What is Serverless Computing?
Serverless Computing is a cloud execution model where the provider automatically manages server infrastructure, scaling, and availability — developers deploy code as functions that run on-demand, and are billed only for actual execution time rather than reserved capacity.
Serverless Computing: Full Explanation
Despite the name, serverless computing still uses servers — you just don't see, manage, or pay for them when they're idle. The cloud provider handles all infrastructure management: provisioning, scaling, patching, and availability. You provide a function (a piece of code) and a trigger (an HTTP request, a file upload, a schedule), and the provider runs it on demand.
The key economic model is event-driven billing: you pay per request and per millisecond of execution, not for a server running 24/7. For workloads with variable or unpredictable traffic, this can reduce costs by 90%+ compared to provisioning dedicated servers.
The major serverless offerings are AWS Lambda, Azure Functions, and Google Cloud Functions. All three follow the same model: write a function in your preferred language (Python, Node.js, Java, etc.), deploy it, and it runs automatically when triggered.
Key Facts About Serverless Computing
- ✓Serverless eliminates server management — no OS patching, capacity planning, or scaling configuration.
- ✓Billing is per-invocation and per-execution-millisecond, not per-hour of server uptime.
- ✓Cold start latency (50ms–3s on first invocation) is the main trade-off vs always-on servers.
- ✓Ideal for: API backends, event-driven processing, scheduled tasks, and AI inference wrappers.
- ✓AWS Lambda, Azure Functions, and GCP Cloud Functions are the three major platforms.
- ✓Serverless is increasingly used to wrap LLM API calls — deploy a function that calls OpenAI/Anthropic and returns responses to your application.
Real-World Example: IT Services
A startup built an AI-powered document processing service on AWS Lambda. When a PDF is uploaded to S3, a Lambda function triggers, sends the document to Claude for structured data extraction, and stores the result in DynamoDB. The entire pipeline costs under ₹2 per 1,000 documents processed — with no server management overhead. The same architecture using EC2 instances would require 3 managed servers and significant DevOps overhead.
Frequently Asked Questions
Is serverless always cheaper than using virtual machines?
For variable or low-traffic workloads, serverless is almost always cheaper. For high-traffic, consistently utilised workloads, reserved EC2/VM instances can be cheaper. The break-even point varies by workload — most cloud providers offer cost calculators to compare.
What is a cold start in serverless?
A cold start occurs when a function that has been idle is invoked — the provider must initialise the execution environment, which adds 50ms to 3 seconds of latency. Functions that are invoked frequently stay "warm" and avoid cold starts. For latency-sensitive applications, provisioned concurrency (keeping functions pre-warmed) is available at extra cost.
Can I run AI models directly in serverless functions?
Small ML models can run directly in Lambda/Functions (within memory and timeout limits). For large language models or GPU-intensive inference, serverless functions are better used as API wrappers that call managed AI services (AWS Bedrock, Azure OpenAI, Vertex AI) rather than running models directly.