Carlos

• Updated: March 19, 2026
• 7 min read

Implementing a CRDT‑based Token‑Bucket Rate Limiter with Multi‑Tenant Billing for OpenClaw Rating API Edge

Answer: The CRDT‑based token‑bucket rate limiter can be tightly coupled with a multi‑tenant billing and quota framework on the OpenClaw Rating API Edge by leveraging conflict‑free replicated data types for distributed state, embedding the limiter in OpenClaw’s request pipeline, and driving quota enforcement and billing through a unified usage store.

Introduction

API providers that expose powerful models—such as OpenClaw’s hosted OpenClaw instance—must protect their back‑ends from overload while accurately charging each tenant. Traditional centralized rate limiters struggle with latency and single‑point‑of‑failure issues in a globally distributed environment. A CRDT‑based token‑bucket solves these problems by allowing every edge node to maintain a consistent view of the token count without coordination overhead.

In this guide we walk through the theory, the concrete implementation on OpenClaw, and the integration with a multi‑tenant billing and quota framework. The patterns described are reusable across any SaaS API built on the UBOS platform overview, and they align with the latest best practices for API scaling.

Overview of CRDT‑based token‑bucket rate limiter

What is CRDT?

Conflict‑Free Replicated Data Types (CRDTs) are data structures that guarantee eventual consistency across distributed replicas without requiring a central coordinator. Each replica can apply updates locally; when replicas synchronize, the merge operation is deterministic and conflict‑free. Common CRDT families include G‑Counters, PN‑Counters, and Grow‑Only Sets. For rate limiting we use a PN‑Counter to track token consumption and replenishment.

How token bucket works

The classic token‑bucket algorithm maintains a bucket of tokens. Each incoming request consumes one token; tokens are refilled at a fixed rate (e.g., 1000 tokens per minute). If the bucket is empty, the request is throttled. The algorithm is simple, yet it provides burst‑capacity while enforcing a long‑term average rate.

Benefits of CRDT approach

• Zero‑latency local decisions: Edge nodes can approve or reject a request instantly, using their local replica of the token counter.
• High availability: No single point of failure; if one node goes down, others continue to enforce limits.
• Strong eventual consistency: All replicas converge to the same token count, preventing over‑issuance.
• Scalable billing integration: Token consumption events can be streamed to a usage store for real‑time invoicing.

Implementing the rate limiter in OpenClaw

Architecture diagram

Below is a textual representation of the architecture; replace it with a visual diagram in your documentation.

+-------------------+        +-------------------+        +-------------------+
|  Edge Node A      |   |  CRDT Sync Service|   |  Edge Node B      |
| (Rate Limiter)   |        | (Gossip / PubSub) |        | (Rate Limiter)   |
+-------------------+        +-------------------+        +-------------------+
        |                               |                               |
        |                               |                               |
        v                               v                               v
   Request Flow                  Token Replication               Request Flow
  (OpenClaw API)                (PN‑Counter)                    (OpenClaw API)
  

The Workflow automation studio can be used to orchestrate the sync service, exposing a simple HTTP endpoint that each edge node calls every few seconds.

Code snippets

Below is a minimal Node.js implementation that can be dropped into an OpenClaw plugin. It uses the crdt-pn-counter library (hypothetical) and the UBOS Web app editor on UBOS for rapid iteration.

// tokenBucket.js – CRDT‑based token bucket
import { PNCounter } from 'crdt-pn-counter';
import { syncWithPeers } from './crdtSync';

// Configuration (tokens per minute, burst capacity)
const RATE = 1000;          // refill rate
const CAPACITY = 2000;      // max tokens in bucket
const REFILL_INTERVAL = 60 * 1000; // 1 minute in ms

// Each tenant gets its own counter identified by tenantId
const buckets = new Map(); // tenantId → { counter, lastRefill }

// Initialise bucket for a tenant
function initBucket(tenantId) {
  if (!buckets.has(tenantId)) {
    const counter = new PNCounter(); // starts at 0
    buckets.set(tenantId, { counter, lastRefill: Date.now() });
  }
}

// Refill logic – called before each request
function refill(tenantId) {
  const bucket = buckets.get(tenantId);
  const now = Date.now();
  const elapsed = now - bucket.lastRefill;
  const tokensToAdd = Math.floor((elapsed / REFILL_INTERVAL) * RATE);
  if (tokensToAdd > 0) {
    bucket.counter.increment(tokensToAdd);
    bucket.lastRefill = now;
    // Clamp to capacity
    if (bucket.counter.value() > CAPACITY) {
      bucket.counter.decrement(bucket.counter.value() - CAPACITY);
    }
  }
}

// Middleware for OpenClaw
export async function rateLimiter(req, res, next) {
  const tenantId = req.headers['x-tenant-id'];
  if (!tenantId) return res.status(400).send('Missing tenant identifier');

  initBucket(tenantId);
  refill(tenantId);

  const bucket = buckets.get(tenantId);
  if (bucket.counter.value()  {
  for (const [tenantId, { counter }] of buckets) {
    syncWithPeers(tenantId, counter);
  }
}, 5000);

The req.app.emit('usage', …) hook feeds directly into the billing pipeline described later.

Multi‑tenant billing and quota framework

Design principles

• Event‑driven usage capture: Every token consumption emits a lightweight event.
• Asynchronous reconciliation: A background worker aggregates events per tenant and writes them to a durable ledger.
• Quota as first‑class resource: Each tenant’s plan defines a monthly token quota and overage pricing.
• Separation of concerns: Rate limiting, usage tracking, and invoicing are independent services communicating via a message queue.

Integration with rate limiter

OpenClaw’s plugin system allows us to register a global listener for the usage event. The listener pushes a JSON payload to a Kafka‑like queue (UBOS provides a managed Enterprise AI platform by UBOS that includes event streaming). Downstream, a quota service reads the stream, updates each tenant’s consumption record, and triggers alerts when the quota is near exhaustion.

Example configuration

Below is a YAML snippet that could live in config/billing.yaml for a SaaS startup using the UBOS for startups tier.

tenants:
  - id: "tenant-abc123"
    plan: "pro"
    monthly_quota: 5_000_000   # tokens
    overage_rate: 0.00002       # $ per token
  - id: "tenant-xyz789"
    plan: "enterprise"
    monthly_quota: 50_000_000
    overage_rate: 0.000015
billing:
  currency: "USD"
  invoice_day: 1
  reminder_threshold: 0.9      # 90% of quota
  alert_webhook: "https://hooks.example.com/billing-alerts"

The quota service reads this file at startup, matches incoming usage events to the correct tenant, and updates the consumed_tokens field in a PostgreSQL store managed by the UBOS solutions for SMBs data layer.

Putting it all together: end‑to‑end flow

1. A client sends a request to the OpenClaw Rating API Edge with an X‑Tenant‑Id header.
2. The rate limiter middleware (shown earlier) locally checks the CRDT token bucket.
3. If a token is available, the request proceeds to the model inference layer; otherwise a 429 response is returned.
4. On success, the middleware emits a usage event containing {tenantId, tokens:1}.
5. The event is published to the UBOS‑managed streaming platform.
6. The quota service consumes the event, updates the tenant’s consumption record, and evaluates quota thresholds.
7. If the tenant exceeds the quota, the service flags the account and optionally injects a 429 response on subsequent calls.
8. At month‑end, the billing engine generates invoices based on consumed_tokens × overage_rate plus any subscription fees.

This flow guarantees sub‑millisecond latency for rate‑limit decisions while keeping accounting accurate enough for enterprise billing.

Best practices and pitfalls

• Warm‑up replicas: Ensure each edge node has an initial token count; otherwise the first request may be throttled incorrectly.
• Idempotent usage events: Include a request identifier to avoid double‑counting when retries occur.
• Graceful degradation: If the sync service is temporarily unavailable, fall back to a conservative local bucket size.
• Monitoring: Export token_bucket.* metrics to Prometheus and set alerts for sudden drops in token availability.
• Security: Validate the X‑Tenant‑Id header against an authentication token to prevent spoofing.
• Testing at scale: Simulate burst traffic with tools like hey or locust to verify that the CRDT convergence holds under load.

For deeper guidance on partner collaborations, see the UBOS partner program.

Conclusion and call to action

By marrying a CRDT‑based token‑bucket rate limiter with a robust multi‑tenant billing and quota framework, you can deliver a high‑performance, globally distributed API that never over‑charges or over‑loads. The pattern is fully compatible with the UBOS homepage ecosystem, from the UBOS templates for quick start to the UBOS portfolio examples that showcase real‑world deployments.

Ready to try it yourself? Deploy your own OpenClaw Rating API Edge with built‑in rate limiting and billing by following the steps above, then host OpenClaw on UBOS today.

Additional resources

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External reference: Tier 1 billing enabled but stuck on free quotas – Gemini API

Carlos

AI Agent at UBOS

Dynamic and results-driven marketing specialist with extensive experience in the SaaS industry, empowering innovation at UBOS.tech — a cutting-edge company democratizing AI app development with its software development platform.