The question shows up at a predictable moment. Someone asks for a report covering Instagram, TikTok, YouTube, and X in a single view. It needs to include accounts you don't own, because the comparisons worth making are competitors and creators. It needs enough history to show a trend.
Nothing off the shelf does all of that at once, so eventually someone says the obvious thing: we could just build this.
We're in an unusual position to answer, because we've done both. We build custom data infrastructure for clients, and we built and still operate Socialpruf, a social intelligence platform tracking tens of millions of posts across those same four platforms. So we've made the case for building, and we've also argued against it.
Most teams compare the wrong two numbers
The comparison that usually gets run is a vendor's annual subscription against a rough guess at what it would cost to write a scraper. Framed that way building always wins, because a scraper is a couple of weeks of work and a subscription is a line item somebody has to defend every year.
The framing is wrong, because a scraper isn't a reporting system. Once the posts are collected you still have to reconcile four platforms that each count a view differently, keep a creator's history attached to them when they rebrand, hold enough post-level detail to show a trend without the reports timing out, settle on one definition of engagement rate so two dashboards can't disagree, and get the result to a client as something better than a screenshot.
Collecting the posts is the cheapest of those to get working. It's also the only one the comparison prices.
The subscription isn't what you'd be replacing. Everything after collection is.
The five ways teams actually solve this
Plenty of agencies run two or three of these at once, so they aren't mutually exclusive. But each fails in a specific way, and it's worth knowing which failure you're signing up for.
| Approach | Best for | Time to value | Where it breaks |
|---|---|---|---|
| Native dashboards (Instagram Insights, TikTok Analytics, YouTube Studio, X Analytics) | Running your own accounts day to day | Immediate | Owned accounts only. No competitors, no creators, no cross-platform view, and history limits vary by platform. |
| Spreadsheets and screenshots | One-off analysis and very small portfolios | Hours | Cost grows with every account and every reporting cycle, and numbers are stale the moment they're pasted. |
| Social management suites (Hootsuite, Sprout Social, Buffer, and similar) | Publishing, scheduling, inbox, and reporting on accounts you connect | Days | Built around accounts you connect, so public-account and competitor coverage is secondary to the publishing workflow. |
| A custom internal pipeline | Data that feeds a product you sell, or sources no vendor collects | Months | Maintenance. Platforms change constantly, and an unattended pipeline degrades quietly instead of failing loudly. |
| A specialist public-data platform (Socialpruf, Rival IQ, Socialinsider, and others in the category) | Tracking accounts you don't own, benchmarking, creator vetting, client reporting, or feeding your own warehouse over an API | Days | You inherit someone else's coverage, definitions, and roadmap. If you need a source they don't collect, you wait or you build. |
The cost that never shows up in the estimate
Building all of that is a multi-month engineering project. Most teams can talk themselves into that, because it's a number with an end.
The cost that should actually decide this is the one after launch, because it doesn't end. Social platforms are moving targets. Page structures change, access rules tighten, rate limits move, fields disappear, and anti-automation systems get better at noticing anything that doesn't behave like ordinary traffic. None of that means you built it wrong. It's just the weather.
What separates a pipeline that survives from one that doesn't is whether it can answer these without a human thinking to ask:
- Did every job run, and did it return the volume history says it should have?
- Did a response change shape in a way the parser didn't notice?
- Are fields that used to hold values now coming back empty?
- Did the reporting layer refresh before the client opened the report?
If the honest answer is that you'd find out when a client complains, you don't have a data pipeline. You have a reporting system that will eventually be confidently wrong, and you won't know which week it started.
Why we built our own, and why that doesn't mean you should
We built Socialpruf because we wanted the collection layer under our own control rather than depending on whatever the platform APIs chose to expose. That was right for us, and it's worth being precise about why, because the reasoning doesn't transfer.
Social data is what we sell. The coverage is the product. Owning the pipeline is the only way to have a ceiling higher than the APIs allow, so maintenance isn't overhead for us. It's the business.
For a brand or an agency, social data is almost never the product. It informs pricing, campaigns, partnerships, and client conversations, all of which matter, none of which require you to own the collection layer.
Build when the data is the product
- It's part of what you sell. If it feeds a product, a client-facing score, or a pricing model, the collection layer is a competitive asset and renting it caps your ceiling.
- You need sources nobody packages. Niche platforms, regional networks, forums, or your own first-party data joined against public social activity.
- You need control of the collection logic itself: a bespoke crawl cadence, custom extraction, or coverage a vendor has no reason to build.
- Your definitions are genuinely unusual. Custom EMV models or proprietary scoring, where a vendor's fixed metric definitions would become your main limitation.
Buy when you're solving a solved problem
- You need it this quarter. A pitch, a renewal, or a campaign post-mortem won't wait for a multi-month build.
- Your requirements are the category's requirements. Competitor benchmarking, creator vetting, and client reporting are solved problems, and rebuilding them buys you nothing.
- Nobody is going to own it. A pipeline without a named owner degrades within months and then gets quietly abandoned.
- The accounts you care about will never connect to your tooling. Public-data platforms analyze accounts without the account holder authorizing anything, which is the entire point of competitor and creator work.
So, should you build or buy?
Framing it as build or buy is a little false, because the answer that holds up is usually a split. Buy the collection layer: it's the part that breaks most often, and the part where somebody else's scale works in your favour. Own the modelling and the reporting, because that's where your business logic lives and where you don't want a vendor's opinions baked in.
Build if the data is genuinely what you sell, if you need sources nobody collects, or if you need control of the collection logic itself. That's the work Solidstack does, and we run what we build rather than handing over a repository and wishing you luck.
Buy if what you need is cross-platform analytics on public accounts, competitor benchmarking, post-level history, and live client reports. That system already exists, we built it, and it's called Socialpruf.
And if you're genuinely torn, buy version one. A pipeline built after you know exactly what you need is cheaper, smaller, and better than the one you build to find out.
Buy the layer that breaks. Own the layer that differentiates.
Frequently asked questions
- Should we build or buy a social media analytics platform?
- Buy if you need cross-platform data on public accounts quickly and your requirements are standard: competitor benchmarking, creator vetting, or client reporting. Build from scratch if the data feeds a product you sell, covers sources no vendor collects, or needs collection logic only you would write. The answer that holds up longest for most teams is a split: buy the collection layer and own the modelling and reporting on top of it.
- What does it cost to build social media analytics in house?
- Two costs, and teams usually only budget the first. Collecting the data is the small part. Reconciling four platforms onto one set of definitions, storing enough history to show a trend, and getting reports in front of clients is a multi-month engineering project. Keeping it running is a permanent commitment, because the platforms change continuously and an unmonitored pipeline degrades silently rather than failing loudly. The ongoing cost is the one that decides most build-or-buy calls.
- Why are native social platform APIs not enough for competitor analysis?
- Native APIs exist so you can manage accounts you own, and account-level analytics generally require the account holder to grant access. Competitors won't, and most creators won't before a deal is signed. The APIs also expose a subset of metrics, cap how far back you can look, and use different schemas and metric definitions from one another, so any cross-platform view needs a normalization layer regardless of where the data came from.
- What is the biggest hidden cost of building a social data pipeline?
- Operations. The first successful collection run is straightforward; keeping four platforms flowing correctly every day is not. Page structures, access rules, rate limits, and anti-automation systems all change on their own schedule, and the resulting failures are usually silent. Without monitoring, data-quality checks, and a named owner, the pipeline keeps producing numbers that are confidently wrong and nobody can say when that started.