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Apify and Lambda solve different problems
Apify describes an Actor as a serverless cloud program that accepts structured JSON input, performs work such as web scraping, browser automation or data processing, and can return structured output. Actors can be started manually, through an API or CLI, or on a schedule. The platform also provides storage for datasets, key-value data and files, and lets Actors interact in larger workflows.
AWS Lambda runs functions in response to events or direct calls. Its main value is execution inside the AWS ecosystem: an invocation can be connected to services such as queues, object storage, databases, API endpoints and event buses. You assemble the surrounding storage, browser tooling, proxy network and workflow services yourself or select additional AWS products.
That distinction produces a useful rule:
- Use Apify first when the job is primarily web data collection or browser automation and you want Actors, storage, proxies and workflow composition managed together.
- Use Lambda first when the job is a general-purpose function tightly coupled to AWS events, identity, networking or other AWS services.
- Use both when Lambda handles application events and Apify performs a specialized crawl or browser task.
This is a suitability judgment based on the documented service models, not a head-to-head speed test.
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Side-by-side comparison
| Decision area | Apify | AWS Lambda |
|---|---|---|
| Primary abstraction | Actors that accept JSON, run a task and optionally produce structured output | Functions invoked by events or direct requests |
| Best-known workloads | Web scraping, browser automation and managed data workflows | Event-driven application logic and AWS-integrated services |
| Built-in workflow resources | Actor composition, datasets, key-value stores, files and proxy options | Function execution; surrounding AWS services are configured separately |
| Memory | 128 MB to 32,768 MB in power-of-two values; CPU scales with memory at one core per 4,096 MB | 128 MB to 10,240 MB |
| Ordinary maximum invocation | No single universal maximum Actor duration is established in the cited documentation; check the limits for your configuration | 1 to 900 seconds (15 minutes) |
| Special long-running case | Depends on the Actor and platform configuration | Up to 5,400 seconds (90 minutes) for Lambda Managed Instances invoked asynchronously or through event source mappings, except Amazon MQ and Amazon DocumentDB |
| Temporary disk | Actor and platform configuration determine available storage | /tmp configurable from 512 MB to 10,240 MB per execution environment |
| Main compute unit | 1 GB of allocated memory for one hour equals 1 compute unit (CU) | Requests plus execution duration, measured with configured memory |
| Other billable factors | Proxies, data transfer, storage operations and, for some Store Actors, event or usage pricing | Other AWS services and data transfer can add charges |
When Apify is the better Lambda alternative
Scraping sites that need a real browser
Browser automation often requires Chromium, JavaScript execution, waits for selectors, session state, pagination and anti-bot or proxy handling. An Apify Actor is shaped around this kind of task, and its managed storage can receive the resulting records. Reproducing the same environment with Lambda generally means packaging a browser runtime, managing layers or container images, designing retries and finding a proxy solution.
Scheduled crawls and reusable data pipelines
If a crawl accepts a JSON configuration, writes a dataset and feeds another Actor, Apify’s model maps directly to the workflow. You can start an Actor manually, through an API or CLI, or on a schedule. This can reduce the amount of orchestration code your application owns.
Jobs whose duration and memory do not fit a short function
Lambda’s ordinary invocation ceiling is 15 minutes. A crawl that must process many pages can be split into queue-driven functions, but that introduces coordination, checkpointing and result aggregation. Apify may be more natural when the job is one managed run, although you must still verify the selected Actor’s limits and behavior.
Teams that want platform features rather than infrastructure assembly
Apify packages compute with storage, proxy usage and Actor interaction. That does not eliminate engineering work—you still design inputs, idempotency, parsing and monitoring—but it can move common scraping infrastructure out of your codebase.
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When Lambda remains the stronger choice
AWS-native event processing
For a function triggered by an AWS event and immediately writing to AWS storage or a database, Lambda avoids introducing another platform boundary. IAM, VPC connectivity, CloudWatch integration and existing deployment pipelines may already be standardized in your organization.
Small, short-lived application functions
Image resizing, webhook validation, queue consumers and API handlers often need neither a browser nor a scraping proxy. Lambda’s request-and-duration model can be straightforward for these functions, especially when the rest of the application is already on AWS.
Precise control over runtime packaging
Lambda supports your chosen runtime, memory setting and temporary /tmp allocation. If you need a custom binary, private network path or tightly controlled permissions, evaluate the AWS configuration directly rather than assuming an Actor provides the same controls.
Rank #2
Execution limits that affect a migration
Memory and CPU
Apify lets you select Actor memory from 128 MB through 32,768 MB in powers of two. CPU allocation follows memory, with one core for each 4,096 MB. Lambda offers 128 MB through 10,240 MB, with CPU increasing as memory increases. Benchmark your parser or browser at the actual setting: memory changes can alter both runtime and cost.
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Timeouts and long runs
A standard Lambda function can run from 1 to 900 seconds. AWS documents a 5,400-second exception for Lambda Managed Instances functions invoked asynchronously or through event source mappings, excluding Amazon MQ and Amazon DocumentDB. Do not apply that exception to every Lambda invocation. Apify’s cited resource documentation does not establish one universal Actor maximum duration, so inspect the limit for the Actor, plan and execution mode you intend to use.
Temporary files and browser profiles
Lambda’s /tmp space is configurable from 512 MB to 10,240 MB and belongs to an execution environment. Large downloads, browser caches and generated archives can exhaust it. On Apify, confirm the Actor’s storage behavior and clean up files between stages; platform storage and data-transfer operations can affect the bill.
How the pricing models differ
Apify’s CU model and plan charges
Apify defines one CU as 1 GB of allocated Actor memory running for one hour. Its total charge can also include proxies, data transfer and storage operations. Store Actors may use pay-per-event or pay-per-usage pricing; some event prices include platform usage and others charge it separately, so inspect the individual Actor.
At the time of the cited pricing information, Apify listed these monthly plans and CU rates:
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|---|---|---|
| Free | $0, with $5 to spend | $0.20 |
| Starter | $19/month | $0.20 |
| Scale | $199/month | $0.16 |
| Business | $999/month | $0.13 |
These figures are time-sensitive; verify the current Apify pricing page before committing.
Lambda’s request-and-duration model
Lambda pricing is based on request count and execution duration. The AWS pricing page cited for September 29, 2026 lists a free tier of one million requests and 400,000 GB-seconds per month. Configuration, data transfer and other AWS services can add charges. A free tier is not a workload estimate: account for concurrency, retries, memory, runtime and connected services.
Rank #3
Build a comparable estimate
- Record runs per day or month, average and worst-case duration, allocated memory and concurrency.
- Add retries, failed runs, cold-start-sensitive work and any batch fan-out.
- For Apify, include CU consumption, proxy traffic, data transfer, storage operations and Store Actor event fees.
- For Lambda, include requests, GB-seconds, temporary storage where applicable, data transfer and every AWS service touched.
- Use the same retention period and output volume for both designs.
- Run a representative sample in production-like conditions; do not infer speed or cost from plan names.
No universal cheaper or faster winner is established. The pricing units are different, so a headline monthly plan cannot be compared directly with a Lambda request price.
A practical migration decision process
1. Describe the job as an input and output
Write down the JSON or event that starts the work, the records or files it must produce, and whether the output must be available synchronously. If the input is a URL list and the output is a dataset, an Actor may fit naturally. If the input is an AWS event and the output is a database mutation, Lambda may be simpler.
2. Identify browser, proxy and anti-bot requirements
List JavaScript rendering, login state, cookies, geolocation, proxy rotation, CAPTCHA exposure and pagination. These requirements often dominate implementation effort and should be priced explicitly.
3. Check limits before rewriting code
Measure peak memory, temporary disk, expected run duration and result size. Confirm the selected Apify Actor’s limits or Lambda’s timeout, memory and storage quotas before migrating.
4. Choose the failure and retry model
Make each page or batch idempotent. Persist checkpoints, record failed URLs and distinguish a retriable network error from a permanent parsing error. Neither platform removes the need for this design.
5. Test security and data residency requirements
Review credentials, cookies, authorization headers, private endpoints and retention policies. A managed proxy or storage feature can change your threat model; document who can access inputs and outputs.
Hybrid architectures are often the sensible answer
You do not have to select one platform for every component. A Lambda function can receive an application event, validate it and start an Apify Actor through an API. The Actor can crawl pages and write structured results, while a second Lambda consumer processes the completed dataset. Conversely, an Apify workflow can collect data while AWS handles downstream notifications, analytics or persistence.
Keep the boundary explicit: pass a job identifier, immutable input and output location; make retries safe; and monitor both sides. This arrangement preserves AWS integration where it matters without forcing Lambda to host every browser or scraping concern.
Troubleshooting common migration problems
The Actor costs more than expected
Check allocated memory and runtime first, then proxy traffic, data transfer, storage operations and Store Actor event pricing. A crawl that appears cheap at low volume can become expensive when retries or proxy use multiply requests.
The Lambda function times out
Confirm whether the invocation is subject to the ordinary 900-second limit. Split the workload into bounded batches, use a queue or evaluate an Actor whose execution model fits the crawl. Do not assume the 5,400-second Managed Instances exception applies to your trigger.
Browser pages fail in Lambda
Verify that the browser binary, fonts, sandbox settings, memory and temporary disk are present. Also check outbound networking and proxy requirements. If browser setup is the dominant work, compare the resulting maintenance against an Apify Actor.
Results are duplicated after retries
Use deterministic item keys, checkpoint progress and make writes idempotent. A platform retry can repeat a successful page even when the original response was lost.
Costs cannot be reconciled
Tag each run with input size, memory, duration, retry count and output volume. Reconcile Apify CU and ancillary usage separately from Actor event charges; reconcile Lambda requests and GB-seconds separately from AWS service and transfer charges.
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FAQ
Can Apify run ordinary backend code?
Actors are programmable cloud jobs, so they can perform data processing as well as scraping. The question is whether the Actor platform’s workflow and resource model are a better fit than a function integrated directly with your application.
Does Apify have a 15-minute timeout?
The 15-minute figure is Lambda’s ordinary invocation limit. The cited Apify material does not state one universal Actor duration; check the specific Actor and configuration.
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Usually not. Plan for an input/output adapter, storage changes, authentication updates, deployment changes and a new retry model. Treat migration as a workload redesign unless the code already matches an Actor interface.
Is the Apify Free plan permanently $0?
The cited pricing snapshot listed Free at $0 with $5 to spend, but plan terms and included usage can change. Verify current terms before relying on it.
Frequently Asked Questions
Which service should I choose for a scheduled website crawl?
Start with Apify when the crawl needs browser automation, proxies, managed datasets or Actor composition. Choose Lambda when the crawl is small, custom and already embedded in an AWS event workflow.
Can Lambda and Apify be used together?
Yes. A Lambda function can validate an event and start an Apify Actor, while another function consumes the Actor’s completed dataset.
What is the first cost metric to compare?
Compare a complete run: memory, duration, retries, concurrency, proxy and transfer usage, storage, and downstream service charges—not just CU or Lambda request price.
The Bottom Line
Apify is a credible AWS Lambda alternative for managed web-data and browser workflows, not a universal Lambda replacement. Select it when its Actor, storage and proxy model removes work your team would otherwise build; select Lambda when AWS integration and general-purpose event processing are the priority.
Quick Recap
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