How Does Pricing Differ Between Serverless and Provisioned Infrastructure?

Let’s kick this off with a surprising stat: According to Flexera’s 2024 State of the Cloud Report, organizations waste over 30% of their cloud spend on underutilized infrastructure. That’s billions of dollars globally going straight down the drain—not because companies aren’t using the cloud, but because they’re using it inefficiently.

And that’s precisely where the debate between serverless and provisioned infrastructure begins.

In today’s cloud-native landscape, where AI inference as a service, microservices, and real-time data processing are becoming the norm, choosing the right infrastructure model is not just a tech decision—it’s a financial strategy.

Should you go with serverless, which promises automatic scaling and “pay-as-you-go” pricing? Or do you opt for provisioned infrastructure, where you pre-allocate resources (often more than you need) but may get better performance predictability?

To make a smart call, you need to understand how the pricing mechanics of each model work. That’s what we’re here to unpack.

Let’s break down the true cost of serverless vs. provisioned infrastructure—and where platforms like Cyfuture Cloud fit into the picture.

Understanding the Basics: What Are We Really Comparing?

Before we jump into the pricing models, let’s get our definitions straight.

Serverless Infrastructure: You don’t manage servers. Your cloud provider runs your code on-demand and scales it automatically. You pay only for actual usage, usually measured in milliseconds.

Provisioned Infrastructure: You manually allocate computing resources (VMs, CPUs, GPUs) in advance. These resources stay up whether or not they’re being used. You pay for availability, not just usage.

While both options can exist in the cloud, their pricing philosophies are fundamentally different.

The Cost Mechanics of Serverless

1. Pay for What You Use

The biggest allure of serverless is cost-efficiency. You’re billed based on:

Number of invocations

Execution duration (e.g., per 100ms)

Memory used during execution

Example: Let’s say your serverless function runs for 400ms and uses 512MB RAM.

Cost = (400ms ÷ 100ms) × price per 100ms × memory used

Multiply that by the number of times it runs, and that’s your bill.

Platforms like Cyfuture Cloud offer AI inference as a service in a serverless format, charging only for the actual compute time when your model is processing requests. So, if your usage is spiky or unpredictable, serverless makes total economic sense.

2. No Idle Cost

If no request comes in, you don’t pay a dime. This is ideal for:

Event-driven applications

Low-traffic microservices

Startups still validating product-market fit

3. Scaling Included

No need to spin up or manage infrastructure. Scaling is automatic, and pricing scales with it—linear and predictable.

The Cost Mechanics of Provisioned Infrastructure

1. Always-On Resources

When you provision a VM or a container, it's yours—whether you're using it or not. You’re billed for:

Uptime (per second/minute/hour)

Resource size (CPU, GPU, RAM, storage)

So even during off-peak hours, you pay.

2. Lower Cost Per Unit—But Only If Fully Utilized

Per-hour or per-second costs may be lower than serverless, but only if you're consistently using the resources. Idle time is waste.

Let’s say you reserve a GPU-enabled VM on Cyfuture Cloud for AI inference as a service, but traffic drops at night. You’re still paying full price, even if your model is sitting idle.

3. More Control, More Responsibility

Provisioned infrastructure gives you performance tuning abilities. Great! But it also means:

You handle scaling (auto-scaling may help but costs can spike)

You manage patching and uptime

You risk overprovisioning (common in enterprise apps)

Comparing Apples to Apples: A Cost Simulation

Let’s take a real-world example:

You’re running an AI-powered chatbot that handles 100,000 user queries daily.

Using Serverless:

Each inference call takes 200ms

Memory usage: 256MB

Cyfuture Cloud serverless price: ₹0.0002 per 100ms per 128MB

Daily cost:
(100,000 × 2 units of 100ms × 2 memory units) × ₹0.0002 = ₹80/day ≈ ₹2,400/month

Using Provisioned Infrastructure:

You provision a VM with 2 vCPUs and 4GB RAM @ ₹5/hour

It's up 24×7

Monthly cost:
₹5 × 24 × 30 = ₹3,600/month

Now here’s the kicker:

If you consistently max out the VM, provisioned might be better

If usage is bursty, serverless saves you ₹1,200/month

When to Choose What: The Real Cost Strategy

Go Serverless If:

Your traffic is inconsistent

You want a quick launch without worrying about infrastructure

You're deploying microservices or event-driven apps

You're scaling AI inference with unpredictable workloads

Serverless is also great for early-stage products. Cyfuture Cloud makes it even easier by bundling AI inference as a service with serverless hosting, meaning you can deploy ML models without configuring GPUs manually.

Go Provisioned If:

Your usage is predictable and high

You’re doing heavyweight processing continuously (e.g., video encoding, training models)

You need fine-tuned control over compute environment

You’ve done the math and resource utilization is near 100%

This is usually the preferred route for large enterprises or applications with fixed demand curves.

Hybrid: The Best of Both Worlds?

Here’s a smart approach many modern developers are adopting: use serverless for spikes, and provisioned for base load.

For example, you can deploy a small inference service for real-time requests using provisioned compute on Cyfuture Cloud, and offload batch inference to serverless functions during low-traffic hours.

The combination allows:

Predictable base cost

Elastic scalability for unexpected traffic

Cost optimization through intelligent resource routing

This strategy is especially effective for teams managing AI inference as a service where workloads fluctuate but still require guaranteed performance at core levels.

Conclusion: Think Cost, Not Just Code

So, back to the big question: How does pricing differ between serverless and provisioned infrastructure?

Short answer: Serverless = pay for usage. Provisioned = pay for uptime.

Long answer: It depends on your workload, traffic patterns, and how much control you need. Neither option is always better—but one is always more suitable depending on the case.

If your architecture needs agility, flexibility, and burst-friendly billing, Cyfuture Cloud’s serverless stack is ideal. If you're focused on consistent performance and high throughput, their provisioned infrastructure lets you get the most out of every rupee—provided you optimize resource use.

At the end of the day, it’s not just about servers or no servers. It’s about value for money, predictable performance, and scalability on your terms.

So next time you architect your cloud system, don’t just pick a model. Pick a pricing philosophy that matches your business model.