Serverless functions cost compared: Lambda vs Azure Functions vs Cloud Functions

The three major clouds offer function-as-a-service with strikingly similar pricing: pay per invocation and per unit of compute consumed. The headline rates differ only modestly, so choosing a provider on function price alone rarely moves the needle. What matters is how efficiently you configure functions and what they cost you downstream.

The shared model

All three charge per invocation and per GB-second of memory-time, with a free tier that covers low-volume workloads. Google separates CPU (GHz-seconds) from memory in its model, and each provider has premium or provisioned tiers for consistent performance, but the core consumption pricing is close across the three.

Where real cost differences come from

Because the rates are close, the differences that matter are configuration and context. Memory sizing (which sets both cost and, on Lambda and others, CPU) determines cost per invocation. Idle warm capacity, provisioned concurrency on Lambda, premium plans on Azure, minimum instances on Google, adds always-on cost if misused. And the surrounding services, the database, queue, or API the function calls, often cost more than the function itself, as covered in serverless cost optimization.

Which is cheapest?

For a given workload the three are usually within a small margin, and the cheapest depends on the exact memory, duration, and volume, not a headline rate. Far more important is running functions on the cloud where their dependencies live, to avoid cross-cloud latency and egress, and configuring them efficiently. Choose the provider your architecture already uses, and optimize configuration, rather than picking a cloud for a marginal function-price difference.

Optimizing on any cloud

The levers are identical across providers: right-size memory to the cost-optimal point, cut duration with efficient code, reduce invocations through batching and caching, limit idle warm capacity, and move to provisioned compute when steady high volume makes per-request pricing more expensive. Because the models match, the same serverless-versus-provisioned crossover applies on all three.

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What to do next

Compare serverless across clouds for your workload. C3X reads your Terraform and prices your resources against a live catalog. Start with the quickstart.