Date.now()
    });

    return realm;
  }
}

Endpoint-Specific Limits

Some endpoints have additional restrictions:

Batch Operations

Batch member updates: Maximum 100 members per request
Member search: Maximum 50 values per search

File Operations

Asset uploads: 10 MB maximum file size
Bulk imports: Maximum 1,000 records per import

Webhooks

Webhook calls: Maximum 5 retries per event
Webhook timeout: 30 seconds maximum response time

Monitoring and Alerts

Track Your Usage

Implement usage tracking to monitor your API consumption:

class UsageTracker {
  constructor() {
    this.requests = [];
  }

  recordRequest(endpoint, status, remaining) {
    this.requests.push({
      endpoint,
      status,
      remaining,
      timestamp: Date.now()
    });

    // Keep only last hour of data
    const oneHourAgo = Date.now() - 3600000;
    this.requests = this.requests.filter(r => r.timestamp > oneHourAgo);
  }

  getUsageStats() {
    const totalRequests = this.requests.length;
    const errorRequests = this.requests.filter(r => r.status >= 400).length;
    const rateLimitedRequests = this.requests.filter(r => r.status === 429).length;

    return {
      totalRequests,
      errorRate: errorRequests / totalRequests,
      rateLimitRate: rateLimitedRequests / totalRequests
    };
  }
}

Set Up Alerts

Create alerts for approaching rate limits:

function checkForAlerts(usageStats, rateLimitRemaining, rateLimitLimit) {
  const usagePercentage = (rateLimitLimit - rateLimitRemaining) / rateLimitLimit;
  
  if (usagePercentage > 0.8) {
    console.warn('WARNING: Using 80% of rate limit');
    // Send alert to monitoring system
  }
  
  if (usageStats.rateLimitRate > 0.05) {
    console.error('ERROR: High rate limit rejection rate');
    // Send alert to monitoring system
  }
}

Upgrading Limits

If you consistently hit rate limits, consider upgrading your plan:

Analyze Usage

Review your API usage patterns and identify peak times

Optimize Code

Implement caching, batching, and efficient request patterns

Upgrade Plan

Contact support to discuss higher rate limits for your use case

Rate Limit Errors

Common Error Scenarios

Burst Limit Exceeded

Cause: Too many requests in a very short time periodSolution: Implement request queuing and spacing between calls

class RequestQueue {
  constructor(maxConcurrent = 5, delayMs = 100) {
    this.queue = [];
    this.running = 0;
    this.maxConcurrent = maxConcurrent;
    this.delayMs = delayMs;
  }

  async add(requestFn) {
    return new Promise((resolve, reject) => {
      this.queue.push({ requestFn, resolve, reject });
      this.process();
    });
  }

  async process() {
    if (this.running >= this.maxConcurrent || this.queue.length === 0) {
      return;
    }

    this.running++;
    const { requestFn, resolve, reject } = this.queue.shift();

    try {
      const result = await requestFn();
      resolve(result);
    } catch (error) {
      reject(error);
    } finally {
      this.running--;
      setTimeout(() => this.process(), this.delayMs);
    }
  }
}

Sustained High Usage

Inefficient Polling

Testing Rate Limits

Development Environment

Test your rate limit handling in development:

async function testRateLimit() {
  const client = new DripClient(process.env.DRIP_API_KEY);
  
  // Make rapid requests to trigger rate limiting
  const promises = [];
  for (let i = 0; i < 150; i++) {
    promises.push(client.getRealm('YOUR_REALM_ID'));
  }
  
  try {
    await Promise.all(promises);
  } catch (error) {
    console.log('Rate limit handling test:', error.message);
  }
}

Only test rate limits in development environments with test API keys to avoid impacting production systems.

Summary

Effective rate limit handling involves:

Monitor rate limit headers in all responses
Implement exponential backoff and retry logic
Use batch operations and caching to reduce calls
Track usage patterns and set up alerts
Optimize your integration for efficiency

Following these practices will ensure your integration remains reliable and performs well within DRIP’s rate limits.

Overview

Developer Guide

Apps

Drip Academy

FAQ

Links

Rate Limits

Rate Limit Overview

Current Limits

Free

Pro

Enterprise

Rate Limit Headers

Header Descriptions

Handling Rate Limits

429 Too Many Requests

Implementation Examples

Best Practices

1. Monitor Rate Limit Headers

2. Implement Exponential Backoff

3. Batch Operations

4. Cache Frequently Accessed Data

Endpoint-Specific Limits

Batch Operations

File Operations

Webhooks

Monitoring and Alerts

Track Your Usage

Set Up Alerts

Upgrading Limits

Analyze Usage

Optimize Code

Upgrade Plan

Rate Limit Errors

Common Error Scenarios

Testing Rate Limits

Development Environment

Summary

Overview

Developer Guide

Apps

Drip Academy

FAQ

Links

​Rate Limit Overview

​Current Limits

​Free

​Pro

​Enterprise

​Rate Limit Headers

​Header Descriptions

​Handling Rate Limits

​429 Too Many Requests

​Implementation Examples

​Best Practices

​1. Monitor Rate Limit Headers

​2. Implement Exponential Backoff

​3. Batch Operations

​4. Cache Frequently Accessed Data

​Endpoint-Specific Limits

​Batch Operations

​File Operations

​Webhooks

​Monitoring and Alerts

​Track Your Usage

​Set Up Alerts

​Upgrading Limits

Analyze Usage

Optimize Code

Upgrade Plan

​Rate Limit Errors

​Common Error Scenarios

​Testing Rate Limits

​Development Environment

​Summary

Rate Limit Overview

Current Limits

Free

Pro

Enterprise

Rate Limit Headers

Header Descriptions

Handling Rate Limits

429 Too Many Requests

Implementation Examples

Best Practices

1. Monitor Rate Limit Headers

2. Implement Exponential Backoff

3. Batch Operations

4. Cache Frequently Accessed Data

Endpoint-Specific Limits

Batch Operations

File Operations

Webhooks

Monitoring and Alerts

Track Your Usage

Set Up Alerts

Upgrading Limits

Rate Limit Errors

Common Error Scenarios

Testing Rate Limits

Development Environment

Summary