Rate & concurrency limits

What is concurrency?

Concurrency refers to the number of API requests you can have in progress (or running) simultaneously. If your plan supports 10 concurrent requests, you can process up to 10 requests at the same time. You’ll get a rate limit error if you send an 11th request while 10 are already processing. Think of concurrency like a team of workers in an office. Each worker represents a “concurrent request slot.” If you have 10 workers, you can assign them 10 tasks (requests) simultaneously. If you try to assign an 11th task while all workers are occupied, you’ll need to wait until one worker finishes. In cloro, each “task” is an API request to an AI model, and each “worker” is a concurrent request slot available based on your subscription.

Rate limits vs. concurrency limits

Free trial accounts are limited to 1 concurrent job. Upgrade for multi-threaded workloads. See the pricing table for concurrency limits per plan. cloro uses two different types of limits depending on the endpoint type:

Limit type	Endpoints affected	How it works
Rate limits	All endpoints (`/v1/*`)	1,000 requests per second per endpoint
Concurrency limits	Monitor endpoints (`/v1/monitor/*`)	Based on your subscription plan (simultaneous requests)

Rate limits restrict how many requests you can make per second, while concurrency limits restrict how many requests can be processing simultaneously. Monitor endpoints are subject to both rate limits (1,000/sec per endpoint) and concurrency limits (subscription-based).

Monitoring concurrency with headers

Each response includes HTTP headers to help you manage your API usage:

Header	Description
`X-Concurrent-Limit`	Total concurrent requests allowed by your plan
`X-Concurrent-Current`	Number of requests currently processing
`X-Concurrent-Remaining`	Available concurrent slots when request was received

For example, if your plan supports 20 concurrent requests and you send 3 requests simultaneously:

X-Concurrent-Limit: 20
X-Concurrent-Current: 3
X-Concurrent-Remaining: 17

This means 17 slots were available when the request was processed.

Monitoring rate limits with headers

All endpoints include rate limit headers in each response:

Header	Description
`X-RateLimit-Limit`	Maximum requests per second allowed (1,000)
`X-RateLimit-Remaining`	Remaining requests available in this second

The limit is per endpoint per API key — each /v1/* path has its own independent 1,000 RPS bucket. For example, if you make a request to /v1/monitor/chatgpt:

X-RateLimit-Limit: 1000
X-RateLimit-Remaining: 999

This means you can make 999 more requests to that endpoint in the current second before hitting the rate limit. The counter resets every second.

Credit headers

Monitor endpoints (/v1/monitor/*) also return the credit balance and the amount charged for each request:

Header	Description
`X-Credits-Remaining`	Number of credits remaining in your account
`X-Credits-Charged`	Number of credits charged for this request

Track spend per request and alert before you run low:

function trackCredits(response) {
  const creditsRemaining = response.headers["x-credits-remaining"];
  const creditsCharged = response.headers["x-credits-charged"];

  console.log(`Credits charged: ${creditsCharged}`);
  console.log(`Credits remaining: ${creditsRemaining}`);

  // Alert if running low
  if (creditsRemaining < 100) {
    console.warn("Low credit balance. Consider topping up");
  }
}

For how credits are allocated, expire, and are priced, see Billing & credits and Providers.

Need higher concurrency? Self-serve plans can be upgraded in the dashboard and the new limit applies immediately. Enterprise customers should email support@cloro.dev to upgrade their existing plan.

Using headers for optimization

Monitor these headers to optimize your request patterns:

function checkConcurrencyUsage(response) {
  const limit = parseInt(response.headers["x-concurrent-limit"]);
  const current = parseInt(response.headers["x-concurrent-current"]);
  const remaining = parseInt(response.headers["x-concurrent-remaining"]);

  console.log(`Concurrency: ${current}/${limit} (${remaining} available)`);

  // Adjust your batch size based on remaining slots
  return Math.min(remaining, 5); // Don't exceed 5 requests per batch
}

For all endpoints with rate limits, you can monitor usage and implement backoff:

async function checkRateLimit(response) {
  const limit = parseInt(response.headers["x-ratelimit-limit"]);
  const remaining = parseInt(response.headers["x-ratelimit-remaining"]);

  console.log(`Rate limit: ${remaining}/${limit} requests remaining`);

  // If you're running low on requests, wait before continuing
  if (remaining < 50) {
    const waitTime = 1000; // Wait 1 second for the counter to reset
    console.log(`Rate limit nearly exceeded. Waiting ${waitTime}ms...`);
    await new Promise((resolve) => setTimeout(resolve, waitTime));
  }

  return remaining;
}

When you do hit a 429, retry with exponential backoff rather than immediately re-sending:

async function makeRequestWithRetry(requestFn, maxRetries = 3) {
  for (let i = 0; i < maxRetries; i++) {
    try {
      return await requestFn();
    } catch (error) {
      if (error.status === 429 && i < maxRetries - 1) {
        const delay = Math.pow(2, i) * 1000; // Exponential backoff
        await new Promise((resolve) => setTimeout(resolve, delay));
        continue;
      }
      throw error;
    }
  }
}

See Error handling for the full 429 error shapes.

Implementation patterns

Most programming languages require explicit concurrency handling. Two common approaches:

Pattern 1: Async with webhooks

For large-scale processing, submit tasks and handle results via webhooks. You don’t need to send requests in batches; cloro handles concurrency automatically. Send API requests for all your tasks concurrently (one request per task):

import axios from "axios";

const API_KEY = process.env.API_KEY;
const TASK_API = "https://api.cloro.dev/v1/async/task";

async function submitTasks(tasks, webhookUrl) {
  // Send API requests concurrently (one request per task)
  await Promise.all(
    tasks.map((task) =>
      axios.post(
        TASK_API,
        {
          taskType: "CHATGPT",
          webhook: { url: webhookUrl },
          payload: task,
        },
        {
          headers: { Authorization: `Bearer ${API_KEY}` },
        }
      )
    )
  );
}

// Webhook handler (Express.js)
app.post("/webhook-handler", (req, res) => {
  const { task, response } = req.body;
  console.log(`Task ${task.id} completed: ${response.text.slice(0, 100)}...`);

  // Process your result here
  saveResult(task.id, response);

  // Always respond quickly
  res.status(200).send();
});

// Usage
const tasks = [
  { prompt: "Analyze market trends", country: "US" },
  { prompt: "Research competitors", country: "US" },
  // ... hundreds more
];

submitTasks(tasks, "https://your-app.com/webhook-handler");

import requests
import asyncio
import json

API_KEY = "YOUR_API_KEY"
TASK_API = "https://api.cloro.dev/v1/async/task"

async def submit_tasks(tasks, webhook_url):
    # Send API requests concurrently (one request per task)
    submit_tasks = []
    for task in tasks:
        submit_tasks.append(
            requests.post(
                TASK_API,
                json={
                    "taskType": "CHATGPT",
                    "webhook": {"url": webhook_url},
                    "payload": task
                },
                headers={"Authorization": f"Bearer {API_KEY}"}
            )
        )

    # Wait for all submissions to complete
    responses = await asyncio.gather(*[asyncio.to_thread(req) for req in submit_tasks])
    print("All tasks submitted")

# Usage
tasks = [
    {"prompt": "Analyze market trends", "country": "US"},
    {"prompt": "Research competitors", "country": "US"},
    # ... hundreds more
]

asyncio.run(submit_tasks(tasks, "https://your-app.com/webhook-handler"))

# Webhook handler example (Flask)
"""
from flask import Flask, request, jsonify

app = Flask(__name__)

@app.route('/webhook-handler', methods=['POST'])
def handle_webhook():
    data = request.json
    task = data.get('task', {})
    response = data.get('response', {})

    print(f"Task {task.get('id')} completed: {response.get('text', '')[:100]}...")

    # Process your result here
    save_result(task.get('id'), response)

    return '', 200
"""

#!/bin/bash

API_KEY="YOUR_API_KEY"
TASK_API="https://api.cloro.dev/v1/async/task"
WEBHOOK_URL="https://your-app.com/webhook-handler"

# Submit tasks individually (cloro handles concurrency automatically)
submit_task() {
  local prompt="$1"
  local country="$2"

  curl -s -X POST "$TASK_API" \
    -H "Authorization: Bearer $API_KEY" \
    -H "Content-Type: application/json" \
    -d "{
      \"taskType\": \"CHATGPT\",
      \"webhook\": {\"url\": \"$WEBHOOK_URL\"},
      \"payload\": {
        \"prompt\": \"$prompt\",
        \"country\": \"$country\"
      }
    }" &
}

# Example usage - send API requests concurrently
tasks=(
  "Analyze market trends|US"
  "Research competitors|US"
  "Extract pricing data|US"
  "Summarize industry reports|US"
  "Identify key trends|US"
  # ... add more tasks as needed
)

echo "Submitting ${#tasks[@]} tasks..."

# Submit all tasks in background (no need to limit concurrency)
for task_data in "${tasks[@]}"; do
  IFS='|' read -r prompt country <<< "$task_data"
  submit_task "$prompt" "$country"
done

# Wait for all submissions to complete
wait

echo "All tasks submitted successfully"

Pattern 2: Concurrent workers

For real-time processing where you want immediate results, run multiple workers that make direct API calls:

import axios from "axios";

const API_KEY = process.env.API_KEY;
const API_URL = "https://api.cloro.dev/v1/monitor/chatgpt";

async function makeRequest(id, prompt) {
  const start = Date.now();

  try {
    const response = await axios.post(API_URL, {
      prompt: prompt,
      country: "US",
    }, {
      headers: {
        Authorization: `Bearer ${API_KEY}`
      }
    });

    const latency = Date.now() - start;
    console.log(`Request #${id}: Success (${latency}ms)`);

    // Monitor concurrency usage
    const limit = parseInt(response.headers["x-concurrent-limit"]);
    const current = parseInt(response.headers["x-concurrent-current"]);
    const remaining = parseInt(response.headers["x-concurrent-remaining"]);

    return {
      success: true,
      latency,
      data: response.data,
      usage: { limit, current, remaining }
    };

  } catch (error) {
    const latency = Date.now() - start;
    console.log(`Request #${id}: Failed (${latency}ms)`);

    if (error.response?.status === 429) {
      console.log(`Rate limited - ${error.response.headers["retry-after"] || "unknown"} seconds`);
    }

    return { success: false, latency, error: error.message };
  }
}

async function runConcurrentRequests(prompts, concurrency = 10) {
  console.log(`Starting ${prompts.length} requests with ${concurrency} concurrent workers\n`);

  const startTime = Date.now();
  const results = [];
  let requestId = 0;

  // Worker function
  async function worker() {
    while (requestId < prompts.length) {
      const id = ++requestId;
      const result = await makeRequest(id, prompts[requestId - 1]);
      results.push(result);
    }
  }

  // Run concurrent workers
  await Promise.all(
    Array(concurrency).fill(0).map(() => worker())
  );

  const duration = Date.now() - startTime;
  const successful = results.filter(r => r.success).length;
  const rateLimited = results.filter(r => r.error?.includes('Rate limited')).length;

  console.log("\n" + "=".repeat(40));
  console.log(`Total: ${prompts.length}`);
  console.log(`Success: ${successful} (${((successful/prompts.length)*100).toFixed(1)}%)`);
  console.log(`Rate limited: ${rateLimited}`);
  console.log(`Duration: ${(duration/1000).toFixed(1)}s`);
  console.log(`RPS: ${(prompts.length/duration*1000).toFixed(1)}`);
  console.log("=".repeat(40));

  return results;
}

// Usage
const prompts = [
  "What is AI and how does it work?",
  "Explain machine learning basics",
  "What are neural networks?",
  "How does deep learning work?",
  "What is natural language processing?",
  // ... add more prompts as needed
];

runConcurrentRequests(prompts, 5) // Start with conservative concurrency
  .then(results => console.log(`Completed processing`))
  .catch(console.error);

import asyncio
import aiohttp
import time
from typing import List, Dict, Any

class ConcurrentWorker:
    def __init__(self, api_key: str, api_url: str):
        self.api_key = api_key
        self.api_url = api_url

    async def make_request(self, session, id: int, prompt: str):
        start_time = time.time()

        try:
            async with session.post(
                self.api_url,
                json={
                    "prompt": prompt,
                    "country": "US"
                },
                headers={
                    "Authorization": f"Bearer {self.api_key}"
                }
            ) as response:
                data = await response.json()
                latency = (time.time() - start_time) * 1000

                # Monitor concurrency usage
                limit = int(response.headers.get("x-concurrent-limit", 0))
                current = int(response.headers.get("x-concurrent-current", 0))
                remaining = int(response.headers.get("x-concurrent-remaining", 0))

                print(f"Request #{id}: Success ({latency:.0f}ms)")

                return {
                    "success": True,
                    "latency": latency,
                    "data": data,
                    "usage": {"limit": limit, "current": current, "remaining": remaining}
                }

        except Exception as e:
            latency = (time.time() - start_time) * 1000
            print(f"Request #{id}: Failed ({latency:.0f}ms)")

            return {
                "success": False,
                "latency": latency,
                "error": str(e)
            }

    async def run_concurrent_requests(self, prompts: List[str], concurrency: int = 10):
        print(f"Starting {len(prompts)} requests with {concurrency} concurrent workers\n")

        start_time = time.time()
        results = []
        request_id = 0

        async with aiohttp.ClientSession() as session:
            async def worker():
                nonlocal request_id
                while request_id < len(prompts):
                    id = request_id + 1
                    result = await self.make_request(session, id, prompts[request_id])
                    results.append(result)
                    request_id += 1

            # Run concurrent workers
            await asyncio.gather(*[worker() for _ in range(concurrency)])

        duration = time.time() - start_time
        successful = sum(1 for r in results if r["success"])
        rate_limited = sum(1 for r in results if "rate limited" in r.get("error", "").lower())

        print("\n" + "=" * 40)
        print(f"Total: {len(prompts)}")
        print(f"Success: {successful} ({(successful/len(prompts)*100):.1f}%)")
        print(f"Rate limited: {rate_limited}")
        print(f"Duration: {duration:.1f}s")
        print(f"RPS: {len(prompts)/duration:.1f}")
        print("=" * 40)

        return results

# Usage
async def main():
    worker = ConcurrentWorker("YOUR_API_KEY", "https://api.cloro.dev/v1/monitor/chatgpt")

    prompts = [
        "What is AI and how does it work?",
        "Explain machine learning basics",
        "What are neural networks?",
        # ... add more prompts as needed
    ]

    results = await worker.run_concurrent_requests(prompts, concurrency=5)
    print(f"Completed processing")

if __name__ == "__main__":
    asyncio.run(main())

#!/bin/bash

API_KEY="YOUR_API_KEY"
API_URL="https://api.cloro.dev/v1/monitor/chatgpt"
TOTAL_REQUESTS=10
CONCURRENCY=5

# Function to make a single request
make_request() {
  local id=$1
  local prompt=$2
  local start_time=$(date +%s%3N)

  response=$(curl -s -w "\n%{http_code}\n%{time_total}\n%{x-concurrent-limit}\n%{x-concurrent-current}\n%{x-concurrent-remaining}" \
    -X POST "$API_URL" \
    -H "Authorization: Bearer $API_KEY" \
    -H "Content-Type: application/json" \
    -d "{\"prompt\":\"$prompt\",\"country\":\"US\"}" \
    2>/dev/null)

  # Parse response
  http_code=$(echo "$response" | sed -n '2p')
  time_total=$(echo "$response" | sed -n '3p')
  limit=$(echo "$response" | sed -n '4p')
  current=$(echo "$response" | sed -n '5p')
  remaining=$(echo "$response" | sed -n '6p')
  body=$(echo "$response" | sed -n '1p')

  latency=$(echo "$time_total * 1000" | bc)

  if [[ "$http_code" == "200" ]]; then
    echo "Request #$id: Success (${latency%.*}ms) - Usage: $current/$limit ($remaining available)"
  else
    echo "Request #$id: Failed (${latency%.*}ms) - HTTP $http_code"
  fi
}

# Worker function that processes multiple requests
worker() {
  local worker_id=$1
  local max_requests=$2

  while true; do
    # Get next request ID from shared counter
    id=$(wget -qO- "http://localhost:8080/next-id" 2>/dev/null || echo "0")

    if [[ $id -gt $max_requests ]]; then
      break
    fi

    prompt="Query $id: What are the benefits of concurrent processing?"
    make_request "$id" "$prompt"
  done
}

# Simple ID counter using files (you could use Redis or a database)
echo "1" > /tmp/next_id

# Start workers
for ((i=1; i<=CONCURRENCY; i++)); do
  worker $i $TOTAL_REQUESTS &
done

# Wait for all workers to complete
wait

echo -e "\n$(printf '=%.0s' {1..40})"
echo "Load test completed"

# Cleanup
rm -f /tmp/next_id

Quick reference

Use case	Pattern	When to use
Large batches	Async + webhooks	Large batches, don’t need immediate results
Real-time results	Concurrent workers	Need immediate responses, smaller batches

Common questions

Why am I getting 429 rate limit errors?

A 429 error means you’re hitting rate limits. This can happen for two reasons: Concurrency limit exceeded (monitor endpoints only) You’re making too many simultaneous requests beyond your plan’s concurrent request limit. Solution:

Check your current usage with response headers: X-Concurrent-Limit, X-Concurrent-Current, X-Concurrent-Remaining
Implement request queuing in your application
Use exponential backoff when retrying
Upgrade your plan for higher limits

Rate limit exceeded (all endpoints) You’ve exceeded 1,000 requests per second to a single endpoint. Solution:

Monitor X-RateLimit-Remaining header
Spread requests over time (the counter resets every second)
Use the async queue for non-time-sensitive requests
Implement retry logic with exponential backoff

See the error handling guide for detailed error responses and implementation patterns.

How do I check my concurrency limit?

Your concurrency limit is shown in the response headers of every API call:

X-Concurrent-Limit: 20        # Your total limit
X-Concurrent-Current: 5       # Currently in use
X-Concurrent-Remaining: 15    # Available slots

You can also use the async status endpoint to see concurrency stats of your account.

Can I increase my concurrency limit?

Yes. Self-serve plans can be upgraded directly in the dashboard — the new limit applies immediately, no support ticket required. If you need concurrency above the highest self-serve tier, email support@cloro.dev for an enterprise quote.

Does higher concurrency delay my logs or dashboards?

No. Dashboard log ingestion runs independently from request processing. If logs look delayed during heavy load, the cause is usually batching on the dashboard side, not concurrency — entries normally surface within a minute.

Can I burst above my concurrency limit?

No. The limit is hard — the (N+1)th simultaneous request gets a 429 immediately rather than queueing. Use the async API if you want cloro to handle queueing for you instead of managing burst capacity yourself.

What’s the best way to handle large batches of requests?

For large batches, choose the right pattern based on your needs: For non-time-sensitive batches (recommended):

Use Pattern 1: Async with webhooks
Send all API requests concurrently. cloro handles queuing automatically
Receive results via webhook when complete
No need to manage concurrency yourself

For real-time results:

Use Pattern 2: Concurrent workers
Respect your plan’s concurrency limit
Monitor X-Concurrent-Remaining header
Implement exponential backoff for 429 errors

Introduction

Guides

Integrations

Rate & concurrency limits

What is concurrency?

Rate limits vs. concurrency limits

Monitoring concurrency with headers

Monitoring rate limits with headers

Credit headers

Using headers for optimization

Implementation patterns

Pattern 1: Async with webhooks

Pattern 2: Concurrent workers

Quick reference

Common questions

Why am I getting 429 rate limit errors?

How do I check my concurrency limit?

Can I increase my concurrency limit?

Does higher concurrency delay my logs or dashboards?

Can I burst above my concurrency limit?

What’s the best way to handle large batches of requests?

​What is concurrency?

​Rate limits vs. concurrency limits

​Monitoring concurrency with headers

​Monitoring rate limits with headers

​Credit headers

​Using headers for optimization

​Implementation patterns

​Pattern 1: Async with webhooks

​Pattern 2: Concurrent workers

​Quick reference

​Common questions

​Why am I getting 429 rate limit errors?

​How do I check my concurrency limit?

​Can I increase my concurrency limit?

​Does higher concurrency delay my logs or dashboards?

​Can I burst above my concurrency limit?

​What’s the best way to handle large batches of requests?

What is concurrency?

Rate limits vs. concurrency limits

Monitoring concurrency with headers

Monitoring rate limits with headers

Credit headers

Using headers for optimization

Implementation patterns

Pattern 1: Async with webhooks

Pattern 2: Concurrent workers

Quick reference

Common questions

Why am I getting 429 rate limit errors?

How do I check my concurrency limit?

Can I increase my concurrency limit?

Does higher concurrency delay my logs or dashboards?

Can I burst above my concurrency limit?

What’s the best way to handle large batches of requests?