Serverless functions still run on managed servers and operating-system processes. The platform provisions and places that compute, routes each invocation to a suitable execution environment, initializes one when needed, runs the handler, and may reuse or discard the environment afterward. “Serverless” means you do not manage the server fleet—not that compute or processes disappear.
What happens when a serverless function is invoked?
An invocation begins when a trigger delivers work to a managed service. The trigger might be an HTTP request, scheduled event, queue message, or change in a storage or analytics service. Google Cloud’s Cloud Run functions architecture blueprint, for example, describes Cloud Storage events, scheduled intervals, and BigQuery changes, with Pub/Sub or Eventarc routing events to functions.
- A trigger reaches the service. The service receives a request or event and identifies the function and configuration that should handle it.
- The platform selects capacity. It may route the invocation to an initialized environment that can be reused. If suitable capacity is unavailable, it prepares or starts an execution environment. The routing and capacity decisions are provider-specific and are generally hidden behind the service interface.
- Any required initialization runs. A new environment may need to fetch code or an image, start its isolation boundary and language runtime, load dependencies, and run application initialization. AWS Lambda documents downloading the code, starting the execution environment, and running initialization code before the handler.
- The handler processes the event or request. The function can perform application logic and call other services. A serverless application may combine managed services with custom function code rather than relying on one function to do everything.
- The platform decides what happens next. It may retain or freeze the environment in anticipation of another invocation, or eventually terminate it. Reuse can avoid repeating some setup, but it is not a promise that the same environment will remain available.
This is a conceptual lifecycle, not a universal recipe for how every service works internally. A platform does not necessarily create a new container for every request, download code every time, or use the same isolation boundary across all its runtimes.
What is a cold start, and what makes a start warm?
Cold start: setup before the handler
A cold start is the additional setup latency when the service must prepare an execution environment before it can run the function. Depending on the service, that work can involve starting a runtime or isolation boundary, loading libraries, and executing initialization code. The handler has not yet begun its work while this preparation is happening.
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AWS Lambda’s current lifecycle documentation characterizes cold starts as occurring in under 1% of invocations typically, with duration ranging from under 100 milliseconds to over 1 second. Those figures are AWS’s general guidance for Lambda, not a guarantee for every function or a comparison with another provider. AWS identifies initialization and dependency loading as contributors and recommends Provisioned Concurrency when predictable Lambda start times are needed.
Warm start: reuse, not permanence
A warm start uses an already initialized environment, avoiding some setup that would otherwise precede the handler. AWS Lambda says an environment may be retained for some time in anticipation of another invocation and frozen after an invocation. Lambda also says environments are terminated every few hours for updates and maintenance, including environments serving continuously invoked functions. A warm environment is therefore a performance opportunity, not a lifecycle contract.
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Why startup times differ
- Initialization and dependencies: more work before the handler, including loading libraries, can add preparation time.
- Images and entrypoints: for container-based services, image size and work performed by the entrypoint can affect startup. Cloudflare says its Containers cold starts can often take 1–3 seconds, depending on image size and code execution time. That is a Cloudflare-specific estimate, not directly comparable to AWS Lambda’s figures.
- Environment availability: an invocation routed to a reusable or provisioned environment follows a different startup path from one that needs a new environment.
- Platform design and workload: the runtime, execution boundary, resource placement, and invocation pattern all matter. The cited provider documentation does not establish a universal ranking or a single latency figure applicable across services.
Cold-start figures should be read with their service and scope attached. The AWS and Cloudflare estimates above describe different services and conditions; without aligned runtimes, workloads, regions, and measurement definitions, they are not a controlled comparison.
What execution boundary does a platform use?
“Serverless” describes an operating model, not one kind of process or isolation technology. Functions may run in containers, lightweight language-runtime isolates, or virtualized environments such as microVMs. These approaches differ in how they start, what code they can run, how they isolate work, and how they handle reuse and state.
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| Model | Execution and startup | Flexibility and state considerations | Documented example |
|---|---|---|---|
| Container | Packages an application with its runtime dependencies. Starting the container and loading libraries can add setup latency; reuse can avoid repeating some setup. | Can package more than a single language-level handler, but the concurrency policy and lifecycle depend on the platform. An idle warm container can occupy resources. | A 2018 USENIX ATC systems paper describes container-per-function mapping as a common FaaS approach among systems of that period. Cloudflare documents Linux containers running inside Firecracker microVMs for Cloudflare Containers. |
| V8 isolate | A lightweight context runs within an existing Workers runtime; one runtime instance can run many isolates, each with isolated memory. It avoids creating a VM for every function in the model Cloudflare describes. | Designed around the supported Workers runtime and APIs rather than an arbitrary Linux process. Cloudflare says isolates can be evicted, so their memory is not durable storage. | Cloudflare Workers documentation describes this model. Cloudflare says an isolate can start around a hundred times faster than a Node process on a container or VM; that is Cloudflare’s own comparison, not an independent benchmark. |
| MicroVM | Uses hardware-virtualized environments. AWS describes Lambda MicroVMs created from an image that is initialized and snapshotted; subsequent MicroVMs can start from the snapshot with dependencies already loaded. | Offers a distinct virtualized execution boundary. The AWS snapshot-based model described is aimed at isolated stateful sessions and jobs, rather than being a statement that every ordinary Lambda invocation follows this path. | AWS describes Firecracker-based Lambda MicroVMs. Cloudflare separately documents Containers instances running in Firecracker microVMs, each with its own kernel and network. |
The 2018 USENIX paper is useful for understanding foundational FaaS trade-offs, not as a current inventory of every provider’s implementation. Its survey found differing concurrency policies: some platforms studied allowed one execution at a time per container, while others permitted concurrent executions. Do not assume that behavior transfers to a particular current service without checking its documentation.
Cloudflare’s Workers documentation says: “Each isolate’s memory is completely isolated, so each piece of code is protected from other untrusted or user-written code on the runtime.” This is Cloudflare’s description of its Workers model, not a blanket security guarantee for all isolates or serverless platforms.
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Can a function keep state between invocations?
Sometimes an execution environment is reused, so data held in process memory or temporary local storage can remain available to a later invocation in that same environment. AWS Lambda documents that objects declared outside the handler can remain initialized across reused invocations, and that files in /tmp persist while the environment is frozen. But the platform can terminate the environment, and a later invocation may run somewhere else or in a newly initialized environment.
- Suitable for opportunistic state: caches, reusable clients, or other initialization that saves work when present and can be rebuilt when absent.
- Unsuitable for durable application state: user records, workflow progress, or any data the application must reliably retrieve later. Store that state in an explicit database, object store, or other persistent service.
- Check concurrency behavior: do not assume that invocations are always serialized or that a given environment handles only one request at a time. Verify the service’s concurrency and isolation behavior before relying on mutable in-memory state.
Cloudflare likewise says Workers isolates can be evicted and are not necessarily long-lived. The practical design rule is the same: treat in-memory state as disposable unless the service explicitly documents a stronger guarantee.
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What should you check when choosing an execution model?
There is no universal winner. Choose based on the workload and on the behavior the specific service documents, rather than on the word “serverless.”
- Latency target: determine whether a cold start is acceptable, whether startup work can be reduced, and whether the provider offers provisioned or prewarmed capacity with suitable controls.
- Runtime requirements: check whether the code needs a particular language runtime, native dependencies, or the ability to run arbitrary Linux processes. An isolate model and a container model do not offer the same execution surface.
- Isolation requirements: identify whether the documented boundary is a runtime isolate, container, or microVM, and evaluate the provider’s actual security documentation for the application’s needs.
- Concurrency and state lifetime: verify how simultaneous invocations are handled and what, if anything, persists between them. Keep durable data in a persistent service.
- Event routing and operations: map how requests and events reach the function, what network access it needs, and how permissions, secrets, logs, and monitoring are configured. Google’s Cloud Run functions blueprint illustrates an architecture using Eventarc or Pub/Sub, VPC networks, firewall rules, Secret Manager, IAM, Cloud Logging, and Cloud Monitoring.
- Workload and cost: distinguish bursty short invocations from long-running interactive sessions or jobs. Confirm current service limits and pricing for the relevant region and configuration; the cited material does not establish comparable current prices or limits.
The platform owns much of the underlying fleet and placement work, but the application still needs deliberate choices about triggers, initialization, state, permissions, networking, and observability. AWS’s Lambda application-design guidance specifically notes that customers do not directly control Lambda’s operating systems, hypervisors, hardware, placement, or scaling decisions.
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