Boost Scalability and Slash Costs with Vercel Fluid Compute

Unlock high-performance, cost-effective, and stateful serverless applications

Vercel has taken a giant leap forward with Fluid Compute—a transformative evolution of the traditional serverless model. Whether you're tired of cold starts, limited execution times, or inefficient resource usage, Fluid Compute is designed to address these pain points and empower modern web and AI applications.

In this article, we’ll explore what Fluid Compute is, how it differs from traditional serverless computing, and why it matters for your next project. Read on to learn how you can enhance performance, reduce costs, and seamlessly run long or stateful tasks on Vercel.

What is Vercel Fluid Compute?

Vercel Fluid Compute is an innovative execution model that blends the simplicity of serverless with the performance and efficiency of long-running servers. In essence, it turns your serverless functions into high-performance, mini-servers that can handle multiple requests concurrently.

“The power of servers, in serverless form.” – Vercel

Fluid Compute makes it possible to overcome common challenges in traditional serverless environments by enabling:

• In-Function Concurrency: Multiple requests can be processed concurrently within a single function instance.
• Cold Start Reduction: Pre-warmed instances and bytecode caching minimize startup latency.
• Extended Execution Times: Functions can run up to 800 seconds (over 13 minutes) on Pro and Enterprise plans.
• Stateful Behavior: Global state and in-memory caches persist across multiple invocations on the same instance.
• Billing Efficiency: You pay only for the compute time actually used, reducing idle time wastage.

How Fluid Compute Differs from Traditional Serverless

The following table summarizes the key differences between traditional serverless and Vercel Fluid Compute:

Why Fluid Compute Was Needed

The traditional serverless model, while revolutionary, has faced several limitations:

• Idle Time Wastage: Serverless functions often remain idle while waiting for external responses, yet you still pay for that reserved compute time.
• Cold Starts and Latency Spikes: Every new instance faces a startup delay (cold start) that can impact performance, especially for latency-sensitive applications.
• Strict Execution Time Limits: Long-running tasks such as video transcoding, large data processing, or complex AI workflows were impractical due to short timeouts.
• Statelessness and Re-Initialization Overhead: The inability to share state between invocations meant that expensive initializations (e.g., loading models, database connections) had to be repeated.

Fluid Compute addresses these issues by allowing a single function instance to serve as a persistent, stateful mini-server. This enables:

• Maximized Throughput: Efficiently handle I/O-bound tasks by multitasking within the same instance.
• Cost Reduction: Lower overhead from fewer cold starts and better resource utilization.
• Enhanced Capabilities: Run moderately long tasks in a single invocation without splitting them across multiple functions.

Use Cases Where Fluid Compute Shines

Fluid Compute opens new possibilities for modern applications. Let’s explore some of the most impactful use cases:

Long-Running Tasks (e.g., Video Transcoding)

Imagine a web app where users upload videos that need transcoding. Traditional serverless functions might timeout on long-running, CPU-intensive tasks. With Fluid Compute, you can:

• Process videos in one go without offloading to external servers.
• Stream progress updates or use background execution (waitUntil) for post-response processing.

This approach simplifies architectures by reducing the need for additional job queues or dedicated processing services.

AI Inference and Complex "Chain-of-Thought" Workflows

Modern AI applications often involve:

• Latency-tolerant, heavy computations: Multi-step AI workflows where each step may involve waiting on an external API call.
• I/O-bound processing: Tasks where the function spends significant time waiting on data or model responses.
• Complex chaining: Iterative reasoning where context and state are maintained throughout the process.

With Fluid Compute:

• In-function concurrency allows one instance to handle multiple AI calls simultaneously.
• Extended execution times ensure that long, multi-step workflows complete successfully.
• Background processing with waitUntil lets you log analytics or update caches after sending a response.

Stateful and High-Concurrency Workloads

Fluid Compute enables scenarios that benefit from shared state and efficient resource utilization:

• Caching and Shared State: Load a dataset or model once and reuse it across multiple requests to speed up subsequent responses.
• Persistent Database Connections: Maintain open connections or connection pools across invocations, reducing the overhead of establishing new connections.
• Real-Time Streaming: Support for streaming responses (e.g., server-sent events) for applications that require continuous updates.
• High-Traffic APIs: Efficiently handle traffic bursts by allowing a single instance to serve multiple requests concurrently, reducing overall compute costs.

Developer Experience: Using Fluid Compute

Adopting Fluid Compute is straightforward and requires no changes to your application code. Here’s how you can enable it on your Vercel project:

1. Access Your Project Settings: Navigate to the Vercel dashboard and select your project.
2. Enable Fluid Compute: Under the Functions section, locate the Fluid Compute toggle and switch it ON.
3. Redeploy Your Project: Trigger a new deployment to apply the changes.

Code Example: Configuring a Function with Extended Timeout

In a Next.js API route, you can specify a longer execution time by exporting a configuration:

export const config = { maxDuration: 300 }; // Set timeout to 300 seconds

export default async function handler(req, res) {
  // Your long-running logic here
  res.status(200).json({ status: 'Processing started' });
}        

Code Example: Using waitUntil for Background Tasks

Fluid Compute introduces the waitUntil API to handle tasks after the HTTP response is sent:

import { waitUntil } from '@vercel/functions';

export default async function handler(req, res) {
  // Handle the request and send the response immediately
  res.status(200).json({ status: 'ok' });
  
  // Continue background processing (e.g., logging or analytics)
  waitUntil(logAnalyticsEvent(req));
}

async function logAnalyticsEvent(req) {
  // Your asynchronous logging logic here
  return Promise.resolve();
}
        

Observability and Monitoring

Once enabled, monitor your Fluid Compute performance through Vercel’s Observability dashboard. Track key metrics such as:

• Execution time
• Concurrency levels
• Cold start occurrences
• Overall compute utilization

These insights help you optimize your application and quantify the benefits of Fluid Compute.

Final Thoughts

Vercel Fluid Compute is set to transform how developers build and scale serverless applications. By merging the scalability of serverless with the robustness of traditional servers, Fluid Compute enables:

• Faster response times through minimized cold starts.
• Extended execution times for long-running and complex tasks.
• Stateful processing that reduces redundant initializations.
• Significant cost savings by maximizing compute utilization.

Are you ready to revolutionize your serverless architecture? Head over to your Vercel dashboard, enable Fluid Compute with a simple toggle, and redeploy your project to experience the future of cloud computing today.

Experiment with Fluid Compute on a test environment. Monitor the metrics, compare performance, and share your success stories with the community. Let’s build faster, smarter, and more efficient applications together!

For more information, refer to the official documentation on Vercel Fluid Compute.

Happy coding, and may your compute be fluid!