Your application is growing. Users love it, but you've started to notice a worrying trend: response times are creeping up. That image upload, report generation, or welcome email sequence is starting to make the UI feel sluggish. You know the solution: offload these long-running operations to background workers.
This immediately leads to a critical architectural decision: do you build and manage your own job queue system using tools like Redis, RabbitMQ, and libraries like Celery or BullMQ? Or do you opt for a managed service?
While the DIY approach seems tempting—offering total control and leveraging familiar technologies—it often hides a mountain of complexity and long-term costs. In this post, we'll break down the trade-offs and show why a managed platform like worker.do is the smarter choice for modern development teams.
The idea of "rolling your own" queue system is appealing. You pick your message broker (like Redis), choose a client library for your language, and you're off... or so it seems.
The initial setup might feel straightforward, but you're not just setting up a queue. You are implicitly signing up to become a distributed systems engineer. You are now responsible for the entire lifecycle of a critical piece of your infrastructure, which includes:
The "Total Cost of Ownership" for a self-hosted queue isn't the cost of the server; it's the immense cost of the developer and DevOps hours spent building, maintaining, and firefighting the system instead of creating value for your customers.
This is where a managed platform changes the game. worker.do is built on a simple premise: Background Workers, Simplified. It abstracts away all the infrastructural pain, allowing you to focus purely on the code you want to run.
Let's revisit the pain points of self-hosting and see how worker.do provides the solution:
| Self-Hosted Pain Point | The worker.do Solution |
|---|---|
| Infrastructure Wrangling | Zero Infrastructure. worker.do is serverless. You don't manage servers, containers, or message brokers. Ever. |
| The Scaling Nightmare | Intelligent Auto-Scaling. The platform automatically scales workers up and down based on queue load, ensuring peak performance during spikes and cost savings during lulls. |
| Observability Black Holes | Built-in Dashboards. Get immediate insight into queue length, job status, logs, and performance metrics—all out of the box. |
| Fragile Failure Handling | Robust, Configurable Retries. worker.do has built-in, customizable retry logic with exponential backoff. Failed jobs are handled gracefully without any manual intervention. |
Instead of spending weeks on infrastructure, you can implement a powerful, scalable background processing system in minutes.
With a self-hosted system, you're managing connections, worker processes, and queue definitions. With worker.do, you just enqueue a job. It's that simple.
Our Agentic Workflow Platform lets you define your tasks and treat them like simple APIs. Here’s how easy it is to queue a video processing job using our SDK:
import { Dô } from '@do/sdk';
// Initialize the .do client with your API key
const dô = new Dô(process.env.DO_API_KEY);
// Enqueue a new job to be processed by a worker
async function queueVideoProcessing(videoId: string) {
const job = await dô.worker.enqueue({
task: 'process-video',
payload: {
id: videoId,
format: '1080p',
watermark: true
}
});
console.log(`Job ${job.id} enqueued successfully!`);
return job.id;
}
This clean, declarative code is all you need. The platform handles the rest: routing the job to an available worker, scaling the fleet if necessary, and managing retries if the job fails. You've turned a complex background process into a simple, manageable Service-as-Software.
A background worker is a process that runs separately from your main application, handling tasks that would otherwise block the user interface or slow down request times. Common examples include sending emails, processing images, or generating reports.
The .do platform automatically scales your workers based on the number of jobs in the queue. This means you get the processing power you need during peak times and save costs during lulls, all without managing any infrastructure.
Any long-running or resource-intensive task is a great fit. This includes video encoding, data analysis, batch API calls, running AI model inferences, and handling scheduled jobs (cron).
.do provides built-in, configurable retry logic. If a job fails, the platform can automatically retry it with an exponential backoff strategy, ensuring transient errors are handled gracefully without manual intervention.
The choice between building and buying has never been clearer. Self-hosting a job queue system is a commitment to endless infrastructure maintenance. It's a distraction that pulls your most valuable resource—your development team—away from what truly matters: building a better product.
worker.do gives you back that time. It provides a robust, scalable, and observable background processing solution that just works. Stop wrestling with Redis connections and auto-scaling groups.
Ready to simplify your background tasks? Get started with worker.do today.