SaaS for Biopharma

Companies

Veeva, Certara, Schrödinger, Simulations Plus, Benchling, Dotmatics, Latch Bio, Unlearn AI, Vial, Convoke, Sleuth, Medidata, Noetik, Chai, Boltz

Compound portfolio companies Briefly and Spaero

Overview

Systems of Record: Data Layers & Orchestration of the Most Vital Functions

These companies modeled more closely to traditional tech SaaS are currently somewhat out of favor given that Benchling is the only recent startup that has been a qualified successes. However, people easily forget about companies like Veeva that ramped to $2.8B in revenue at 75% gross margin as detailed below.

Just like in traditional tech SaaS, the prime “beach front” SoR properties of CRM, clinical trial & EMR management command orders of magnitude more value than all other areas. While the aforementioned companies currently own those properties, we view the likes of Veeva, Certara, etc. as being very much surmountable (unlike Epic Systems in healthcare or Salesforce, etc. in traditional SaaS).

We believe this not only because their positions aren’t all that strong but also because biotech is more gated than traditional SaaS by intelligence (e.g. digesting academic literature) and interpersonal interaction (e.g. 40 minute calls with thousands of patient for trial recruitment). Indeed, the largest biopharma SaaS companies in existence are still often 60% consulting-like services businesses vs 5-15% for the tech SaaS giants. AI is of course the fixer.

AI may well be enough to dislodge the incumbents holding the most desirable properties because the way the technology is built shifts from the UI / front-end being as easy as possible for humans to manually enter and edit data. Going forward, the winner might be the builder of the most flexible and efficient database for use by AI agents.

We’re also particularly excited about startups automating clinical trial recruitment with the ultimate goal of being an end-to-end clinical trial operator. The company’s core competency beyond cheap operation should be optimizing for each individual trial the most efficient and effective possible route with modern tools: integrate the short-cuts to getting human evidence as detailed in Shelby’s fantastic post and biomarkers.

Financials of Biotech SaaS incumbents

Veeva Financials:

https://sacra.com/c/benchling/; https://sacra.com/research/benchling-github-of-biotech/?utm_source=chatgpt.com

Drug Discovery Tooling

Companies building here should be keenly aware of the difficulties of selling pre-clinical tools to pharma. Pharma is very reluctant to try novel technologies and change their workflows around them. Worse, they will pay minuscule amounts for it.

As such, discovery-focused companies typically pursue the platform route commercialize via services and JVs where they orchestrate their own tech to help pharma hit targets that have proven impossible to drug with their existing internal methods, which is of course a high bar.

It normally works up from services payments to JVs after customer feels the value and the startup can negotiate for larger downstream economics.

Building a business purely around subscriptions is so much harder. Schrödinger is the most successful version. They launched in 1990 under what sounds like a paradigm-shifting premise of applying computation to drug discovery, chiefly via MD simulation. 25 years later, they make $200M in revenue and have turned a profit in just one year. They’ve now pivoted to also doing internal drug development (in addition to spinning off Nimbus).

This industry dynamic of pharma paying pennies for pre-clinic discovery tools may change if and only if pharma starts to view novel approaches as existential threats. We’re currently seeing this play out with our portfolio company Wayve. Just within the last year, the legacy car OEMs have internalized that AVs are here and are existential to their very survival. Negotiations have shifted from contracts for tiny margins won via hand-to-hand combat over 2-year sales cycles to truly massive deals with 3 month sales cycles.

SaaS startups building discovery tooling especially expensive tooling like foundation models are implicitly betting the company on their tech striking sufficient fear into pharma such that it trigger its survival instinct such that they can get 8+ figure up-fronts for pre-clinical tooling or JVs.

Early in 2026, we may be seeing the start of this for AI models with Noetik, Chai, and Boltz all securing large software license deals with pharma. Several are $10M+ per year without exclusivity.

One final thing to consider is that hiring a sales/BD team so strong that they can keep going a perpetual pipeline of software deals worth $10s-100s of millions isn’t that much easier (if easier at all) than hiring a great drug development team and pursuing partnerships and/or an internal pipeline. The difference is that historically the latter captures orders of magnitude more upside if it works.

And, hiring quality BD people into SaaS biopharma startups is hard and more importantly they’re too expensive for early stage startups and don’t tend to be the right cultural fit (e.g. they need EAs to do everything, want a company car, etc.).

This tends to mean that these startups rely on founder-led sales. Deciding if this is a good fit requires a deeply personal assessment of themselves, their desire to devote themselves to B2B sales growth, and depth of personal network in pharma BD teams.

For more on selling software to biopharma, read this excellent post.

Further Reading

https://astera.org/software-sales-handbook/

https://www.aibiodesign.com/p/selling-ai-products-services-to-big

https://shawndimantha.substack.com/p/the-techbio-idea-maze-to-be-or-not

https://centuryofbio.com/p/vial

https://scuttleblurb.substack.com/p/veeva-ai

https://www.benchling.com/blog/biotech-guide-to-data-driven-rd

https://www.michaeldempsey.me/blog/2025/10/03/sequencing-vs-equal-odds-applied-research/

• Sales cycle: 6-18 months of hand-to-hand combat. Polyphron’s evidence-as-a-service is trying out $10k contracts because that size doesn’t have to be cleared by higher ups like $1M+ deals do. TBD if that actually short-circuits the cycle. 

Contract sizes for scaled companies:

• Point tools / seats (PK/PD, PBPK, ELN/LIMS modules, validation tools): ~$10k–$150k per year per customer/tenant. Examples: Simulations Plus’ avg software revenue per customer ≈ $129k; Certara small-software buys often in the $16k–$41k/yr band.
• Mid-market multi-module: ~$150k–$750k/yr. Example anchor: Veeva median contract ≈ $212k/yr (Vendr sample).
• Enterprise platform (Top-20 pharma scale): $1M–$10M+ per year. Schrödinger now has 31 customers ≥$1M ACV and 8 customers ≥$5M ACV; Veeva regularly lands multi-app “platform” wins at top pharmas (one of its largest subscription orders ever cited recently).

Veeva’s GTM & Distribution

• Piggybacked on Salesforce’s platform & channel (early credibility + reach). Veeva CRM launched as a Force.com app; Salesforce named Veeva its premier ISV partner for pharma/biotech and co-marketed the product (press release includes a Pfizer SVP endorsement and AppExchange call-to-action).
• Exclusive/non-compete positioning inside Salesforce’s ecosystem. Veeva’s S-1: “We are salesforce.com’s preferred and recommended… provider” and SFDC agreed not to build/promote a directly competitive pharma/biotech sales-automation app during the term—reducing channel conflict while Veeva sold. It recently fully broke away from any dependence on Salesforce.
• Direct enterprise sales, globally organized by region, with CS & services under GM ownership. Veeva sold through a global direct sales force in 13 countries, with regional GMs owning sales, services, and success (tight post-sale expansion loop).
• Word-of-mouth within a tight vertical. S-1: “We… benefit from word-of-mouth marketing as customers endorse our solutions to their industry peers,” enabling a leaner sales headcount.
• Partner ecosystem as distribution amplifiers. Pre-built integrations (sample reconciliation, EDC, expenses) plus a content partner program with 100+ agencies that build PromoMats materials—both create downstream pull from customers’ vendors.
• Wedge → land-and-expand. Started with commercial CRM, then expanded across R&D/Reg/Quality (Vault). S-1 explicitly lays out the plan to “increase adoption… in R&D departments” by growing the sales org.
• Community & customer conferences drive upsell. Veeva’s R&D & Quality (and Commercial) Summits convene 1,500–2,400+ life-sciences leaders annually—high-density forums for reference selling and cross-suite expansion.
• Founder narrative: ultra-vertical focus as GTM strategy. Peter Gassner (SaaStr): choose the “Salesforce for Pharma” niche and go deep; a core pillar behind efficient distribution and large deal sizes.

Schrödinger GTM & Distribution

• Direct, technically led enterprise sales + regional distributors. S-1: a ~130-person global team of sales, technical and scientific personnel sells directly across the US/EU/Japan/India, with distributors in China & South Korea—reflecting the need for app-scientist-led selling. SEC
• Academia-first seeding → industry pull. S-1: software used at 1,250+ academic institutions; “by introducing the benefits… at the academic stage, we will drive brand awareness” that spills into biopharma jobs. Academic Site Licenses and free PyMOL for students/educators reinforce this funnel. SECSchrödingerpymol.org