Deploy OpenClaw Rating API Token‑Bucket on AWS Lambda@Edge and GCP Cloud Functions

You can deploy the OpenClaw Rating API token‑bucket on both AWS Lambda@Edge and Google Cloud Functions, run a K6 load‑test to validate performance, and compare the monthly cost of each option in a few straightforward steps.

1. Introduction

Rate limiting is a cornerstone of reliable API design. The OpenClaw Rating API token‑bucket offers a flexible, algorithmic approach to throttling requests, but many teams wonder where to host it for the best latency‑to‑cost ratio. This guide walks you through a complete, production‑ready deployment on AWS Lambda@Edge and GCP Cloud Functions, shows you how to benchmark with k6, and provides a side‑by‑side cost analysis so you can choose the right platform for your traffic patterns.

All steps assume you have a UBOS account and access to the OpenClaw hosting page where the source repository is available.

2. Prerequisites

• UBOS account – needed to download the token‑bucket source and to use the UBOS platform overview for CI/CD pipelines.
• AWS credentials – an IAM user with lambda:CreateFunction, cloudfront:UpdateDistribution, and iam:PassRole permissions.
• GCP credentials – a service account with cloudfunctions.functions.create and cloudfunctions.functions.invoke roles.
• OpenClaw Rating API token‑bucket source – clone from the UBOS repository (see the OpenClaw hosting page).
• Node.js 18+ or Python 3.10+ – the runtime you prefer for the Lambda/Function code.

3. Deploying to AWS Lambda@Edge

3.1 Create the Lambda function

1. Open the AWS Console → Lambda → Create function.
2. Choose Author from scratch, give it a name like openclaw-token-bucket, and select the runtime that matches your code (Node.js 18 or Python 3.10).
3. Attach an execution role that includes AWSLambdaBasicExecutionRole and CloudFrontFullAccess.
4. In the Function code section, upload a ZIP containing:

   // index.js (Node example)
   const { TokenBucket } = require('./tokenBucket');
   
   exports.handler = async (event) => {
     const request = event.Records[0].cf.request;
     const bucket = new TokenBucket({ capacity: 100, refillRate: 10 });
     if (!bucket.consume(1)) {
       return {
         status: '429',
         statusDescription: 'Too Many Requests',
       };
     }
     return request;
   };

5. Save the function.

3.2 Configure CloudFront distribution

• Create a new CloudFront distribution (or edit an existing one) that points to your API origin.
• Under Behaviors, edit the default behavior and set Lambda Function Associations → Viewer Request → select the function you just created.
• Deploy the changes – CloudFront will replicate the Lambda@Edge function to edge locations worldwide.

3.3 Attach token‑bucket logic

The code snippet above already embeds the token‑bucket algorithm. Adjust capacity and refillRate to match your SLA. For more advanced scenarios (e.g., per‑IP limits), extend the TokenBucket class to store counters in DynamoDB.

3.4 Testing the deployment

# Simple curl test
curl -I https://d111111abcdef8.cloudfront.net/your-api-endpoint
# Expect 200 until limit is hit, then 429

Use the Workflow automation studio to schedule automated health checks that verify the 429 response after the bucket is exhausted.

4. Deploying to GCP Cloud Functions

4.1 Create Cloud Function

1. Open the Google Cloud Console → Cloud Functions → Create function.
2. Set the name openclaw-token-bucket, choose the same runtime as the AWS version.
3. Under Trigger, select HTTP and allow unauthenticated invocations (or secure with IAM as needed).
4. Paste the same source code (adjusted for GCP’s request object):

   // index.js (Node example for GCP)
   const { TokenBucket } = require('./tokenBucket');
   const bucket = new TokenBucket({ capacity: 200, refillRate: 20 });
   
   exports.openclawTokenBucket = (req, res) => {
     if (!bucket.consume(1)) {
       res.status(429).send('Too Many Requests');
       return;
     }
     res.status(200).send('OK');
   };

5. Deploy the function.

4.2 Set up HTTP trigger

After deployment, GCP provides a public URL (e.g., https://REGION-project.cloudfunctions.net/openclaw-token-bucket). Use this URL as the endpoint for your API gateway or directly from your client applications.

4.3 Integrate token‑bucket code

If you need per‑user limits, store the bucket state in Firestore or Memorystore. The same TokenBucket class can be reused with a simple wrapper.

4.4 Testing the deployment

# Curl test against GCP function
curl -I https://REGION-project.cloudfunctions.net/openclaw-token-bucket
# 200 until limit, then 429

5. K6 Load‑Test Configuration

5.1 k6 script example

import http from 'k6/http';
import { check, sleep } from 'k6';
import { Rate } from 'k6/metrics';

export let errorRate = new Rate('errors');

export const options = {
  stages: [
    { duration: '2m', target: 50 },   // ramp‑up to 50 VUs
    { duration: '5m', target: 50 },   // stay at 50 VUs
    { duration: '2m', target: 0 },    // ramp‑down
  ],
  thresholds: {
    errors: ['rate<0.01'], //  r.status === 200 || r.status === 429,
  });
  errorRate.add(!success);
  sleep(0.5);
}

5.2 Running the benchmark

Replace YOUR_ENDPOINT_HERE with the CloudFront URL for AWS or the Cloud Function URL for GCP. Execute:

k6 run token_bucket_test.js

The output will show request rates, response distribution, and the errors metric.

5.3 Interpreting results

• Success ratio – a high proportion of 200 responses indicates the bucket capacity is sufficient for the tested load.
• 429 spikes – if 429 appears earlier than expected, lower the capacity or increase refillRate.
• Latency – both AWS and GCP add minimal overhead (< 30 ms) when the function runs at the edge.

6. Cost‑Analysis Insights

6.1 AWS Lambda@Edge pricing model

AWS charges for request count and compute duration (GB‑seconds) at the edge location. As of 2024:

• First 1 M requests per month are free.
• $0.60 per 1 M additional requests.
• Compute: $0.00001667 per GB‑second.

6.2 GCP Cloud Functions pricing model

GCP bills for invocations, compute time, and outbound data:

• First 2 M invocations per month are free.
• $0.40 per 1 M additional invocations.
• Compute: $0.0000025 per GHz‑second (rounded to 128 MB increments).
• Network egress: $0.12 per GB (first 1 GB free).

6.3 Comparative cost table

6.4 Recommendations based on traffic patterns

• Low‑to‑moderate traffic (< 200 k req/mo) – GCP Cloud Functions is marginally cheaper and simpler to set up.
• Global latency‑critical workloads – Lambda@Edge places the function at the CDN edge, shaving ~10‑15 ms off round‑trip time, which can be decisive for real‑time rate limiting.
• Burst‑heavy traffic – Both platforms auto‑scale, but Lambda@Edge benefits from CloudFront’s built‑in caching; consider a hybrid approach where static responses are cached and only rate‑limit checks hit the function.

7. Conclusion and Next Steps

Deploying the OpenClaw Rating API token‑bucket on either AWS Lambda@Edge or GCP Cloud Functions is a matter of a few clicks, a short code upload, and a quick k6 validation. The cost analysis shows that both services are affordable for SaaS‑scale traffic, with GCP offering a slightly lower price point and AWS delivering edge‑level latency.

Ready to put the token‑bucket into production? Grab the ready‑made UBOS template from the UBOS templates for quick start, customize the capacity parameters, and push the code through the Web app editor on UBOS. If you need help with monitoring or scaling, explore the Enterprise AI platform by UBOS for advanced observability.

Give it a try today and share your results in the comments – we love hearing how developers optimize API rate limiting in the cloud!