Every week, somewhere between 50 and 100 acquisitions get announced. Most of them barely make a splash — a press release on a company blog, a short item on an industry newsletter, a LinkedIn post from the CEO. Then they disappear into the noise.
That is a shame, because an acquisition is rarely just an acquisition. For the sales rep, it is a trigger: the acquiring company just got budget approval and is about to consolidate vendors. For the investor, it is a signal: this sector is heating up, and the buyer just revealed its strategy. For the analyst, it is data: deal sizes, sectors, cash vs. stock, distressed vs. strategic.
The information is all public. The problem is that it is scattered across dozens of sources, announced at random hours, and written in a dozen different formats. By the time it shows up in the big commercial databases, the edge is usually gone.
That gap between "announced" and "structured" is exactly what our Acquisitions API is built to close. This post is about why we built it, and what it actually gives you.
Why M&A Data Is Weirdly Hard to Track
If you have ever tried to monitor acquisitions yourself, you already know the pain points. If you haven't, here is a quick tour.
1. There is no single source. Tech acquisitions show up on TechCrunch and the company blog. Industrial deals get a paragraph in a trade journal. European deals often surface first in local-language business press. If you only watch one or two outlets, you miss most of the picture.
2. The same deal gets announced multiple times. The buyer blogs it, the target blogs it, three news sites rewrite the press release, and someone posts it on LinkedIn. Naive pipelines count that as five deals. Deduplicating correctly — matching "Snowflake acquires Natoma" across five different URLs — sounds easy until you see how inconsistently companies are named.
3. The money is fuzzy. "Undisclosed terms." "Approximately $50 million." "A deal reportedly worth up to €120 million with earnouts." Raw text like that is useless if you want to filter deals by size or aggregate deal value by sector. Someone has to normalize it, and that someone should probably not be you at 11 PM.
4. Announcement is not completion. A deal announced in March might close in June, get blocked by regulators in August, or quietly fall apart in October. Most scraped datasets just give you the headline and stop there. Knowing the status of a deal — announced, completed, blocked — matters a lot depending on what you are using the data for.
5. The sites fight back. High-value business data sits behind Cloudflare, rate limits, and increasingly, paywalls. Anyone who has maintained a scraper against those defenses knows it is a treadmill that never stops.
What We Actually Built
At ParheliaWeb, we were already running crawlers for funding rounds, IPOs, layoffs, and regulatory fines. Acquisitions was the natural next dataset — same pattern of "scattered public news that should be structured data" — and honestly, one of the most requested by our customers, especially sales intelligence and market research teams.
So we pointed the pipeline at it. Every acquisition announcement we find gets deduplicated, normalized, enriched, and cross-checked. On the Pro tier, we also extract the details that matter for real analysis: deal status, currency, sector, whether it was an all-cash or all-stock deal, whether it was a distressed acquisition, the acquirer's stock ticker, and the target's country.
The result is one endpoint that looks like this in 5 lines of Python:
import requests
API_KEY = "your_api_key_here" # Get one at parheliaweb.com
response = requests.get(
"https://parheliaweb.com/v1/acquisitions",
headers={"x-api-key": API_KEY},
params={
"max_age_days": 30,
"max_records": 10
}
)
data = response.json()
for deal in data["results"]:
print(f"{deal['company_name']} acquired {deal['acquired_company']} "
f"for {deal['deal_amount']} ({deal['announcement_date']})")And the response is clean, structured JSON — not a pile of headlines you still have to parse:
{
"user_tier": "pro",
"count": 10,
"max_age_days": 30,
"last_crawled": "2026-09-05T08:15:00Z",
"results": [
{
"company_name": "Snowflake",
"acquired_company": "Natoma",
"deal_amount": "$6 billion",
"deal_type": "Acquisition",
"announcement_date": "2026-08-28",
"source_url": "https://example.com/...",
"source_status": "active",
"deal_status": "Announced",
"currency": "USD",
"sector": "Enterprise AI/Software",
"acquisition_rationale": "AI strategy",
"is_all_cash": false,
"is_all_stock": true,
"is_distressed": false,
"acquirer_ticker": "SNOW",
"target_country": "US"
}
]
}No deduplication logic to write. No currency normalization. No waking up to find your selectors broke overnight. It just works.
Who Is This Actually For?
I get asked this a lot, so let me be concrete about the use cases we see most:
- Sales and outbound teams. An acquisition is a buying signal. The acquirer is integrating systems, reassessing vendors, and often has fresh budget. If your outreach is timed to the announcement instead of six months later, your reply rates tell the story.
- Investors and analysts. Deal flow by sector, deal size trends, cash vs. stock mixes, distressed deal frequency — these are inputs to real theses, not vanity metrics. The Pro tier fields exist precisely for this kind of aggregation.
- Competitive intelligence. When a competitor starts acquiring, they are telling you their strategy in public. Structured acquisition history is a much better read on a company's direction than their marketing site.
- Data teams. If you are building a CRM enrichment, a market map, or a risk product, this is a dataset you can plug in with a single cron job instead of a dedicated pipeline.
One more thing that matters to some of our customers: you can append ?lang=zh to any request and get industry terms and AI-translated summaries in Simplified Chinese. We have a growing base of Chinese SaaS and data companies building on our APIs, and full localization — including the payment side — is something we take seriously.
The Honest Comparison
You know I am going to be transparent about this, because that is kind of our whole thing. Here is how building your own M&A pipeline compares to using the API, based on what it actually took us to build ours:
| Factor | Build It Yourself | Acquisitions API |
|---|---|---|
| Time to first result | 4-8 weeks | 5 minutes |
| Ongoing maintenance | 10+ hours/week | 0 |
| Deduplication across sources | Your problem, forever | Handled |
| Deal status tracking (announced/completed/blocked) | Rarely implemented | Pro tier field |
| Amount normalization (currency, "undisclosed", ranges) | Messy, manual | Standardized |
| Coverage | The 2-3 sources you can scrape | Multiple sources, continuously crawled |
| Compliance (GDPR, source attribution) | Your risk | Handled |
| Cost | Your time + proxy infrastructure | Free tier → €29/month (Pro) |
My rule of thumb, same as always: if your core business is collecting this data, build it — and respect, that is a hard job. But if your core business is using the data, use the API and spend your engineering hours on the part that actually differentiates you.
Pricing, Without the Games
Since transparency is our whole pitch, here it is plainly. The free tier gives you 100 calls per day, forever, with access to the last 30 days of data — real data from the same engine our paid customers use, not a crippled demo. The Pro tier is €29/month: 1,000 calls per day, the full dataset going back up to 10 years, and all the enriched fields.
One API key works across all six ParheliaWeb APIs — Acquisitions, Funding, Layoffs, Fines, IPOs, and Email Validation — and you can upgrade each one independently depending on what you actually use.
Getting Started
Try it free
100 free API calls per day, no credit card required. See if the data quality works for your use case.
Pro tier: Full dataset, 1,000 calls/day, enriched deal fields — €29/month.
Get your free API key →🚀 Acquisitions are only half the signal
M&A tells you who is consolidating. Funding rounds tell you who is growing — often months before the acquisition does. Check out how we built our Startup Funding Signal API to deliver AI-verified funding data through the same simple REST endpoint.
Questions?
I'm Andy, the founder. I'm just an IT guy who likes solving problems for people. If you have questions about the data, the coverage, or the API, just drop me a line.