--- title: "Build a funding signal pipeline, or buy one | Datahyena" url: https://datahyena.com/compare/build-vs-buy/ description: "Scraping funding news is a weekend project. Keeping it correct is not. What the build actually costs, and the numbers a bought feed has to beat: 71% of rounds within an hour of publication, median 36 minutes." --- [Build vs. buy](https://datahyena.com/compare) # The first version takes a weekend. The maintenance never stops. Parsing funding announcements is genuinely not hard. What is hard is that six outlets report one raise with three different amounts, the company name matches four companies, and every source changes its markup on a schedule nobody tells you about. [Get your API key →](https://app.datahyena.com/register) [See the signals](https://datahyena.com/signals/funding) In short You can build this. Most teams that try get a working prototype quickly and then discover the real work: deciding that several articles describe one event, attaching it to the right company out of several with similar names, reconciling amounts and stages where sources disagree, and knowing when to publish nothing rather than something wrong. None of that is finished once. Sources change and the pipeline degrades quietly, which is the failure mode that costs the most, because nobody notices until a customer does. ## Measured, not asserted. Recomputed weekly from the live corpus. Every figure below is reproducible from your own API responses. 57% of rounds are reported by exactly one publication The single number that decides whether a build succeeds. Watching a handful of well-known outlets is achievable in a day and misses most of the market permanently. 73% of rounds are queryable within an hour of publication The bar a scheduled crawl has to clear. Measured from the article's publish timestamp, recomputed weekly. 45+ sources monitored continuously Each with its own format, quirks and failure modes, all of which change without notice. ~790 new rounds a month to keep correct Deduplicated, resolved to a company, and reconciled where outlets disagree. As of August 31, 2026 ## Side by side. Datahyena Building it yourself Time to first clean signal One API call Weeks to a working version Coverage of the long tail 60% of rounds come from a single outlet Scales with sources you maintain Deduplication One raise is one event You design and maintain it Entity resolution Companies, investors and people resolved The hardest part, and never finished Knowing when to publish nothing Confidence floor; low-confidence events are withheld You build the judgement Source maintenance Ours Yours, forever Latency Median 36 minutes from publication Your schedule plus your uptime Cost shape Usage-based, scales down as well as up Engineering salary, permanently ## Which one fits you. ### Choose Datahyena when - You want the problem solved rather than owned. One call returns a resolved event with the company and investors attached. - Your engineers have something more valuable to do than repair parsers. - You need coverage beyond the obvious outlets, which is where most of the market actually is. - You want someone else on the hook when a source changes shape on a Sunday. ### When building it yourself makes sense - Signal collection is your product, not an input to it. Then this is your core IP and you should own it. - You have proprietary or private sources no API can replicate. - You have a data team that will still own this in two years, budgeted and staffed. - Your requirements are narrow enough to stay narrow: one country, one sector, a handful of sources you can watch by hand. ## What makes Datahyena different. ### The hard part is not fetching Every build underestimates the same three things: that one raise appears across many outlets and has to collapse into one event, that the company name in an article often matches several real companies, and that outlets disagree about the amount and the stage. Getting an article is a solved problem. Deciding what it means is the product. ### Silent degradation is the real cost Pipelines rarely fail loudly. A source changes markup and one feed quietly returns nothing; a parser starts reading the wrong element and produces plausible garbage. Weeks later someone notices the numbers are wrong. Detecting that continuously is its own system, and it is the part nobody scopes. ### Abstention has to be designed in The instinct when evidence is ambiguous is to pick the most likely answer. For this data that is the wrong instinct: a wrong company on a real round routes work to the wrong account and corrupts everything downstream. Deciding to return nothing is a feature, and it is harder to build than the happy path. ## Where Building it yourself wins. A comparison with no losses is an advert. These are the cases where we would tell you to buy the other thing. ### If you only need a few sources, build it Genuinely. If your scope is one country, one sector, and five publications you can name, a scheduled fetch and a language model will get you most of the way, and paying for a corpus you will not use is waste. The economics turn when you need the long tail or the maintenance stops being free. ### You give up control of the schema Our fields are our fields. If you need a bespoke shape, an unusual event type or your own confidence rules, an API is a constraint where a build is not. ### Our funding record starts in 2020 Dense from 2020 forward. If you need a decade of history, that is a gap a build with archive access could close and we currently cannot. ## Common questions Datahyena and Building it yourself, answered. How long does building this actually take? A prototype covering a few known outlets: days. Something you would put in front of a customer — deduplicated across sources, resolved to companies, sensible when outlets disagree, and monitored so you find out when it breaks — is a materially larger project, and unlike the prototype it does not end. Could I just use an LLM on RSS feeds? For extraction, yes, and it works well. That gets you structured events per article. It does not tell you that this article and five others describe the same raise, or which of four similarly named companies it belongs to, or which figure to trust when two outlets disagree. That is the part that takes the time. What if we build and keep you as a fallback? That is a reasonable architecture and some teams run it. Use your own pipeline for the sources you care most about, and the API for coverage breadth and corroboration. The single-source figure is the argument for the second half. How do we compare our build against you? Take a week you already know about, run both, and compare on three things: how many rounds each caught, how long after publication, and how many were attached to the wrong company. The third one is usually the surprise. ## Start pulling signals in minutes. Create a key, claim your 50 free credits, and make your first request today. No sales call, no credit card. [Get your API key →](https://app.datahyena.com/register) [Read the docs](https://datahyena.com/docs) 50 free credits · no credit card