Technographics: what companies run, and what they are hiring to build.
What technographic data is, what it is used for, where it comes from, and the part most guides leave out: what job postings reveal about a company’s stack that no website scan can see. With the shares to prove it, a free live scan and an API.
What is technographic data?
Technographic data is information about the technologies a company uses: its frontend framework, analytics, CDN, payment processor, CRM, cloud platform, databases and every other tool it runs. It is the technology counterpart of firmographic data, and sales, marketing and recruiting teams use it to segment, score and target companies by their stack.
The term is written “technographics” and “technographic data” interchangeably. The question it answers is simple: what does this company run, and what does that say about whether it is a fit, a prospect, a competitor’s customer about to churn, or an employer worth approaching.
On its own, a list of technologies is trivia. The value comes from acting on it: segmenting accounts by stack fit, scoring leads, triggering outreach when a tool appears or disappears, and sizing markets by adoption. The rest of this guide covers where the data comes from and how the sources differ, then puts numbers on the one source most guides skip.
| Firmographic | Technographic | |
|---|---|---|
| Describes | The company itself | The technology the company runs |
| Example fields | Industry, headcount, revenue, location, founding year | CMS, analytics, CRM, cloud platform, database, data warehouse |
| Answers | Who is this account? | What does this account run? |
| Typical source | Company registries, filings, business databases | Website scans, job postings, surveys |
| Use in scoring | Defines the ICP boundary | Refines fit by stack and surfaces displacement targets |
A useful way to hold it: firmographics draw the boundary of your addressable market, technographics tell you which accounts inside that boundary are the best fit, and a momentum signal such as hiring tells you which of them are in motion right now. The glossary entries on technographics and firmographic vs technographic data cover the terminology in more depth.
What are technographics used for?
Technographics are used to find, score and prioritise companies by the technology they run. Sales teams build prospect lists by installed tool, marketing teams segment accounts by stack fit, competitive-intelligence teams track adoption and migrations, and recruiters find which companies hire for a given stack. Product teams use the same data to decide which integrations to build.
Sales and prospecting
Build target lists filtered by the technology a prospect already runs. A vendor that integrates with Shopify can isolate every Shopify store; a security vendor can find companies hiring for a competitor's tool and pitch displacement.
- Filter lead lists by installed or hired-for technology rather than firmographics alone.
- Trigger outreach when a prospect starts hiring for a complementary or competing tool.
- Personalise messaging with a verifiable detail about the prospect's stack.
Account-based marketing
Technographics sharpen account scoring and segmentation. Accounts running the technologies your product complements score higher; accounts on a competitor's platform become a displacement segment.
- Score accounts by stack fit, not only by company size and industry.
- Build technology-based segments for tailored ad and content plays.
- Pair with hiring signals so the right account hears the right message when it is in motion.
Competitive intelligence
Track which companies run your product versus a competitor's, watch for migrations, and size a market by adoption. A company hiring for a competitor's platform is a loss signal; one hiring to migrate off it is a win signal.
- Monitor install bases for you and your competitors.
- Detect platform migrations from the hiring that precedes them.
- Size an addressable market by the share of companies that name a technology.
Recruiting and talent intelligence
The same data answers the recruiter's question from the other side: which companies hire for a given stack, in which cities, at which seniority, and how a candidate's skills map to live demand.
- Find every company hiring for a specific language or platform.
- Benchmark a stack's demand by industry, country and company size.
- Feed sourcing tools with the technologies a company actually staffs for.
Where does technographic data come from?
Technographic data comes from three sources: scanning a company’s public website and DNS records for technology fingerprints, parsing the technologies named in its job postings, and surveys or self-reported data from companies and vendors. Each sees a different slice of the stack, and no single source is complete, so serious programs combine at least the first two.
| Source | How it works | What it sees | What it misses | Freshness |
|---|---|---|---|---|
| Website scan | Fetch a domain's public pages and match scripts, headers, cookies, meta tags and asset URLs against a library of technology signatures. DNS and certificate records extend it to email, CDN and security vendors. | Frontend frameworks, analytics, tag managers, CDNs, payment widgets, chat and support tools, CMS and ecommerce platforms, and any SaaS that injects client-side code. | Everything behind login or inside the company's own infrastructure: databases, warehouses, the CRM, the ERP, cloud accounts, internal languages and tooling. | Current as of the scan. Says nothing about direction: a tool being adopted and a tool being retired look identical on the day. |
| Job postings | Parse the technologies named in a company's open roles. Each posting is matched against a lexicon of technologies and skills, so the company's hiring becomes a list of what its teams work with. | Backend languages, databases, cloud platforms, data warehouses, CRMs, ERPs, BI tools, observability and security stacks, and the internal tooling that never touches a public page. | Companies that are not hiring, and technologies a company runs but never staffs for. A posting can also name a tool the team is leaving, or one it merely wants candidates to know. | Forward-looking: postings describe what a team is building next, and each carries a posting date. Coverage is limited to the boards and ATSs collected. |
| Surveys and self-reported data | Ask companies, or the vendors selling to them, which products they run. Analyst panels, review sites, case studies and vendor logo walls all belong here. | Whatever the respondent chooses to disclose, including contract details a scan or posting would never show. | Everything nobody disclosed. Coverage is uneven, favours large accounts and marquee vendors, and cannot be verified from outside. | Ages quickly and silently: a logo stays on a vendor's page long after the customer churns. |
Website scans, step by step
Fingerprinting is the workhorse of the category and the engine behind every “what does this site run” tool. A scanner requests a domain’s public pages and inspects everything the server returns:
- Script and asset URLs: a request to a known analytics or tag-manager domain is a direct fingerprint.
- HTTP response headers: server software, CDN edge headers and security tooling leave identifiable traces.
- HTML markup and meta tags: generator tags, framework-specific attributes and CMS signatures.
- Cookies and global JavaScript variables: many SaaS tools set named cookies or window globals.
Because the evidence is in the page itself, frontend detection is highly accurate. The limitation is structural: anything that does not touch the public site leaves nothing to find. You can watch it happen on any domain with the free tech-stack scanner.
Job postings, step by step
A posting is a company describing, in its own words, what a new hire will work with. JobsPipe collects postings from job boards and applicant tracking systems, matches each title and description against a curated lexicon of technologies and skills, and stores the matches on the job as technology_slugs. Group the results by company and you have a list of who is hiring for any technology; split them by industry, country and company size and you have the shape of that technology’s market.
The honest caveat: it is a hiring signal, not a purchase record. A posting can name a tool the team is migrating away from, or one it only wants candidates to know. Read the data as “companies investing hiring effort in this technology” and it is reliable; read it as a customer list and it is not.
What job postings reveal that websites do not
Job postings reveal the internal stack: the cloud platform, databases, data warehouse, CRM, ERP, languages and observability tools that run behind login and leave no trace on a public website. They also carry context a scan never has: which team uses the tool, at what seniority, in which industry and company size, and when the company started hiring for it.
The chart below is the share of active postings, across the ten boards JobsPipe collects, that name each of the tracked technologies. Most of the list cannot be detected from a homepage at all.
- Python4.7%
- AWS3.1%
- SAP2.9%
- Microsoft Azure2.8%
- Salesforce1.9%
- Power BI1.8%
- Java1.7%
- Google Cloud1.7%
- Kubernetes1.5%
- React1.3%
- Docker1.3%
- Jira1.2%
- TypeScript0.9%
- Terraform0.9%
- Tableau0.9%
Kubernetes: who hires for it
Within active postings that name Kubernetes, the split by employer size and by employer industry. Full company list at companies using Kubernetes.
- 1000+ employees25.7%
- 201-1000 employees7.5%
- 51-200 employees4.6%
- 1-50 employees4.5%
- Software and IT services23.8%
- Financial services7.0%
- Employment and staffing5.3%
- Electronics manufacturing2.2%
- Telecommunications1.8%
- Other transport equipment1.6%
Salesforce: who hires for it
Within active postings that name Salesforce, the split by employer size and by employer industry. Full company list at companies using Salesforce.
- 1000+ employees25.2%
- 201-1000 employees7.3%
- 51-200 employees3.6%
- 1-50 employees3.1%
- Software and IT services17.9%
- Financial services5.1%
- Employment and staffing3.9%
- Healthcare2.0%
- Electronics manufacturing2.0%
- Machinery1.8%
Snowflake: who hires for it
Within active postings that name Snowflake, the split by employer size and by employer industry. Full company list at companies using Snowflake.
- 1000+ employees27.4%
- 201-1000 employees7.4%
- 51-200 employees4.1%
- 1-50 employees3.1%
- Software and IT services21.2%
- Financial services9.1%
- Employment and staffing5.4%
- Healthcare2.2%
- Head offices and consulting1.8%
- Information services1.4%
Kubernetes hiring concentrates in software and it services (23.8% of its postings) and in employers with 1000+ employees (25.7% where size is known). Salesforce hiring concentrates in software and it services (17.9% of its postings) and in employers with 1000+ employees (25.2% where size is known). Snowflake hiring concentrates in software and it services (21.2% of its postings) and in employers with 1000+ employees (27.4% where size is known). None of that is visible from a homepage, and all of it changes which accounts a vendor of the technology, or a recruiter staffing for it, should call first.
How to get technographic data
Run a free live scan for a single domain, use the two API endpoints for anything programmatic, and buy an enterprise dataset only when the gap is contract-level depth rather than coverage. The scan and the jobs search share one key and one free tier.
For pre-call research or a one-off competitive check, paste the domain into the free tech-stack scanner. It fetches the public site and returns the detected technologies with categories and confidence scores. No signup, no card.
Scan a domain freePOST /v1/stack/scan takes a domain and returns the technologies it serves as JSON: slug, name, categories, confidence and the signals that fired. Results are cached, so enriching a list is cheap on repeat calls.
curl -X POST https://api.jobspipe.dev/v1/stack/scan \
-H "Authorization: Bearer $JOBSPIPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"domain": "stripe.com", "mode": "auto"}'POST /v1/jobs/search with skills_or returns every active posting naming a technology, with the company on each record. Add company_name_or to profile one company, isic_division_or or job_country_code_or to cut by industry or country, and discovered_at_gte to catch companies that just started hiring for it. The same tools are exposed to AI agents through the MCP server.
curl -X POST https://api.jobspipe.dev/v1/jobs/search \
-H "Authorization: Bearer $JOBSPIPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"skills_or": ["snowflake", "databricks"], "status": "active", "posted_at_max_age_days": 30, "limit": 50}'If you need install-base estimates, IT-spend figures and contract-level detail on sales-led terms, the enterprise tech-intel providers fill that gap. See the technographic data providers comparison, and the BuiltWith alternatives and Wappalyzer alternatives round-ups for where each scanner fits.
FAQ
What is technographic data?+
Technographic data is information about the technologies a company uses: its frontend frameworks, analytics, CDN, payment processors, CRM, cloud platform, databases and the rest of its stack. It is the technology equivalent of firmographic data (company size, industry, location). Sales, marketing, competitive-intelligence and recruiting teams use it to segment, score and target accounts by the tools they run.
What are technographics used for?+
Four jobs cover most of it: sales prospecting (lists filtered by installed or hired-for technology), account-based marketing (scoring and segmenting accounts by stack fit), competitive intelligence (tracking adoption, migrations and market size) and recruiting (finding which companies hire for a given stack). Product and partnership teams also use it to prioritise integrations by real demand.
Where does technographic data come from?+
Three sources. Website scans fingerprint a domain's public pages and DNS records. Job postings name the technologies a company's teams work with, including backend and internal systems. Surveys and self-reported data cover whatever companies or vendors disclose. Each sees a different slice of the stack: scans see the public surface, postings see the internal stack and its direction, surveys see contract detail nobody else can, with uneven coverage.
What do job postings reveal that a website scan does not?+
The internal stack. Kubernetes, Snowflake, Salesforce, SAP, Python, PostgreSQL, Datadog and every other tool that runs behind login or inside a company's cloud leave no fingerprint on a public website. Job postings name them, and add context a scan lacks: which teams use the tool, at what seniority, in which industries and company sizes, and whether the company is hiring into it or away from it.
What is the difference between firmographic and technographic data?+
Firmographic data describes the company itself: size, industry, revenue, headcount, location. Technographic data describes what the company runs: its CMS, analytics, CRM, cloud platform and other tools. Firmographics answer 'who is this account'; technographics answer 'what does this account run'. Most account-scoring models blend both, then add a momentum signal such as hiring activity.
Is technographic data accurate?+
It depends on the source and the claim. A live website scan is highly accurate for client-side tools because the evidence is in the page. Job-posting technographics are accurate as a record of what a company is hiring for, which is not the same as a purchase record: a posting can name a tool the team is leaving. Survey and vendor data are only as accurate as the disclosure. The reliable approach is to treat each source as evidence for the kind of claim it can support.
How do I get technographic data for a single company?+
Run a free live scan of the domain at jobspipe.dev/stack for what its public website serves, then search its job postings with company_name_or on the JobsPipe API for the internal stack. The two together cover what the company runs today and what it is building next. No signup is needed for the scan; the jobs API has a free tier.
How do I get technographic data at scale?+
Use an API. POST /v1/stack/scan returns a domain's detected technologies as JSON with categories and confidence scores. POST /v1/jobs/search with skills_or returns every active posting naming a technology, with the company on each record, so grouping by company produces the list of who is hiring for it. Both run on the same key, and the MCP server exposes them to AI agents as tools.
How is technographic data different from intent data?+
Technographic data is a current-state signal: what a company runs. Intent data is a momentum signal: whether an account is moving toward a purchase. They are complementary. Technographics tell you an account fits your ICP by stack; hiring-signal intent tells you when that account is in motion. Job postings are unusual in carrying both at once, because a posting names the stack and dates the investment.
Both halves of technographics on one key: scan the public site, search the postings. Free tier included.
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