If you need to know which companies just raised, you have five real options. They trade cost against effort in different directions, and the right pick depends on one question: do you need history, or do you need new activity?
Those are different products, and most of the frustration I see comes from buying one and needing the other.
Reading it yourself
Free. Fine for a handful of deals a week. TechCrunch, EU-Startups, Axios Pro Rata, and your vertical's trade press cover most of what matters in your segment.
It does not scale, and it is the first thing to slip when the week gets busy. If your process depends on someone remembering to skim on Monday, it will break within a month.
Incumbent databases
Crunchbase, PitchBook, Dealroom. Deep history, strong coverage, entity graphs that took years to build.
They are built for research and investment workflow, sold on annual contracts, and usually need a sales conversation before you can evaluate them. API access is often a separate and more expensive tier than seat access, which catches teams out when they try to automate later.
Right choice if you need to answer "who has raised in this space over five years". Heavy if you only need "who raised this week".
Newer signal providers
Harmonic, Coresignal, and similar. Built for the GTM use case rather than for research, generally API-first, generally cheaper to start. Coverage is narrower than the incumbents on history but competitive on recent activity.
Public filings
SEC EDGAR Form D in the US, national registries elsewhere. Free and authoritative.
The catch is lag and completeness. A Form D is filed within 15 days of first sale, plenty of rounds never surface in a form you can watch, and the filing tells you almost nothing useful about the company. Good as a cross-check, weak as a primary feed.
Signal APIs
Pay for a filtered feed rather than a database seat. You send a filter, you get structured rows back, you pay for what you pull.
This is the shape that fits an automated pipeline best, because the output is already structured and the cost scales with what you actually use instead of with headcount.
Choosing
Ask what you are going to do with it.
Research and market mapping wants depth and history. Buy an incumbent database, take the seat, accept the contract.
Outbound and pipeline wants a feed. You need recency, structured fields, and something that runs on a schedule without a human. Depth of history is close to worthless here, because nobody is opening with "congratulations on your 2019 Series A".
The mistake is buying a research tool for a pipeline job. You end up paying for five years of history to use the last five days, and you still have to build the automation yourself.
Before you commit
Whichever direction you go, test with your real filter, not with a demo query. Run your actual stage, amount, geography and industry constraints and count what comes back per day. A source that looks broad in a demo can return three rows a week once your filter is applied.
Then check the quality of those rows. I wrote up the four failure modes that matter most in Four traps that quietly break funding data pipelines, and they apply to every provider on this list.
You can run that test against our data without signing up for anything, using the funding rounds actor on Apify, or read the filter behaviour in the API docs if you want to compare it against what you use today.
I work on Datahyena, which is the last category on this list. The comparison above is how I would pick if I were not.
Top comments (0)