Serverless
Cloud model where the provider fully manages infrastructure and code runs only when needed – billing is per invocation instead of per server.
Serverless computing frees developers from server management. Instead of provisioning, scaling and patching virtual machines, they upload their code – the cloud does the rest. Billing is by actual use: zero requests mean zero cost. This model has fundamentally changed how modern applications are built and operated.
What is Serverless?
Serverless (also Function as a Service / FaaS) is a cloud execution model where the provider fully manages the server infrastructure. Developers write functions that are triggered by events (HTTP requests, file uploads, database changes, schedules). The platform scales automatically from zero to thousands of parallel runs and back to zero. There are still servers – they are just managed by the provider, not the developer. Leading services are AWS Lambda, Google Cloud Functions and Azure Functions. Beyond FaaS, serverless also includes managed services like serverless databases (DynamoDB, Firestore) and event queues.
How does Serverless work?
Developers write individual functions (e.g. in Python, Node.js or Go) and define trigger events. When an event occurs, the platform starts a runtime (container), runs the function and then shuts it down. Under load it starts more instances (horizontal scaling). A cold start happens when no warm instance is available and a new one must start – that adds a short delay, typically 100–500 ms. Warm instances are reused for subsequent requests.
Practical Examples
Image processing: An upload to S3 triggers a Lambda that resizes the image and creates thumbnails – fully automatic and scalable.
REST API backend: API Gateway forwards HTTP requests to Lambda functions that read from DynamoDB and return JSON – no servers to manage.
Real-time data: IoT sensor data flows through Kinesis into Lambda functions that detect anomalies and trigger alerts.
Scheduled jobs: A Cloud Function runs nightly, builds reports from the database and emails them to management.
Chatbot backend: Incoming messages trigger serverless functions that call an AI API and respond on multiple channels.
Typical Use Cases
Event-driven processing: Files, messages or database changes trigger automatic processing
Microservice backend: Individual API endpoints as independent functions with their own scaling
Cron jobs and batch: Regular tasks without permanently running servers
Prototyping and MVPs: Getting a working backend up quickly without infrastructure setup
Webhooks and integrations: Processing incoming webhook data and forwarding to other systems
Advantages and Disadvantages
Advantages
- No server management: No patching, no infrastructure monitoring, no capacity planning
- Pay per use: Only actual executions are billed – ideal for variable workload
- Automatic scaling: From zero to thousands of parallel runs without configuration
- Faster development: Focus on business logic instead of infrastructure
- High availability: Vendors guarantee availability and redundancy
Disadvantages
- Cold starts: First requests after idle time have higher latency (100–500 ms)
- Vendor lock-in: Tight coupling to the provider through proprietary services and config
- Debugging: Distributed functions are harder to debug than a monolith
- Time limits: Functions have maximum runtimes (e.g. 15 minutes on AWS Lambda)
Frequently Asked Questions about Serverless
Is serverless really cheaper than your own server?
Can you run a full application serverless?
How do you handle cold starts?
Related Terms
Want to use Serverless in your project?
We are happy to advise you on Serverless and find the optimal solution for your requirements. Benefit from our experience across over 200 projects.