In this guide
- Stan’s take: why Apify is essential in our agency stack
- The use cases and features that matter to us
- A practical play: turn relevant engagement into research
- Actors, tasks, runs and datasets explained
- Connect runs to your workflow without losing results
- Apify pricing: platform costs and Actor costs
- Apify versus Firecrawl and a contact database
- Failure modes and acceptance checks
- Apify implementation with frontBrick
- Apify buyer questions
Stan’s take: why Apify is essential in our agency stack
Apify is one of the tools I consider essential for a modern GTM agency. We use it for social listening, LinkedIn profile and comment research, advertising libraries and many other source-specific research tasks that feed campaigns.
The strength is having a broad catalog of Actors in one place. When we need data from a particular source, we can look for a maintained Actor instead of building a new scraper from zero. The right Actor gives us information that a conventional contact database may never contain.
It also fits the way we build with agents. Through Apify’s supported AI integrations, Claude, ChatGPT or another connected agent can use Actors as part of a larger workflow. That makes advanced research practical, but choosing the right source and explaining why it matters to the campaign still comes first.
The use cases and features that matter to us
| Use case | Feature | Why we use it |
|---|---|---|
| Find relevant conversations | Social listening Actors | Identify topics and discussions that reveal useful campaign context. |
| Research LinkedIn activity | Profile, post and comment Actors | Understand an audience and the content it engages with, subject to each Actor’s access and coverage. |
| Study an account’s advertising | Ad-library Actors | Use current positioning and campaign themes as research inputs. |
| Collect source-specific prospects | Apify Store catalog | Find a purpose-built collector for the source your market actually uses. |
| Add web data to an agent | MCP, API and AI integrations | Let the agent run an appropriate Actor and use its results in the broader GTM workflow. |
Feature references: Apify AI integrations; Apify MCP server.
A practical play: turn relevant engagement into research
Here is a practical way to apply that approach to your own team:
- Choose a topic, post or public source closely related to the problem your offer solves.
- Use an appropriate Actor to collect the relevant activity and people, then inspect the result.
- Match the audience to your ideal customer profile and enrich only the accepted prospects.
- Build the outreach around the business context, rather than treating every comment as buying intent.
Actors, tasks, runs and datasets explained
An Actor is the program. A task saves its configuration so the same setup can be reused. A run is one execution of that configuration. Its dataset contains structured output when the Actor writes results there. Those distinctions matter when automating research.
For example, “collect UK commercial maintenance businesses” is a business request. The implementation might use a particular directory Actor, a saved input defining the location and query, a scheduled run, and a dataset that is normalized before CRM import.
Do not assume all Actors accept the same input fields or return the same schema. Read the Actor's README and input definition. Check recent maintenance, documented limits, pricing and a small sample output before building anything around it. The Store documentation explains why each Actor needs its own evaluation.
Connect runs to your workflow without losing results
Apify documents both synchronous and asynchronous API patterns. A short synchronous run can return results directly, but the request has a time limit. For longer work, start the run, store its ID, wait for completion through polling or an appropriate webhook, then retrieve the dataset. The official API guide provides both approaches.
A robust sequence is: create run, persist run ID, wait, verify terminal status, fetch all required dataset pages, validate the output, then commit accepted records. A network timeout should not automatically start a second run. First check whether the original run is still working or has already finished.
Record the Actor and configuration version used. When a source changes its website or an Actor changes its output, compare the new sample before allowing automatic CRM writes. A technically successful run can still return an empty or changed dataset.
Apify pricing: platform costs and Actor costs
Reviewed 18 October 2026: Apify offers $5 of monthly usage on Free. Paid monthly plans start at $19 for Starter, $199 for Scale and $999 for Business, with usage-based charges beyond included allowances.
Actor billing varies. Pay-per-event Actors charge for defined events such as returned results or starts. Most include platform usage, but some charge it separately. Pay-per-usage Actors consume platform resources. Read the selected Actor's pricing before estimating cost. Older rental-model explanations may be outdated following the October 2026 transition.
For compute-based usage, one compute unit represents 1 GB of memory for one hour. Starter lists $0.20 per compute unit. A run using 2 GB for half an hour therefore consumes one compute unit, before any other charges. Proxy, storage, transfer and Actor-specific fees can change the total.
Set result limits and supported charge caps before scaling. Budget by accepted, unique accounts, not raw rows. If half a dataset is duplicate locations or wrong-fit businesses, the cost of useful output is higher than the advertised price per result.
Apify versus Firecrawl and a contact database
| Task | Starting point | Reason |
|---|---|---|
| Collect structured data from a specific platform | Apify | A suitable maintained Actor may handle source-specific behavior |
| Read and structure known company websites | Firecrawl | Its web-content workflow can be simpler for this scope |
| Find verified details for an identified buyer | Prospeo or another contact provider | Scraped public information is not the same as verified contact enrichment |
| Combine discovery, qualification and routing | Clay or n8n | Orchestration coordinates the data sources and business rules |
These are starting points, not universal rankings. Compare a small real workload and the maintenance required. A cheaper scraper can become expensive when someone spends hours repairing its output.
Failure modes and acceptance checks
- Empty output: distinguish no matching businesses from blocking, a changed page or incorrect input.
- Partial run: inspect status and logs before treating a smaller dataset as complete.
- Schema changes: validate required fields and types before importing anything.
- Repeated runs: deduplicate by source identifier and business entity, not row number.
- Cost growth: compare billed usage with accepted records and alert on unusual ratios.
- Source restrictions: use an appropriate access method and respect the site's terms and the data's intended use.
Track collection success, accepted-account rate, duplicate rate, missing-domain rate, cost per accepted account and downstream qualified conversations. Keep technical completion separate from commercial usefulness.
Apify implementation with frontBrick
frontBrick can connect source selection to the GTM outcome: Actor evaluation, configuration, account qualification, enrichment, CRM mapping and campaign staging. A handover should include the source contract, saved settings, field dictionary, monitoring, budget controls and a recovery procedure for failed runs.
Discuss your research workflow with Stan. Discovery is complimentary and implementation pricing is custom.
Apify buyer questions
Is Apify a lead database?
It is primarily a platform for running collection and automation tools. Some Actors provide prospecting data, but their sources, quality and pricing differ. Evaluate the individual Actor.
Can I use Apify without writing code?
Many Actors have configurable forms and downloadable results. Recurring production workflows still need someone to own input quality, mapping, exceptions and the destination system.
Does the subscription cover every Actor?
No single pricing assumption covers all Actors. Check event or usage charges, what platform resources are included and whether downstream dataset operations add cost.
What happens if the API request times out?
Check the stored run ID before starting again. The run may still be executing. An asynchronous design separates job execution from the connection that requested it.
Are scraped emails ready for outreach?
Not automatically. Confirm identity, relevance and contact quality, apply exclusions, and use an appropriate sending process. A visible address is not a qualification decision.
What should we test before scaling?
Test source coverage, required fields, duplicates, pagination, partial failures, cost and CRM reconciliation. Approve the useful records and recovery behavior before increasing volume.
