Your application is humming along, users are happy, and then it happens. A marketing campaign goes viral, a new feature takes off, or it's simply peak season. Suddenly, your application grinds to a halt. User requests time out, dashboards fail to load, and your once-responsive app feels sluggish. The culprit? Long-running tasks like sending newsletters, generating reports, or processing uploads are hogging all the resources.
The standard solution is to offload these tasks to background jobs. By moving heavy work to a separate worker process, your main application stays fast and available. But this introduces a new, expensive problem: How do you manage the infrastructure for these workers?
Traditionally, you had two bad options:
There's a better way. On-demand, auto-scaling infrastructure ensures your jobs are processed promptly without forcing you to overpay for idle capacity. Let's see how.
Imagine you run a fleet of servers dedicated to processing background jobs. Your analytics show that you need 10 worker instances to handle your busiest hour, from 9 AM to 10 AM. But for the other 23 hours of the day, you only need two.
If you provision 10 servers to run 24/7, you are paying for 8 servers to sit completely idle for most of the day. This is a direct hit to your infrastructure budget. The cost isn't just monetary; it also includes the engineering time spent monitoring, patching, and managing this fleet. When your workload pattern changes, an engineer has to manually intervene to scale up or down. This model is inefficient, expensive, and slow to react.
This is where a managed service like worker.do changes the game. Instead of maintaining a fixed set of servers, worker.do provides scalable background workers on-demand.
Our platform constantly monitors the depth of your job queues.
This elastic scaling means you get the best of both worlds: the power to handle any workload and the cost-effectiveness of paying only for the compute time you actually consume. You're no longer provisioning for the "what if" scenario; you're reacting perfectly to the "what is" reality of your application's workload.
The beauty of a managed service is its simplicity. You don't need to think about server management, auto-scaling groups, or container orchestration. You just enqueue your jobs with a simple API call, and we handle the rest.
Here’s how easy it is to offload a report-generation task with the worker.do SDK:
import { Worker } from '@do/sdk';
// Initialize the worker service with your API key
const worker = new Worker('YOUR_API_KEY');
// Define the task payload
const payload = {
userId: 'usr_1a2b3c',
reportType: 'monthly_sales',
format: 'pdf'
};
// Enqueue a new job to be processed asynchronously
const job = await worker.enqueue({
queue: 'reports',
task: 'generate-report',
payload: payload,
retries: 3
});
console.log(`Job ${job.id} has been successfully enqueued.`);
That's it. Behind this simple call, worker.do ensures that a worker is available to process your generate-report task. If 10,000 users request a report at the same time, our system scales up to meet the demand. When the queue is empty, the resources scale down.
While on-demand scaling is a massive cost-saver, a robust job processing system needs more. At worker.do, we've built a platform to handle the entire lifecycle of your asynchronous tasks.
Stop overprovisioning and start saving. By moving from a fixed-capacity model to an on-demand one, you eliminate wasted resources, reduce management overhead, and gain the confidence that your application can handle anything you throw at it.
Ask yourself: how much of your current infrastructure budget is spent on idle worker processes? How much engineering time is dedicated to managing them? With worker.do, you can reclaim that budget and free up your team to build features that matter.
Ready to slash your infrastructure costs and guarantee performance? Get started with worker.do today and experience the power of scalable background workers on-demand.