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Deep-tech startups generally need more patient, milestone-based capital and take longer to reach commercial scale than SaaS startups. In Boston Consulting Group’s comparison, the median path from seed to Series D was about 95 months for deep tech versus 75 months for other technology ventures. SaaS can test distribution and pricing earlier, but it remains exposed to churn, sales efficiency, security, competition and platform risk. The right financing strategy depends on when a company can prove technical performance, customer demand and repeatable economics—not on a label alone.
The short answer: where the two models diverge
Deep tech commercializes a scientific or engineering breakthrough. A company may need to prove that a material, device, drug platform, energy system, robotics stack or other technology works reliably, can be manufactured, meets regulation and solves a valuable customer problem. Those proofs often arrive in sequence, while revenue is still limited.
SaaS (software as a service) usually delivers software through cloud infrastructure. A team can release a minimum viable product, place it in customers’ hands and test pricing, acquisition channels and retention comparatively early. That does not make SaaS safe: weak product-market fit, high churn, expensive sales, cybersecurity incidents, commoditization and dependence on a major platform can destroy an otherwise promising company.
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- Capital intensity: Deep tech may require laboratories, specialized equipment, pilot lines, tooling, certification and inventory before meaningful sales. SaaS can often begin with software development and cloud costs, although enterprise sales and security programs can become expensive.
- Evidence available to investors: A SaaS company can show active users, recurring revenue, retention and acquisition efficiency earlier. Deep tech investors may have to underwrite technical data, intellectual property, prototypes and regulatory plans before revenue metrics exist.
- Timing: Technical validation, manufacturing scale-up and approvals lengthen deep-tech rounds. BCG found longer intervals at every early financing step in its sample.
- Risk pattern: Deep tech combines technical, manufacturing, regulatory, financing and adoption risk. SaaS concentrates more risk in product-market fit, retention, sales execution, security, pricing and competitive response.
- Return and exit pattern: Deep-tech portfolios can produce strong returns, but individual outcomes remain highly variable and exits may depend on strategic buyers, licensing or later public markets.
There is no authoritative cross-market funding amount that applies to every deep-tech or SaaS startup. A hardware-heavy company pursuing certification can need orders of magnitude more capital than a software company selling to small businesses, while an enterprise SaaS product with a long sales cycle can consume substantial capital before reaching efficiency. Build the financing plan from milestones and cash needs rather than from a generic industry average.
What “deep tech” and SaaS require founders to prove
Deep tech: two unknowns at the same time
The World Bank describes a deep-tech “double unknown”: founders must validate the technology while they search for product-market fit. A laboratory result is not automatically a manufacturable product, and a technically elegant product is not automatically something customers will buy. Some solutions also require three or more key technologies to work together, making development paths less predictable.
Before commercial revenue, a deep-tech team may need evidence such as repeatable performance in relevant conditions, a credible bill of materials, manufacturing yield, supplier capability, safety data, regulatory strategy, protected intellectual property and paid-customer or pilot commitments. The exact evidence differs by sector; a medical device, battery material and industrial-control system do not share one validation checklist.
SaaS: earlier market tests, continuing execution risk
Cloud delivery lets a SaaS team ship updates without building physical production capacity. Early tests can include activation, usage frequency, conversion from trial to paid plan, renewal, expansion and customer-acquisition cost. These signals are available sooner, but they can be misleading if customers are heavily discounted, implementations are manually supported or a small number of accounts dominate revenue.
As SaaS scales, investors examine recurring-revenue growth together with retention, gross margin, sales efficiency and security controls. A company can grow quickly and still be fragile if customers leave, service costs rise, a sales team cannot recover its cost or a platform changes its rules. Comparing SaaS with deep tech is therefore a comparison of risk timing and evidence—not a claim that one model is universally safer.
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Why deep tech usually needs more patient capital
The European Commission summarizes the financing problem this way: “Deep tech innovation requires large amounts of patient capital as the companies concerned generally: lack established revenue streams and secured cash flow; are rich in intellectual property (IP) but have little tangible collateral; and will take time to deploy their results in terms of both marketable products and financial returns.”
That combination affects both the amount and the structure of funding. A prototype can consume cash without producing recurring revenue, and intellectual property may be valuable but difficult to use as collateral for a conventional loan. Investors must finance the company through technical and commercial milestones that may be months or years apart.
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Funding instruments and a milestone-based plan
Grants and public programs for technical de-risking
Research grants and public innovation programs can fund feasibility work, prototypes, testing and other activities that are difficult to finance with ordinary venture capital. Non-dilutive money can extend runway without immediately issuing shares, but grants have defined eligibility, reporting and expenditure rules. Founders should confirm the current geography, company-size limits, eligible costs, co-funding requirements and application dates for each program.
The European Innovation Council (EIC) framework illustrates a blended approach: a public grant can support development while patient equity finances later commercialization. It is an example of a structure, not a guarantee that every applicant receives both components.
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Specialist deep-tech venture capital
Specialist investors can evaluate scientific evidence, intellectual-property strategy, regulatory pathways and manufacturing plans that generalist investors may not be equipped to assess. Their value is not only a cheque; relevant industrial relationships, pilot customers and hiring networks can shorten the route from prototype to production. Founders should still test an investor’s reserve policy, decision authority, follow-on capacity and experience with the company’s particular technology.
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Strategic corporate partnerships
A manufacturer, healthcare company, energy firm or other strategic partner may provide paid pilots, equipment access, development funding, distribution or a path to acquisition. The trade-off is potential dependence on one customer and contract terms that affect exclusivity, intellectual-property ownership or future financing. Put milestone acceptance criteria and rights to use resulting data in writing.
Venture debt after de-risking
Debt can reduce immediate equity dilution, but it creates repayment obligations. It is generally more appropriate after the company has meaningful technical and commercial de-risking, dependable financial controls and a credible path to repayment. Using debt to fund an uncertain laboratory program can force a distressed equity raise or insolvency if a milestone slips.
Growth equity and later financing
Once a deep-tech company has validated its technology, secured customers and demonstrated a route to repeatable production, growth equity can finance capacity, sales and international expansion. At that stage, the investor conversation looks more like a scale-up case, but manufacturing working capital and regulatory commitments can still make cash planning more complex than in software.
A practical financing sequence
- Define the next value-creating milestone. State what will be proven, by whom, under which conditions and by what date. Examples include independent performance testing, a production-yield target, a regulatory submission or a paid pilot.
- Cost the milestone completely. Include personnel, facilities, equipment, materials, testing, insurance, legal work, quality systems and contingency. Separate one-time capital expenditure from recurring operating costs.
- Match instrument to risk. Use grants where the work has public or research value, equity for uncertain development and market-building, strategic money where a partner adds distribution or manufacturing capability, and debt only when repayment risk is manageable.
- Set an evidence gate. Agree in advance what result supports the next round, what result triggers a redesign and what result ends the program. This prevents fundraising from becoming a substitute for technical decision-making.
- Raise before the cash deadline. Deep-tech rounds can take longer because investors need technical diligence and specialist co-investors. Start the process while the company has enough runway to handle a delayed close.
- Preserve optionality. Keep intellectual-property ownership, data rights, supplier alternatives and customer contracts compatible with later investors and strategic exits.
How long fundraising and commercialization take
BCG reported the following average intervals between financing stages in its comparison of deep tech with other technology ventures:
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| Financing interval | Deep tech | Other technology ventures |
|---|---|---|
| Seed to Series A | 20 months | 15 months |
| Series A to Series B | 24 months | 17 months |
| Series B to Series C | 25 months | 20 months |
| Series C to Series D | 24 months | 23 months |
| Seed to Series D (estimated total) | About 95 months | About 75 months |
These are figures from BCG’s sample, not a timetable for every sector or company. McKinsey likewise says progression from deep-tech seed to Series A is often about 12 months longer than in traditional technology, while later rounds can accelerate as engineering and commercialization mature. A company that reaches reliable production and customer adoption may therefore experience a different pace after its early technical work.
What creates the delay
- Sequential validation: Performance must be demonstrated in conditions that matter to customers, not only in a laboratory.
- Manufacturing scale-up: A working prototype may require new tooling, qualified suppliers, process controls and yield improvements.
- Regulatory work: Testing, documentation and review can be prerequisites for selling in a target market.
- Customer discovery alongside engineering: The product may change as buyers, integrators and channel partners clarify requirements.
- Specialist diligence: Investors often need outside experts to evaluate claims, intellectual property and competitive defensibility.
SaaS can shorten the first market feedback loop, but an apparently quick launch does not equal a quick path to durable revenue. Enterprise procurement, integrations, security reviews and implementation work can lengthen sales cycles, while retention data may take several renewal periods to become meaningful.
Risk comparison: different failure modes, not “high” versus “low”
| Risk area | Deep-tech exposure | SaaS exposure |
|---|---|---|
| Technology | Core science or engineering may fail to meet performance, reliability or cost targets. | Software may fail to deliver a compelling workflow or scale reliably. |
| Physical operations | Manufacturing yield, equipment, materials and supply chains can constrain delivery. | Cloud capacity and vendors matter, but there is usually no product factory to finance. |
| Regulation | Approvals, safety evidence and quality systems may be required before sales. | Privacy, security and sector rules can delay deals or add compliance cost. |
| Market adoption | Customers may need to change processes, infrastructure or budgets to adopt a new technology. | Customers can switch products readily, making differentiation and retention critical. |
| Financial evidence | Revenue and acquisition metrics may be unavailable before commercialization. | Recurring revenue, retention and acquisition data can appear earlier but may be distorted by discounts or services. |
| Funding risk | Long milestones increase exposure to a funding gap before the next technical proof. | Fast hiring and sales spending can create a cash shortfall if growth or retention misses plan. |
The World Bank notes that deep-tech solutions may require three or more key technologies to work together and that development timelines are less predictable. That uncertainty compounds financing risk: a delay in one subsystem can postpone testing, customer pilots and the next fundraise. SaaS avoids much of the laboratory and physical-asset risk, but platform dependence, cyber incidents, churn and aggressive competitors can still produce severe losses.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Returns: what the available figures do—and do not—say
McKinsey reports that, since 2003, deep-tech investments delivered a superior net annual average internal rate of return (IRR) of 16% after all fees and costs, compared with 10% for traditional technology investments. This is a portfolio-level result from McKinsey’s cited analysis. It is not a forecast for an individual startup, fund or investment round, and it does not mean every deep-tech company outperforms SaaS.
The OECD describes venture-capital targets of 35% to 40% IRR for fast-growing companies with large markets and sustainable competitive advantage. Those are target returns investors may seek when underwriting high-growth risk, not observed returns that a founder or limited partner should assume.
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IRR is also sensitive to timing, follow-on investment, dilution, write-offs and the method used to value unrealized holdings. Compare the definition, period, fees and stage exposure before treating two return figures as equivalent.
Exit routes and geography
Strategic acquisition and licensing can be especially important for deep tech. An industrial, healthcare or energy incumbent may value a protected technology, manufacturing capability or regulatory position before the startup has the scale expected of a public company. Licensing can generate cash and broaden distribution while leaving the startup focused on core research or specialized components.
Public markets remain an option, but the European Commission reported that in 2020 only 5% of total European Union venture-capital divestment amount occurred through IPOs, compared with 30% in the United States. Those figures describe that year and those geographies; they are not a permanent split or a prediction for a specific company. Founders and investors should model acquisition, licensing, secondary sales and public-market scenarios separately, including currency, listing and regulatory differences.
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How founders should choose investors and grants
Questions for a deep-tech investor
- Has the investor funded the relevant science, hardware, regulatory pathway or manufacturing transition?
- Can the fund support the full period between the next technical milestone and commercial scale?
- Will the investor help recruit technical leaders, qualify suppliers or secure pilot customers?
- How does the fund handle follow-on rounds when a milestone slips but the underlying opportunity remains attractive?
- What intellectual-property, exclusivity and board-control terms are standard?
Questions for a SaaS investor
- How does the investor evaluate retention by cohort, expansion, gross margin and sales efficiency?
- What level of security, privacy and reliability evidence is expected for the target customer segment?
- Does the investor understand the company’s route to market—self-serve, product-led, channel or enterprise sales?
- How much growth is expected before the next round, and what happens if acquisition costs rise?
Questions for a grant program
- Is the applicant eligible in the relevant country, sector and company stage?
- Are salaries, equipment, subcontractors, testing and overhead eligible expenses?
- Does the program require matching funds, consortium partners, milestone reports or repayment?
- Who owns the resulting intellectual property and data?
Program rules and fund mandates change. Verify current eligibility, deadlines and investment scope directly with the administering organization before committing the company’s plan to a grant or investor.
A decision framework for founders
Use the following sequence to decide whether the company’s financing challenge is primarily deep-tech or SaaS-like, or a hybrid of both:
- Identify the hardest irreversible proof. If it is a scientific, physical, manufacturing or regulatory proof, plan for deep-tech timing even if the final product includes software.
- Map the first customer value event. A signed pilot, paid deployment, recurring subscription or regulated approval should have a defined owner and acceptance test.
- List dependencies. Include laboratories, suppliers, cloud platforms, data, certifications, channel partners and specialized hires. The longest dependency usually sets the financing horizon.
- Choose the least risky capital for each phase. Non-dilutive funding can support eligible research; equity can absorb technical uncertainty; strategic capital can unlock production or distribution; debt belongs after repayment capacity is visible.
- Model dilution and downside. Show ownership after each round, the effect of a down round, the cost of delays and the consequences of abandoning a milestone.
- Keep two plans. The base case should fund the next proof and commercialization step. A downside case should specify which experiments, markets or hires pause if the round takes longer.
A hybrid company—such as industrial software coupled to novel hardware—should separate the software metrics from the physical-product milestones. This lets investors see which risks have been retired and prevents strong subscription growth from obscuring unresolved manufacturing or regulatory exposure.
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Choose deep-tech financing when the company’s value depends on proving and scaling a scientific or engineering breakthrough; expect longer milestones, specialist diligence and a blend of grants, patient equity and strategic support. Choose a SaaS-style financing cadence only when software can reach users, demonstrate retention and improve acquisition efficiency without waiting for physical production or regulatory approval. Historical portfolio evidence suggests deep tech can reward patience, but neither the 16% McKinsey figure nor any investor target guarantees a company-level outcome. The strongest plan ties every dollar to a measurable technical or commercial proof and keeps enough runway to reach the next decision point.
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