AWS Lambda — Serverless functions
AWS's serverless compute service: run your code in response to events and pay only for the time it actually runs, with no servers to provision or manage.
What is AWS Lambda?
AWS Lambda is AWS's serverless compute service: you upload your code —in Node.js, Python, Java, Go and other languages— and AWS runs it when an event happens, taking full ownership of the infrastructure. There are no servers to provision, patch or scale: when ten requests arrive, Lambda runs ten times; when ten thousand arrive, it scales on its own to handle them; and when there is no traffic, it charges nothing.
The model is event-driven: a Lambda function is triggered by something that happens —a file landing in a storage bucket, a request to an API, a message in a queue, a new record in a database— runs its logic and finishes. That ephemeral nature is precisely its strength: compute appears when needed and disappears when done, so cost follows real usage instead of paying for servers running around the clock.
Lambda is one of the core building blocks of modern serverless and cloud-native architectures. Combined with API Gateway it exposes APIs with no servers behind them; alongside other managed AWS services it lets you build complete backends, automations and integrations that scale on their own and require very little operations. For many workloads —especially those with variable or bursty traffic— it is the most efficient way to run code in the cloud.
Behind its simplicity, the mechanics are straightforward: when an event triggers the function, AWS prepares an isolated execution environment, runs your code and keeps that environment available for a while to reuse it on the next invocations. If many requests arrive at once, it spins up more environments in parallel; if the function had been idle for a while, the first invocation can take a little longer to start —what is known as a cold start— something managed with good design practices when latency is critical.
Versus a traditional server —an EC2 instance— or a container running permanently, the underlying difference is the cost and operating model: with Lambda you pay for the number of invocations and for the time and memory each execution consumes, and when it is not running, it costs nothing. That makes it very efficient for intermittent or unpredictable workloads. For long-running processes, sustained compute or fine-grained control of the environment, containers on Amazon EKS or EC2 instances remain the best option; the key is to choose the tool based on the workload.
What AWS Lambda is used for
Serverless APIs and backends
Expose APIs with API Gateway + Lambda that scale automatically and are billed per use, with no servers to maintain.
Event-driven processing
React to incoming files, queue messages or data changes to process them on the spot, automatically.
Data processing and ETL
Transform files as soon as they land in a bucket, move data between services and feed analytics pipelines with no dedicated infrastructure.
Automation and integrations
Connect systems, trigger tasks and orchestrate flows between services with no dedicated infrastructure.
Scheduled tasks
Run recurring jobs —reports, cleanups, syncs— on a defined schedule, with no server kept on waiting for the time to come.
Variable or bursty workloads
Handle intermittent or seasonal traffic paying only for execution time, with no idle capacity.
AWS Lambda with an AWS partner
At Caleidos we design and operate serverless architectures on Lambda as part of our cloud-native applications practice: we define when Lambda is the best fit and when containers make more sense, integrate it with managed AWS services and instrument it so it scales and runs with minimal operations. The result is code that follows the business without infrastructure or cost becoming a problem. If you are just exploring the approach, we explain in detail what serverless is and how Lambda fits in. It is part of our Cloud Native Apps service.
Explore Cloud Native Apps →Frequently asked questions
- What does it mean that AWS Lambda is serverless?
- It means you do not provision or manage servers: you upload the code and AWS assigns compute capacity when the function runs and releases it when it finishes. You pay for execution time and the memory you use, not for permanently running servers.
- When does Lambda make sense versus a server or a container?
- Lambda shines with event-driven workloads, variable or bursty traffic, automations and integrations, where automatic scaling and pay-per-use are a major advantage. For long-running processes, sustained intensive compute or fine-grained control of the environment, options like containers on EKS or EC2 instances may fit better. We define it based on your workload.
- Does Lambda scale on its own when a lot of traffic arrives?
- Yes. Lambda runs as many copies of your function as the requests arriving in parallel, with no autoscaling to configure, and returns to zero when there is no activity. That automatic scaling is one of the reasons it is so efficient for unpredictable traffic.
- Which languages does AWS Lambda support?
- Lambda natively runs Node.js, Python, Java, Go, C#/.NET and Ruby, and supports virtually any other language through custom runtimes or container images. In practice, you can bring to Lambda the stack your team already works with.
- What is a cold start and how does it affect performance?
- It is the extra time the first invocation takes when AWS prepares a new execution environment, for example after a period of inactivity. For most workloads it is imperceptible; when latency is critical it is mitigated with techniques such as provisioned concurrency and a lightweight function design.
- Is there an execution time limit in Lambda?
- Yes: each invocation can run for up to 15 minutes. For longer tasks or sustained compute another option is better —containers on Amazon EKS, EC2 instances or batch processing services—, and we define it case by case.
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