Tech Stack Adoption Monitor from Job Posts

Find the companies adopting your category — the week they start hiring for it.

What is the Tech Stack Adoption Monitor?

Job postings are the earliest public evidence of a technology decision. A company that starts requiring Snowflake in its data engineer postings has bought Snowflake. One that stops mentioning it may be leaving.

This Actor reads job posts across company career pages, detects mentions of more than 70 tools, and reports when a company starts or stops requiring one.

Adoption is a buying signal with a date on it

Firmographic databases tell you a company's size. They do not tell you that last Tuesday it started hiring Kubernetes engineers.

Because this Actor diffs against a snapshot in your own account, it reports transitions rather than a static stack: started-requiring and stopped-requiring, with the job posting as evidence and the mention count as strength.

For anyone selling developer tools, that transition is the moment a prospect becomes reachable — they have budget, a project and a gap.

The first run is a baseline: no per-result charge, small sample returned.

What you can use it for

What you want	How to ask for it
Technographics — who runs what stack	technologies: ["Snowflake","dbt"]
Buying signals — the week a company adopts	Read started-requiring changes
Sales prospecting for dev tools	Reach them while the project is live
Churn detection in your category	Read stopped-requiring changes
Category momentum for investors	Count adoptions across a market
Competitive displacement	Watch a rival's tool disappear from postings
Recruiting for a specific stack	Which companies build with your skill
Analyst research on real adoption	Primary evidence, not vendor claims

Typical jobs: technographics, buying signals for dev-tool sales, competitor monitoring, and category adoption research.

What data you get

For each signal: the technology, the company, the change type (started or stopped requiring), how many postings mention it, an example job title and URL as evidence, the category of the technology, and the detection timestamp.

Input

Every field is optional unless marked required.

Field	Type	Description
oneShot	boolean	Return which technologies each company asks for right now, instead of what changed. Use this for a single lookup or from an AI agent — as a monitor, the first run returns nothing because there is no history to compare against yet.
technologies	array	Watch only these, by exact name: Snowflake, Kubernetes, Rust, Databricks, Datadog... Leave empty to watch all 70+. Watching only your own category gives far less noise.
categories	array	Watch every technology in these groups instead of naming them one by one.
monitorKey	string	Keep this the same across runs — it is how the actor remembers what it already saw. Use a different name for an independent watchlist.
trackSignals	array	Leave empty to get all. Adoption is the buying signal; market demand is the trend.
companyTokens	array	Only if you want named companies instead of the built-in index of 872. Accepts "greenhouse:stripe" or a job board URL.
maxCompanies	integer	Keep this stable across runs. Changing it changes which companies are watched and produces a burst of false adoptions.
minMentions	integer	How many of a company's job posts must mention the technology before it counts as adoption. Raise it to 2 to cut noise from one-off mentions.
maxSignals	integer	Safety cap. You are charged per signal returned.
resetBaseline	boolean	Forget everything seen before and take a fresh baseline. The baseline run is never charged.

Input sample

{
  "technologies": [
    "Snowflake",
    "Databricks",
    "dbt"
  ],
  "companyTokens": [
    "greenhouse:stripe",
    "ashby:ramp"
  ]
}

Who uses this

• Developer tool vendors — reach a company the week it adopts your category
• Sales teams — a stack transition is a qualified, dated trigger
• Recruiters — find companies building teams around a specific technology
• Investors — track real adoption of a technology across a market
• Analysts — measure category momentum from primary evidence

Honest limits

These are real and none of them is fixable by any tool. They are here so you know what you are buying before you run it.

• Job posts are a proxy, not proof. A mention means a company is hiring for a skill, which usually but not always means adoption.
• Only companies with public job boards on Greenhouse, Lever, Ashby or Workable are covered.
• The first run is a baseline and returns a sample.
• More than 70 technologies are tracked, weighted toward backend, data and infrastructure. A tool outside that list will not be detected.
• A stopped-requiring signal is weaker than a started one. A company may simply not be hiring for that role right now.

FAQ

How does reading job posts tell you what a company uses?

Because companies list required skills in postings. If the data engineer role now asks for dbt and did not last month, something changed. It is the earliest public evidence of a technology decision, well before a case study or press release.

Which technologies are tracked?

More than 70, across backend, data, infrastructure, observability and frontend — things like Snowflake, Databricks, dbt, Kubernetes, Rust, Terraform and Kafka. You choose which to watch per run.

Why is my first run nearly empty?

It is the baseline. Transitions can only exist relative to a previous state, so real signals begin on the second run.

Can I track a technology that is not on the list?

You can pass your own terms. Very generic words produce noise, so prefer distinctive product names.

How reliable is stopped-requiring?

Less reliable than started-requiring. A company might have paused hiring for that role rather than dropped the technology. Treat it as a hint, not a fact.

How often should I schedule it?

Weekly is usually right. Hiring stacks change on the scale of months, so daily runs mostly find nothing.

Do I need an account or an API key for the source?

No. This Actor only reads public pages and public endpoints, the same ones any logged-out visitor sees. You never give it credentials, and nothing is done on your behalf.

Is it legal to scrape this data?

Scraping publicly available information is broadly accepted as legal, and this Actor only ever touches pages that require no login. That said, what you may do with the data afterwards is a separate question — personal data in particular is regulated by GDPR and CCPA. If you plan to process personal data, take advice first. Apify has a good primer: Is web scraping legal?

How much will a run cost me?

You are charged per result, so the cost scales with what you actually get back. The pricing is shown on this page above the input form. Start with a small run to see real numbers before you scale up.

Can I export to Excel, CSV or Google Sheets?

Yes. Every run's dataset can be downloaded as CSV, Excel, JSON, XML or HTML from the Storage tab, and Apify integrates directly with Google Sheets, Airtable, Zapier, Make and Slack.

Can I run this on a schedule?

Yes. Open the Schedules tab and pick an interval — hourly, daily, weekly. Each run only costs you what it returns.

Can I call it from my own code instead of the console?

Yes. Every Actor on Apify is also an API endpoint. There are official clients for Python and JavaScript, plus a plain REST API. The API tab on this page shows ready-made snippets with your input already filled in.

Something came back empty. Is it broken?

Usually not. An empty result normally means the filters were too narrow, or the source genuinely has nothing to return for that query. This Actor is written to finish successfully and tell you what happened rather than fail — check the run log, it says which step returned nothing.

Related Actors

• ATS Job Monitor — all hiring changes, not just technology mentions
• Company Enrichment — firmographics and contacts for the companies you find
• Company Signals — competitor changes beyond hiring

Where the data comes from

The public JSON endpoints of Greenhouse, Lever, Ashby and Workable. No login, no API key. Snapshots are kept in your own Apify account.