What are RCS Rate Limits and How Do I Handle Them?

RCS Rate Limits: How to Handle Them

Every RCS API has rate limits. Here's how to work within them.

Typical Rate Limits by Provider

Entry tier: 100 messages/second, 10K messages/minute Mid tier: 500 messages/second, 50K messages/minute Enterprise: 1,000+ messages/second, 100K+ messages/minute

Daily caps: Vary by plan (1M-100M+ messages/day)

Rate Limit Headers

Most APIs return headers indicating limits:

X-RateLimit-Limit: 1000
X-RateLimit-Remaining: 950
X-RateLimit-Reset: 1640995200

Monitor these to track usage and avoid hitting limits.

Handling Rate Limits

Strategy 1: Exponential Backoff

def send_with_retry(message, max_attempts=5):
    for attempt in range(max_attempts):
        try:
            return api.send(message)
        except RateLimitError as e:
            wait = 2 ** attempt  # 1s, 2s, 4s, 8s, 16s
            time.sleep(wait)
    raise Exception("Rate limit exceeded")

Strategy 2: Message Queuing

Use a queue (Redis, RabbitMQ, SQS) to buffer messages:

• Producer adds messages to queue
• Worker processes queue at controlled rate
• Prevents overwhelming API

Strategy 3: Batch Processing

Send messages in batches rather than individually:

• Group 100-1,000 messages per batch
• Use bulk send API endpoints
• More efficient than individual sends

Strategy 4: Request Limit Increases

As you scale:

• Contact provider to request higher limits
• Justify with growth projections
• Most providers accommodate legitimate growth

Designing for Rate Limits

When sending bulk campaigns:

1. Estimate total volume needed
2. Check current rate limits
3. Calculate time required (volume / rate)
4. Use queue with appropriate worker count
5. Monitor and adjust as needed

Example:

• Need to send 100,000 messages
• Rate limit: 1,000/second
• Time required: 100 seconds (~2 minutes)
• Use 10 workers × 100 msg/sec each = 1,000 msg/sec total

Common Rate Limit Issues

Burst sending: Trying to send all at once → Throttling Solution: Use queue with consistent rate

Not monitoring limits: Hit limit unexpectedly → Messages fail Solution: Track rate limit headers, alert at 80% usage

Insufficient retry logic: Transient errors fail permanently Solution: Exponential backoff with jitter

Ignoring daily caps: Exceed plan limit → Overage charges Solution: Track daily usage, throttle before hitting cap

Best Practices

• Implement exponential backoff for all 429 errors
• Use message queuing for bulk sends
• Monitor rate limit headers in every API call
• Set up alerts at 80% of rate limits
• Request limit increases proactively
• Have fallback strategy if rate limits hit

The Bottom Line

Rate limits are real and will affect your campaigns. Plan for them upfront with queuing, exponential backoff, and monitoring.

Most providers accommodate growth with limit increases. Don't let rate limits be a surprise — design your system to handle them gracefully.