AWS Lambda vs EC2: when to use which?

The choice between AWS Lambda and Amazon EC2 isn’t just about picking a compute service; it’s a fundamental architectural decision that shapes your application’s cost profile, operational overhead, and scaling behavior. At its core, the debate pits the predictability and control of traditional virtual servers against the ephemeral, event-driven nature of modern serverless computing. Getting this decision wrong can lead to spiraling costs, performance bottlenecks, or a crippling management burden.

The Operational Burden: A Tipping Point

Imagine your team spends 20% of its sprint cycles not on new features, but on patching operating systems, applying security updates, and tuning web server configurations on a fleet of EC2 instances. That’s the hidden, often exhausting, operational tax of Infrastructure-as-a-Service (IaaS). Lambda, in stark contrast, abstracts all of that away. AWS manages the underlying servers, runtime environments, and scaling logic. Your team’s responsibility shrinks to just the application code and its configuration. For startups or teams pushing for rapid innovation, this reduction in undifferentiated heavy lifting isn’t just convenient—it’s a strategic advantage that can accelerate time-to-market by orders of magnitude.

The Cold Start Conundrum

Lambda’s promise of infinite scale has a well-known caveat: cold starts. When a function hasn’t been invoked recently, AWS needs to provision a new execution environment, which can add latency—anywhere from 100 milliseconds to several seconds for complex runtimes like Java or .NET. For a user-facing API endpoint, that initial delay can be jarring. EC2, with its always-on instances, suffers no such penalty. The trade-off is clear. If you’re building a real-time trading platform or a multiplayer game server where consistent, sub-100ms response is non-negotiable, EC2 (or perhaps containers on ECS/Fargate) is your only sane choice. But for an internal data processing pipeline that runs every hour, or an asynchronous task triggered by a file upload, those cold starts are irrelevant. The cost savings and simplicity of Lambda win outright.

Cost Dynamics: Predictability vs. Granularity

Lambda’s pricing model is famously granular: you pay for the number of invocations and the gigabyte-seconds of compute time consumed. It’s the ultimate pay-per-use model. An API that gets 10 million requests a month might cost $20. An idle function costs nothing. EC2, however, bills by the second (or hour) for instance uptime, regardless of whether it’s processing requests at 100% CPU or sitting idle at 2% utilization. This creates a predictable, but often inefficient, cost structure.

The break-even analysis is fascinating. For spiky, unpredictable workloads—like a marketing site hit by a viral post—Lambda’s auto-scaling ensures you never over-provision. But for steady-state, high-throughput workloads, the math flips. A single c5.large EC2 instance running continuously might handle a constant stream of requests for roughly $60/month. To achieve the same throughput with Lambda, you might need thousands of concurrent executions, potentially exceeding the EC2 cost. The sweet spot for Lambda is irregular, bursty traffic. The sweet spot for EC2 is predictable, sustained load.

A Hybrid Reality

The most sophisticated cloud architectures rarely choose one exclusively. They blend them. A common pattern uses EC2 for the core, stateful application monolith or database host, while offloading event-driven side tasks—image thumbnail generation, order confirmation emails, stream processing—to Lambda. This hybrid approach leverages the strengths of each: EC2 provides the durable, always-available backbone, while Lambda acts as a scalable, cost-effective extension for peripheral functions.

So, when do you use which? Choose EC2 when you need full control over the OS, long-running processes, consistent ultra-low latency, or predictable costs for 24/7 workloads. Opt for Lambda when your workload is event-driven, stateless, ephemeral (under 15 minutes), and highly variable in volume. The decision isn’t permanent, either. Start with Lambda for its agility; if you hit its limits around execution time, file system access, or cost, you can always refactor that component to run in a container or on EC2. The cloud’s power lies in having the right tool for the job, not a one-size-fits-all dogma.