Amazon Bedrock Error - InsufficientThroughputException: Model Processing Capacity Reached

Amazon Bedrock Error - InsufficientThroughputException: Model Processing Capacity Reached

Problem:

When calling InvokeModel in Amazon Bedrock, you may encounter the following error:

$ aws bedrock-runtime invoke-model \
    --model-id anthropic.claude-v2 \
    --body '{"prompt": "Hello, world!"}' \
    --region us-east-1

# Error:
# An error occurred (InsufficientThroughputException) when calling the 
# InvokeModel operation: Model processing capacity reached.

Issue:

This error occurs when Amazon Bedrock does not have enough available capacity to process your request at that moment.

Common reasons include:

• Exceeded Throughput Quota – AWS imposes request-per-second limits on model invocations.
• AWS System Load – If AWS infrastructure is under high demand, requests may be throttled.
• Burst Traffic Spikes – A sudden increase in API requests can trigger this error.
• Low Provisioned Throughput – Your AWS account may have a low quota for Bedrock model invocations.
• Shared Model Resources – Foundation models in AWS Bedrock are shared across multiple users, meaning availability fluctuates.

If left unaddressed, this issue can lead to API failures, slow performance, and disruptions in AI-driven applications.

Fix: Verify Quotas, Request an Increase, Implement Retries, and Optimize Requests

# Step 1: Check AWS Service Quotas
aws service-quotas list-service-quotas \
    --service-code bedrock \
    --region us-east-1

# Expected Output (Abbreviated):
# {
#   "Quotas": [
#     {
#       "QuotaName": "ModelInvocationThroughput",
#       "QuotaCode": "L-BEDROCK-MODEL-THROUGHPUT",
#       "Value": 10
#     }
#   ]
# }

# If "Value" is too low, proceed to request a quota increase.

# Step 2: Request a Quota Increase
aws service-quotas request-service-quota-increase \
    --service-code bedrock \
    --quota-code L-BEDROCK-MODEL-THROUGHPUT \
    --desired-value 50

# Expected Output:
# {
#   "RequestedQuota": {
#     "QuotaCode": "L-BEDROCK-MODEL-THROUGHPUT",
#     "DesiredValue": 50,
#     "Status": "PENDING"
#   }
# }
# If request is denied, open an AWS Support ticket.

# Step 3: Use Exponential Backoff for Retrying API Calls
# Python script to retry requests with increasing wait times

import time
import random
import boto3

client = boto3.client('bedrock-runtime', region_name='us-east-1')

def invoke_model(payload, attempt=1):
    """Attempts to invoke the model with exponential backoff on failure."""
    try:
        response = client.invoke_model(
            modelId="anthropic.claude-v2",
            body=payload
        )
        print(response['body'].read().decode('utf-8'))
    except Exception as e:
        delay = min(2 ** attempt + random.uniform(0, 1), 60)  # Increase wait time per retry
        print(f"Error: {e} | Retrying in {delay:.2f} seconds...")
        time.sleep(delay)
        if attempt < 5:  # Limit retries to prevent infinite loops
            invoke_model(payload, attempt + 1)
        else:
            print("Max retries reached. Consider reducing request volume or switching models.")

# Example Invocation
invoke_model('{"prompt": "Hello, world!"}')

# Step 4: Reduce API Request Frequency with Batch Processing
from concurrent.futures import ThreadPoolExecutor

def process_batch(requests):
    """Processes model requests in batches to reduce API request frequency."""
    with ThreadPoolExecutor(max_workers=5) as executor:
        results = list(executor.map(invoke_model, requests))
    return results

# Example Batch Processing Usage
requests = ['{"prompt": "Hello"}', '{"prompt": "World"}']
process_batch(requests)

# Step 5: Try a Different AWS Region
aws ec2 describe-regions --query "Regions[*].RegionName"

# Expected Output:
# [
#   "us-east-1",
#   "us-west-2",
#   "eu-west-1"
# ]
# If one region is overloaded, try invoking the model in another region.

aws bedrock-runtime invoke-model \
    --model-id anthropic.claude-v2 \
    --body '{"prompt": "Hello, world!"}' \
    --region us-west-2

# Step 6: Monitor CloudWatch Metrics for Performance Insights
aws cloudwatch get-metric-statistics \
    --namespace "AWS/Bedrock" \
    --metric-name "ModelInvocationThroughput" \
    --start-time $(date -u -d '5 minutes ago' +%Y-%m-%dT%H:%M:%SZ) \
    --end-time $(date -u +%Y-%m-%dT%H:%M:%SZ) \
    --period 60 \
    --statistics Maximum \
    --region us-east-1

# Expected Output:
# {
#   "Datapoints": [
#     {
#       "Timestamp": "2025-02-27T12:00:00Z",
#       "Maximum": 8.5
#     }
#   ]
# }
# If throughput is maxed out, AWS is likely throttling your requests.

Final Thoughts

If you're encountering InsufficientThroughputException errors, follow these steps:

• Check AWS Service Quotas to verify your request-per-second limits.
• Request a Quota Increase to support higher request volumes.
• Use Exponential Backoff to retry requests gradually and reduce API congestion.
• Reduce API Request Frequency using batch processing or rate limiting.
• Try a Different AWS Region to find available processing capacity.
• Monitor CloudWatch Metrics to detect performance bottlenecks in real time.

Need AWS Expertise?

If you're looking for guidance on Amazon Bedrock or any cloud challenges, feel free to reach out! We'd love to help you tackle your Bedrock projects. 🚀

Email us at: info@pacificw.com

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