Your Real Competitor Is a Data Engineer with 3 Months and a GitHub Repo
Scale or Fail: A GTM Clinic for Healthtech
There’s a category of healthcare data product that keeps getting built, keeps getting priced correctly, keeps solving a real problem, and keeps struggling to sell.
The product is some variation of: “We took messy, public healthcare data (RxNorm, FDA, CMS, DailyMed, ICD-10, SNOMED, you name it), cleaned it up, joined it together, and made it queryable. You can pay us $5-50K/year instead of spending months doing it yourself.”
The value proposition is obvious. Government healthcare data is free and notoriously painful to work with. Enterprise data products from companies like First Databank, Medi-Span, and Elsevier Gold Standard cost $100K+ per year and come with complex vendor contracts. There’s a clear gap in between.
Founders who’ve personally suffered through parsing DailyMed XML files or normalizing NDC formats across five different CMS data sources see the opportunity immediately: build the middle option. Better than free, cheaper than enterprise. A wedge product for early-stage healthtech startups, researchers, and analytics teams that can’t justify six-figure data contracts.
The product gets built. The data quality is good. The documentation is clean. The price is right.
And then sales stall.
The competitor nobody puts on a slide
When these founders build their competitive positioning, the slide usually has two columns: “us vs. enterprise databases.” Sometimes three: “raw government data vs. us vs. enterprise databases.” The argument is clear and usually accurate. Enterprise databases are expensive and come with legacy formats and long procurement cycles. Raw government data is free but requires months of specialized knowledge to make usable. The middle option saves time and money.
What’s missing from the slide is the competitor that actually kills most deals: the internal build.
At almost every early-stage healthtech company, there’s a data engineer who has already started building an internal pipeline for whatever government data the product needs. They’ve written Python scripts to download weekly RxNorm releases. They’ve built a dbt model that normalizes NDC formats. They’ve set up an Airflow DAG that refreshes the data on a schedule. It works. It’s not great. Parts of it break regularly. But it exists, and it was “free” because the engineer was already on payroll.
Selling a $5,500/year data product to a team that has a “good enough” internal pipeline is harder than selling against a $100K enterprise database. The enterprise database is a clear budget decision: do we have $100K for this? But the internal pipeline is a sunk cost and a psychological barrier. The team already invested time in it. The engineer who built it has opinions about it. Ripping it out and replacing it with an external dependency feels like admitting the internal effort was wasted.
This is the real sales objection, and it never shows up in a discovery call as “we built our own.” It shows up as “we’re not ready to commit right now” or “let us evaluate this internally” or “we need to check with our data team.”
Why the build-vs-buy math never works in the founder’s favor
Every founder in this space has done the math: it takes a data engineer 2-3 months to build a usable pipeline from raw government data sources. At $150K/year fully loaded, that’s $25-37K in labor cost. Our product costs $5,500. The ROI is obvious.
The problem is that this math only works if the buyer agrees with the framing. And the buyer usually doesn’t, because:
The internal build is incremental. Nobody spent 3 months heads-down on a drug data pipeline. They spent 20 minutes here, an afternoon there, a week when something broke. The cost is real but invisible. It never appeared as a line item on a budget. It’s death by a thousand commits, and nobody ever adds them up.
The engineer who built it has ownership. Replacing an internal pipeline with a commercial product means telling someone their work is being deprecated. In a 10-person startup, that’s a political conversation nobody wants to have over a $5,500 purchase.
“Good enough” is sticky. The internal pipeline covers 80% of what the team needs. The remaining 20% (edge cases in NDC normalization, inactive ingredients, classification hierarchies) causes occasional pain but not enough to trigger a purchase. The product that solves 100% of the problem competes with a solution that solves 80% of the problem at $0 marginal cost.
The buyer doesn’t know what “good” looks like. If you’ve never used a properly built drug data mart, you don’t know what you’re missing. The engineering lead who’s been querying their homegrown pipeline for a year has adapted their workflows to its limitations. They don’t experience those limitations as bugs. They experience them as “how drug data works.”
The enterprise database isn’t really the competitor either
Here’s the other positioning problem. When a $5,500 data product claims “95% cost savings vs. enterprise databases,” it raises a credibility question rather than closing a sale: if it’s 95% cheaper, what’s missing?
The answer is usually a lot. Enterprise drug databases like First Databank’s MedKnowledge include clinical decision support rules, drug interaction screening, patient education monographs, drug images, regulatory compliance content, and 40+ years of accumulated clinical knowledge. They’re integrated into the majority of hospital systems and pharmacy platforms in the US. They’re not just data. They’re clinical infrastructure.
A curated government data product and an enterprise clinical database are fundamentally different product categories. Positioning them as competitors on a pricing slide conflates the two and makes the buyer wonder whether the cheaper option is just a worse version of the expensive one.
The more honest positioning, and the one that actually helps the buyer decide, is: “We provide drug terminology, pricing, and classification data for analytics and software development. We don’t provide clinical decision support, interaction screening, or patient-facing content. If you need those, you need First Databank. If you need what we provide, you don’t need to pay for what they include.”
That framing doesn’t win every deal. But it wins the right deals cleanly, instead of creating doubt in all of them.
What a viable go-to-market looks like in this category
The products in this space that do sell have figured out a few things that the ones that stall haven’t.
They sell to the moment, not the org. The best entry point for a healthcare data product isn’t “replace your existing pipeline.” It’s “you’re starting a new project and you need drug data by Thursday.” A startup that just raised a Series A and is building their first analytics dashboard. A research team that needs drug classification data for a grant deadline. A consultancy building a prototype for a client. These buyers don’t have an existing pipeline to defend. They have a deadline and a credit card.
They make the data visible before the purchase. The biggest conversion blocker for data products is the gap between “this sounds useful” and “I can see it working with my use case.” A free sample dataset, a sandbox environment, or even a well-documented schema with example queries does more selling than any feature comparison table. The buyer who can run a SQL query against sample data before committing $5,500 closes faster than the buyer who has to take the homepage’s word for it.
They position against the build, not the buy. Instead of “we’re cheaper than First Databank,” the effective pitch is “we’re faster than building it yourself.” This reframes the decision from a budget conversation (do we have $5,500?) to a time conversation (do we have 3 months?). Time is the scarcer resource at every early-stage company.
They create switching costs early. Open-source foundations are common in this space, and they’re a smart distribution strategy. But the commercial product needs to create value that the open-source version doesn’t, and that value needs to compound over time. Weekly data refreshes that the self-hosted version doesn’t automate. Pre-joined tables that save 10 hours of SQL every month. Data marts for specific use cases (pharmacy pricing analysis, drug class aggregation, excipient tracking) that would take weeks to build from scratch. Each month the customer uses the product, the gap between “I could do this myself” and “I’d have to rebuild all of this” gets wider.
They find distribution partners, not just customers. A drug data product that’s listed on Snowflake Marketplace or AWS Data Exchange gets discovered by data teams who are already shopping. A product that integrates with dbt gets adopted by teams that already use dbt. A product that partners with a pharmacy management system vendor gets bundled into an existing purchase decision. Distribution through platforms and partnerships is how sub-$50K data products reach scale without building a sales team.
The questions the founders who close deals can answer
Five things separate the healthcare data products that get traction from the ones that stall.
They can name their buyer in one sentence. Not “everyone who works with drug data.” That’s a market, not a buyer. The founders who close can say something like: “Series A healthtech startups building pharmacy analytics in their first 6 months” or “clinical data teams at mid-size health plans who don’t have budget for First Databank.” One sentence. Specific enough that you could find 50 of them on LinkedIn in an afternoon.
They’ve built something the buyer couldn’t replicate in a quarter. If a competent data engineer could rebuild 80% of your product in 3 months, your moat is thin and your buyer knows it. The products that hold are the ones that add a layer the engineer can’t easily build: proprietary data joins across sources that don’t naturally connect, classification hierarchies that require clinical domain expertise, or delivery infrastructure (weekly refreshes, quality checks, schema migrations) that’s boring to maintain and expensive to get wrong.
They’ve drawn a clear line between free and paid. If your product has an open-source foundation, the buyer needs to understand exactly what they get for free and exactly what they’re paying for. “You can build the pipeline yourself or you can subscribe to the maintained output” is a clear boundary. “We’re figuring out what stays open and what goes commercial” tells the buyer to wait.
They put their credentials on the site. For a product that touches drug data, medication information, or clinical terminology in a regulated industry, “built by experts” is only credible if the experts have names, faces, and verifiable backgrounds. A PharmD on the founding team. A clinical informaticist as an advisor. A visible career history in the domain. That changes the trust equation in ways that technical documentation can’t. If you have domain expertise, it should be the first thing a visitor sees, not something they have to dig for.
They have proof that someone has bought this. Customer logos, a case study, or even a detailed walkthrough of a real use case with named tools and workflows. One real example of a team using the product to solve a specific problem is worth more than every feature comparison table on the site combined. If you don’t have paying customers yet, publish a detailed use-case walkthrough that shows the product working end-to-end. “How to build a drug pricing dashboard in 30 minutes” with actual SQL and actual output is the next best thing to a testimonial.
The bottom line
The middle tier of healthcare data products (better than free government data, cheaper than enterprise databases) is a real and growing market. The products are usually well-built, fairly priced, and solving genuine pain.
The reason they stall isn’t product quality. It’s commercial positioning. They compare themselves to the enterprise option the buyer was never going to purchase anyway, while ignoring the internal build that’s their actual competitor. They price against a budget that doesn’t exist instead of against the time the buyer is already spending.
The founders who figure this out stop selling against First Databank and start selling against Thursday’s deadline. That’s a conversation the buyer is already having, and the data product is the fastest answer to it.