Batch email send via API fails with 413 Payload Too Large error in marketing automation

CRM HubSpot
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1

We’re hitting 413 Payload Too Large errors when sending batch marketing emails via the HubSpot Email API. Our campaigns typically include 5,000-8,000 contacts with personalized content tokens (first name, company, custom fields). The payload size seems to be the issue, especially when we include multiple personalization tokens per email.

Current approach:


POST /marketing/v3/emails/batch
Content-Type: application/json
{
  "emailId": "12345678",
  "contacts": [5000+ contact objects with tokens]
}

The error occurs inconsistently - sometimes 5K contacts work, sometimes 3K fail. We’ve noticed campaigns with more personalization tokens fail more frequently. Is there a documented payload size limit? How should we handle batch processing for large campaigns while maintaining personalization quality?

2

Let me address all three focus areas comprehensively:

Payload Size Limits: HubSpot’s Email API has an undocumented soft limit around 10-15MB per request. The variability you’re experiencing comes from the dynamic nature of personalization data. Each contact object with tokens contributes differently based on field lengths.

Batch Processing Strategy: Implement adaptive batching with these guidelines:


// Calculate approximate payload size
contactSize = baseSize + (tokenCount * avgTokenLength)
maxContacts = targetPayloadSize / contactSize
batchSize = min(maxContacts, 2000)

Use 1,000-2,000 contacts per batch as a safe baseline. For campaigns with extensive personalization (5+ tokens), reduce to 500-1,000. Implement parallel processing across multiple batches to maintain throughput - HubSpot’s rate limits (typically 100 requests per 10 seconds for email endpoints) allow concurrent batch sends.

Personalization Impact Mitigation: The key is to minimize redundant data transmission. Instead of including full personalization values in each contact object, leverage contact property references:


{
  "emailId": "12345678",
  "contacts": [
    {"vid": 12345},
    {"vid": 12346}
  ],
  "personalizationTokens": ["firstname", "company"]
}

This approach references existing contact properties rather than transmitting values, reducing payload by 70-80%. HubSpot resolves tokens server-side during send.

Additional optimizations:

  1. Enable gzip compression (Content-Encoding: gzip header)
  2. Remove unnecessary fields from contact objects
  3. Implement retry logic with exponential backoff
  4. Monitor payload sizes and adjust batching dynamically
  5. Consider pre-segmenting contacts by personalization complexity

For your 5,000-8,000 contact campaigns, split into 4-8 batches of 1,000-1,500 contacts each. Process batches in parallel (respecting rate limits) to maintain acceptable send times. This approach eliminates 413 errors while preserving full personalization capabilities and maintaining campaign performance.

3

Quick addition to the batching approaches mentioned - implement exponential backoff with retry logic. When you hit a 413, automatically reduce batch size by 50% and retry. Track successful batch sizes over time to establish optimal batch size for your typical personalization patterns. We built this into our integration and it self-adjusts based on campaign complexity without manual intervention.

4

The inconsistency you’re experiencing points to variable payload sizes based on personalization data length. A contact with a 50-character company name versus a 5-character one creates different payload sizes. When you multiply that across thousands of contacts with multiple tokens, you hit the threshold unpredictably. Batch size reduction is correct, but you also need to monitor payload size dynamically. Consider implementing a payload estimator that calculates approximate size before sending and adjusts batch size accordingly. Also, check if you’re including unnecessary fields in the contact objects - only send what’s required for personalization.

5

One more consideration: compression. If you’re sending large payloads, enable gzip compression on your HTTP requests. Most HTTP clients support this natively. It can reduce payload size by 60-70% for text-heavy data like contact information and personalization tokens, which gives you significant headroom below the 413 threshold.

6

Have you considered pre-processing the personalization tokens? Instead of sending raw token data in each contact object, you could use HubSpot’s contact properties that are already stored in the system. Reference the contact by ID only and let HubSpot handle token replacement server-side. This dramatically reduces payload size since you’re not transmitting the actual personalization data - just references. Your payload becomes much smaller and more predictable.

7

We solved this by implementing a two-tier batching strategy. First tier: segment contacts by personalization complexity (number of tokens and average field length). Second tier: use smaller batches (500-1000) for high-complexity segments and larger batches (2000-3000) for simple ones. This way you maximize throughput while avoiding 413 errors. The API rate limits are generous enough that smaller batches don’t significantly impact overall send time.