Serverless computing lets you run code without provisioning or managing servers, with the provider handling scaling automatically. Both AWS and Azure offer mature platforms. The strongest fit for serverless computing on Azure and AWS depends on the workload, not on brand preference. This reduced operational overhead allows engineering teams to focus entirely on delivering core business functionality rather than managing infrastructure.
Key takeaways:
A retailer's checkout API gets hit twice as hard every November and barely touches the rest of the year. Paying for idle servers for eleven months to cover one month of spikes never made sense. Multiply that pattern across dozens of internal tools, and the wasted spend adds up quickly.
However, serverless isn't always the most economical choice; for steady, predictable traffic, traditional fixed servers remain far more cost-effective than pay-as-you-go pricing. However, serverless isn't always the cheapest route; for steady, predictable traffic, traditional fixed servers remain far more cost-effective than pay-as-you-go pricing.
That's the gap AWS Lambda serverless computing was built to close. You pay for execution, not idle capacity. Utilising structured cloud migration services ensures your architecture is properly optimised for these serverless benefits.
Azure offers a similar model through Functions, Logic Apps, and Event Grid. Both ecosystems now treat serverless as a default option for new workloads, not a niche one.
This article looks at how that works in practice and where AWS and Azure differ. It also covers what IT leaders should check before relying on it in production.
It's a cloud model where the provider handles provisioning, scaling, and patching, making the underlying infrastructure entirely invisible to you. While the name is misleading, servers still run your code. You never manage them directly; you simply deploy your code instead of configuring servers or clusters.
Both major cloud providers split serverless into a function layer and a set of supporting managed services. The mechanics differ slightly, but the core idea, an event triggers code, stays the same.
| Layer | AWS | Azure |
|---|---|---|
| Compute | Lambda | Functions |
| Workflow automation | Step Functions | Logic Apps |
| Event routing | EventBridge | Event Grid |
| Serverless container compute | ECS/Fargate | Container Apps |
AWS Lambda remains the most mature serverless function compute service. It handles event-driven functions triggered by HTTP requests, queues, or file uploads.
Glue provides serverless ETL for data pipelines. ECS with Fargate provides a serverless compute engine for containers, so you don't have to manage the underlying cluster. Step Functions can orchestrate multiple Lambda invocations into a single workflow when logic spans more than one function.
Azure serverless computing centres on three services. Functions handle event-driven code. Logic Apps automate workflows across systems. Event Grid routes events between services in near real time.
The business case usually comes down to cost and speed. You stop paying for idle compute, and teams ship features faster because there's no server to provision first.
A workload that spikes for two hours a day runs on a fraction of the compute. A constantly-provisioned server would need far more, and billing follows the same curve.
Maintenance overhead drops, too. Patching, scaling, and capacity planning move from your team's list to the provider's. Built-in redundancy across availability zones also comes by default, without extra configuration.
Serverless fits some workloads far better than others. Knowing the difference upfront avoids a costly redesign later.
Event-driven APIs, scheduled batch jobs, and image or file processing are common examples of serverless computing. Each has unpredictable or spiky demand. That's exactly what serverless billing rewards.
Long-running processes with steady, predictable loads are usually cheaper on traditional infrastructure. A steady-state API serving the same traffic around the clock rarely benefits from per-execution billing. The cost curve just doesn't favour it.
Cold starts can also rule out serverless for workloads with strict latency requirements.
Furthermore, hard execution time limits and stateless environments make complex, multi-step transactions difficult to manage, forcing developers to rely on heavy architectural orchestration or external state stores.
Enterprises evaluating serverless computing use cases usually start with a single API or background job rather than a full migration. That's a sound approach. Working with structured enterprise cloud advisory services helps map these initial small wins into a secure, long-term roadmap.
Running serverless on Azure and AWS in parallel is increasingly common, too. Often, that's because an acquisition brings in a second cloud, rather than by design. It usually works fine, provided governance, identity, security, and observability span both environments rather than being managed separately.
Before committing to serverless for a given workload, IT leaders should check:
None of these rules goes away. They just changed how a deployment should be scoped and tested before going live.
Navigating these technical trade-offs requires balancing immediate architectural needs with long-term infrastructure costs. Making the right choice demands a deep evaluation of your existing legacy systems and future scalability targets.
Most teams don't need to choose serverless everywhere or nowhere. They need to know which serverless computing applications actually make sense for their workload mix.
LDS Infotech helps enterprise teams assess AWS and Azure environments to identify where serverless can reduce costs without adding operational risk, and where traditional infrastructure remains more economical.
If you're running steady, predictable workloads on pay-per-execution pricing, that's a high-risk area to revisit. Speak to the LDS Infotech team about tailoring a workload assessment, application modernization strategy, or cloud architecture workshop to evaluate your architecture's fit for serverless.
Is serverless suitable for all enterprise workloads?
No. It suits unpredictable, event-driven workloads well. Steady, high-throughput workloads are often cheaper and more predictable on traditional infrastructure. A mixed approach, serverless for some workloads and traditional infrastructure for others, is the norm rather than the exception.
How is serverless different from containers?
Containers still need you to manage scaling and the runtime, even with automated orchestration. Serverless removes that layer entirely. You deploy code, not a container image. Teams moving from containers to serverless usually start with the simplest, most isolated function first.
What are the security risks in serverless architecture?
The main risks are over-permissioned functions and exposed event triggers. Each function should have the minimum access it needs, not a broad, shared role. Regular permission audits catch scope creep before it becomes a real exposure.
How do enterprises manage serverless costs?
By setting concurrency limits, monitoring invocation counts, and modelling cost at production scale before launch. Per-execution billing can scale unpredictably without those guardrails. A runaway loop or retry storm can rack up costs fast.
Can serverless and traditional infrastructure coexist?
Yes, and for most enterprises, that's the realistic end state. Steady workloads stay on traditional infrastructure while spiky, event-driven ones move to serverless. Most enterprise cloud estates already look like this, whether or not it was planned that way.
When should you not use serverless computing?
Avoid serverless for long-running, steady-state processes with high baseline traffic, workloads requiring sub-millisecond latency, or legacy systems with complex state requirements that cannot easily migrate to stateless architectures.
Serverless computing lets you run code without provisioning or managing servers, with the provider handling scaling automatically. Both AWS and Azu...
Read Blog