AWS Lambda vs Azure Functions: Choosing the Right Serverless Platform 

Serverless computing has fundamentally changed how we think about building and deploying applications. Gone are the days of provisioning servers, patching operating systems, and worrying about capacity planning. Instead, we can focus on what really matters: writing code that delivers value.

And when it comes to serverless functions, two platforms dominate the conversation: AWS Lambda and Azure Functions.

Both have matured considerably since their launches, and both offer compelling reasons to jump aboard the serverless train. But which one’s right for your organisation? Or perhaps more importantly, do you need to choose just one?

Before we dive into the comparison, let’s clear up what we mean by serverless. Despite the name, servers are definitely still involved—you’re just not managing them anymore. Serverless computing, particularly Function as a Service (FaaS), lets you run code in response to events without thinking about the underlying infrastructure.

You write a function, deploy it, and it runs when triggered. You pay only for what you use, scale automatically, and never have to SSH into a server at 2AM because something’s gone wrong. It’s particularly brilliant for event-driven architectures, microservices, and anywhere you have variable or unpredictable workloads.

And no, serverless doesn’t mean you should migrate everything. Traditional architectures still have their place, especially for long-running processes or applications with predictable, steady workloads. But for many modern use cases, serverless offers a compelling mix of simplicity, scalability, and cost-effectiveness.

What is AWS Lambda?

AWS Lambda burst onto the scene in 2014, essentially creating the serverless functions category. Amazon’s pioneering effort has paid off—Lambda has become the de facto standard for serverless computing, processing trillions of executions monthly across millions of active customers.

Lambda integrates deeply with the AWS ecosystem, triggering from over 200 AWS services and events. It’s the glue that holds many AWS architectures together, whether you’re processing S3 uploads, responding to API Gateway requests, or reacting to DynamoDB streams. The platform supports a wide range of programming languages and offers extensive customisation through layers and extensions.

After nearly a decade of refinement, it’s battle-tested at scales most of us can barely imagine. The ecosystem around Lambda—from frameworks like Serverless and SAM to monitoring tools and third-party integrations—is incredibly rich.

What is Azure Functions?

Microsoft launched Azure Functions in 2016, bringing serverless to the Azure ecosystem. Whilst it might have arrived later to the party, Azure Functions has quickly established itself as a serious contender, particularly for organisations already invested in Microsoft’s cloud.

Azure Functions takes a slightly different approach, offering multiple hosting options from fully serverless consumption plans to dedicated App Service plans. This flexibility lets you start serverless and shift to dedicated resources if needed, all while keeping the same programming model. It integrates nicely with Microsoft’s development tools, making it a natural choice for teams using Visual Studio and Azure DevOps.

The platform does really well in hybrid scenarios, with excellent support for on-premises connectivity through Azure’s hybrid services. If you’re running a mixed cloud and on-premises environment, Azure Functions offers some unique advantages in bridging those worlds.

Language and runtime support

AWS Lambda supports a solid range of runtimes including Node.js, Python, Java, .NET, Go, and Ruby. Each runtime is versioned specifically, and AWS maintains a deprecation schedule that gives you plenty of warning before retirement. Custom runtimes let you bring practically any language to Lambda through Lambda Layers, though this requires more setup and maintenance.

Azure Functions takes a more flexible approach with its runtime stack. Beyond the usual suspects (C#, JavaScript, Python, Java), it offers first-class support for F#, PowerShell, and TypeScript. The Azure Functions runtime itself is open source, which means you can run it anywhere—including your local machine or even in containers.

AWS Lambda vs Azure Functions pricing

AWS Lambda charges based on three factors: the number of requests, the duration of execution (measured in GB-seconds), and any additional provisioned concurrency you might configure. The free tier is generous, and most small to medium applications can run very cost-effectively. Lambda’s pricing is straightforward and predictable, though you’ll need to factor in costs for associated services like API Gateway, which can sometimes exceed the Lambda costs themselves.

Azure Functions offers more pricing flexibility with multiple hosting options. The Consumption plan mirrors Lambda’s pay-per-execution model, charging for executions and GB-seconds. But Azure also offers Premium plans with pre-warmed instances and dedicated App Service plans for predictable billing. This variety means you can optimise costs based on your usage patterns.

Both platforms have free tiers that are perfectly adequate for development and testing. The key difference is that Azure’s variety of hosting options gives you more levers to pull when optimising costs, whilst Lambda’s single model is simpler to understand and predict.

Remember to consider the hidden costs too: data transfer, storage for deployment packages, monitoring, and associated services all add up. The function execution costs are often just a fraction of the total serverless application cost.

Azure Functions vs AWS Lambda performance

Performance nuances like cold starts and execution limits can affect user experience and workload suitability, so choose based on your app’s latency and resource demands.

Cold starts (the delay when a function runs for the first time or after being idle) plague both platforms, though they’re improving. Lambda’s SnapStart has largely solved Java cold starts, whilst Azure’s Premium plan keeps instances warm. or those looking for a middle ground, Azure’s Flex Consumption plan offers a smarter approach than standard Consumption: it scales more dynamically and keeps instances alive longer, reducing cold starts without the full cost of Premium. For execution limits, Lambda wins with 15-minute maximum runtime versus Azure’s 10 minutes (in Consumption mode). Memory-wise, Lambda goes up to 10GB, whilst Azure Consumption stops at 1.5GB (though Premium plans reach 14GB, and the Flex plan can scale similarly).

Both scale really well, just differently: Lambda manages concurrent executions whilst Azure scales based on your hosting plan.

Development experience

Azure Functions arguably offers the superior developer experience, especially if you’re already using Visual Studio or VS Code. The local runtime mirrors production perfectly, and debugging works just like any other application. Lambda development relies more on third-party tools like the Serverless Framework, and local testing can be trickier.

For infrastructure as code, Lambda typically uses CloudFormation or Terraform, whilst Azure Functions naturally pairs with ARM templates or Bicep. And yes, you can use Azure DevOps to deploy AWS Lambda if you fancy mixing things up.

Integration capabilities

Within their respective ecosystems, both platforms integrate extensively.

Lambda can be triggered by virtually any AWS service and has the mature API Gateway for HTTP endpoints. Azure Functions offers similar breadth with its trigger system, but its model reduces boilerplate code significantly. Azure Logic Apps provides a visual alternative for workflow orchestration, giving you a different approach to AWS Step Functions. Lambda’s ecosystem maturity means more third-party services have native integrations, though Azure Functions is quickly closing the gap.

Monitoring and observability

Keeping an eye on serverless workloads is crucial, and both platforms have strong tools in place.

AWS leans on CloudWatch, which tracks metrics, logs, and alarms across your Lambda functions. You can dig into invocation counts, error rates, and duration, and even trace requests end-to-end with X-Ray for a deeper look. Azure takes a similar approach with Application Insights, giving detailed telemetry, performance metrics, and distributed tracing. Its dashboards make spotting slow functions or unusual behaviour straightforward, and integration with Azure Monitor centralizes alerts across your environment.

Both ecosystems give you the tools to see what’s happening under the hood—but the way they present the data, and the depth of tracing can shape how you manage and debug your serverless apps.

Strengths and ideal scenarios

AWS Lambda really shines when you’re building event-driven architectures within AWS or need the vast AWS ecosystem at your disposal.

It’s brilliant for:

• Processing files uploaded to S3
• Building APIs with API Gateway
• Reacting to database changes in DynamoDB
• Implementing microservices architectures
• Running scheduled tasks with EventBridge
• Processing streaming data from Kinesis

The maturity of Lambda means you’ll find solutions, patterns, and frameworks for almost any use case. The community is massive, documentation is extensive, and you’re unlikely to hit a problem that someone hasn’t already solved.

Lambda is definitely a strong choice for organisations already invested in AWS. The tight integration with AWS services means less glue code and fewer authentication headaches. If you’re using AWS for everything else, Lambda is the natural choice for serverless functions.

Limitations to consider

However, Lambda isn’t without its quirks and limitations that might make you think twice. The 15-minute execution limit rules out long-running processes (though AWS Step Functions can help orchestrate longer workflows). The learning curve for the broader AWS ecosystem can be steep; Lambda might be simple, but understanding IAM, VPCs, and the range of associated services takes time.

Cold starts, while getting better recently, can still be problematic for latency-sensitive applications. The lack of built-in development tools means relying on third-party solutions or AWS’s sometimes clunky offerings. And whilst Lambda itself might be cost-effective, the associated services (particularly API Gateway and NAT Gateway for VPC access) can surprise you with their costs.

Strengths and ideal scenarios

Azure Functions excels when you need flexibility in hosting options or you’re working in a Microsoft-centric environment.

It’s particularly well-suited for:

• Hybrid cloud/on-premises scenarios
• Integration with Office 365 and Dynamics
• .NET applications and Windows workloads
• Scenarios requiring long-running functions (with Premium or dedicated plans)
• Teams already using Azure DevOps and Visual Studio

The multiple hosting options give you migration paths as your needs change. Start with Consumption for cost-effectiveness, move to Premium for performance, or shift to Dedicated for predictable costs, all without changing your code.

Azure Functions’ development experience is arguably superior, especially for .NET devs. The tooling feels more polished, debugging is straightforward, and the local development story is stronger. If developer productivity is your primary concern, Azure Functions has some advantages.

Limitations to consider

But Azure Functions has its own set of challenges to consider.

The Consumption plan’s resource limits (1.5GB memory, 10-minute timeout) are more restrictive than Lambda’s. While Premium and Dedicated plans remove these limits, they also remove some of the serverless benefits like true pay-per-use pricing.

The ecosystem, whilst growing rapidly, isn’t as mature as AWS’s. You’ll find fewer third-party integrations, community frameworks, and battle-tested patterns. The documentation, despite being comprehensive, sometimes lacks the depth of real-world examples you’ll find for Lambda.

Azure Functions also has some quirks around scaling and cold starts in Consumption mode that can be less predictable than Lambda. The platform’s relative youth means you’re more likely to encounter edge cases that haven’t been thoroughly documented.

You don’t actually have to choose just one platform, though. Many organisations successfully use both AWS Lambda and Azure Functions, playing to each platform’s strengths.

The hybrid approach makes sense in several scenarios. So, you might be using AWS for your core infrastructure but need Azure Functions for Office 365 integrations. Or you’re primarily an Azure firm but need Lambda for its integration with a specific AWS service.

M&A scenarios are a good one: mergers and acquisitions can result in mixed environments where maintaining both platforms makes more sense than a costly migration.

The key to making this work is treating each platform as a separate domain rather than trying to tightly couple them. Tools like Terraform can help manage infrastructure across both clouds, whilst CI/CD platforms like GitHub Actions or Azure DevOps can deploy to either platform (yes, Azure DevOps can deploy AWS Lambda workflows work quite well).

The complexity of managing both platforms is real. You’ll need expertise in both ecosystems, double the monitoring and security considerations, and careful cost management across providers. But for the right use cases, the benefits of using the best tool for each job outweigh these challenges.

Sometimes, despite your best planning, you need to migrate from one platform to another. That might be for cost savings, better integrations, or organisational requirements.

Start by auditing your functions; not everything needs migrating at once. Focus on independent functions first. The core logic in Python or Node.js often ports directly, but platform-specific code for triggers, environment variables, and service interactions needs refactoring. Consider abstraction layers to ease future migrations.

Map your services carefully: S3 becomes Blob Storage, DynamoDB becomes Cosmos DB, API Gateway becomes API Management or HTTP triggers. Some translations are straightforward; others do need architectural rethinking. Don’t forget the operational bits—monitoring, logging, CI/CD pipelines, and permission models all need reconfiguring too.

While we’ve framed this as Lambda to Azure Functions (fitting our Azure focus), the principles work both ways.

Watch out for common pitfalls: underestimating the effort that goes into integration refactoring, ignoring pricing model differences, and missing edge cases that behave differently across platforms. A phased migration will usually beat a big-bang approach.

So how do you actually make this decision? Asking the right questions will help clarify which direction makes sense.

First, consider your existing ecosystem. Where does the rest of your infrastructure live? Using the same cloud provider for functions usually simplifies operations, reduces latency, and minimises data transfer costs.

Think about your team’s expertise. Do you have AWS or Azure experience already? The learning curve for either platform is significant, so if you have existing knowledge, it’ll make things easier.

Evaluate your integration needs. Which third-party services do you need to connect with? Which platform offers better native integrations? Sometimes the perfect integration can make the decision for you.

Consider your performance requirements. Do you have strict latency requirements that cold starts might ruin? Need long-running functions? Require specific memory or CPU configurations? Match the platform capabilities to your needs.

Don’t forget about compliance and data residency. Both platforms do have extensive compliance certifications, but specific requirements might favour one over the other.

Finally, think about the future. Where is your organisation heading? If you’re planning a broader cloud migration, align your serverless choice with that strategy.

Starting small is usually the wise choice. Build a proof of concept on your preferred platform. Test your assumptions about performance, cost, and developer experience. Both platforms offer free tiers: perfect for experimentation.

AWS Lambda and Azure Functions are both excellent serverless platforms.

The truth is: you probably can’t make a wrong choice here. Both platforms are enterprise-ready, continuously improving, and backed by tech giants committed to serverless computing. The best platform is the one that fits your existing infrastructure, team expertise, and business requirements.

What matters most is taking that first step into serverless. The benefits of reduced operational overhead, automatic scaling, and pay-per-use pricing are too significant to ignore.

Whether you’re Team Lambda, Team Functions, or exploring a hybrid approach, we’re here to help you make the most of serverless. Talk to us today if you’d like to find out more.