Deep-tech startups generally need more patient capital and a longer financing runway than SaaS startups. Their teams must prove difficult science or engineering, build prototypes or production capacity, navigate regulation and supply chains, and discover a market at the same time. In the Boston Consulting Group sample cited below, deep-tech companies took about 95 months from seed to Series D, compared with 75 months for other technology ventures. SaaS companies can usually test distribution, pricing and customer demand earlier, but they still face serious risks in retention, cybersecurity, sales efficiency, competition and platform dependence.
There is no authoritative, universal funding amount for either model. The sensible comparison is how capital is deployed, which milestones unlock the next round, how long each stage takes, and what type of risk investors are being paid to accept.
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What “deep tech” and SaaS mean in a financing context
Deep tech commercializes a scientific or engineering breakthrough. Examples can include advanced materials, climate hardware, robotics, biotechnology, semiconductor systems, quantum technologies and other products whose core advantage depends on difficult technical work rather than mainly on software configuration. A deep-tech company may be pre-revenue while it validates the technology, develops a prototype, establishes a repeatable manufacturing process, completes regulatory work and interviews potential customers.
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SaaS (software as a service) delivers software over cloud infrastructure, normally through subscriptions or usage-based pricing. A team can often release a minimum viable product, place it in customers’ hands and test a sales channel without building a factory or obtaining a product-specific safety approval. That earlier feedback does not make SaaS safe. Churn, intense competition, security incidents, poor pricing, long enterprise sales cycles, cloud costs and dependence on a platform can all destroy an otherwise promising business.
The practical distinction is therefore not “hardware versus software” or “risky versus safe.” It is whether the company must solve fundamental technical and physical deployment problems before the market can reliably buy the product.
How funding needs differ
Why deep tech consumes more patient capital
The European Commission says deep-tech innovation requires large amounts of patient capital because these companies generally lack established revenue and secured cash flow, hold valuable intellectual property but little tangible collateral, and need time to turn results into marketable products and financial returns. That combination limits ordinary bank lending and makes a sequence of equity, grants and strategic financing more important.
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Capital is spent on laboratory work, specialist staff, test equipment, certification, pilot manufacturing, tooling, materials, field trials, insurance and sometimes an inventory or service network. Several of those costs arrive before a customer can be charged. A technically successful demonstration may therefore be only the midpoint of the financing journey, not the point at which the company becomes self-funding.
What SaaS capital usually pays for
SaaS pre-seed and seed equity commonly funds product development, cloud infrastructure, design, initial hiring, customer discovery and go-to-market experiments. Because software can be released incrementally, the company can often measure activation, conversion, recurring revenue, retention and gross margin during the same period. Once those signals are credible, a company may raise against recurring-revenue growth, customer retention and increasingly efficient acquisition rather than against a laboratory milestone.
The exact mix still depends on the market. A self-serve product may need relatively little capital before revenue, while enterprise SaaS may require years of sales hiring, integrations, security certifications and customer procurement. A regulated or safety-critical software product can also acquire a deep-tech-like validation burden.
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Comparison of the capital problem
| Dimension | Deep-tech startup | SaaS startup |
|---|---|---|
| Primary early milestone | Technical validation, prototype or production readiness, followed by commercial proof | Minimum viable product, customer adoption and repeatable distribution |
| Early cash generation | Often delayed until technical, manufacturing and regulatory work is complete | Can begin with pilots, subscriptions or usage fees while the product is still improving |
| Collateral profile | IP-heavy with little tangible collateral, according to the European Commission | Usually asset-light, with value concentrated in software, data, people and customer relationships |
| Typical financing tools | Research grants, public programs, specialist deep-tech venture capital, strategic partnerships, venture debt after de-risking and later growth equity | Pre-seed and seed equity, followed by growth equity or other financing tied to recurring revenue and operating efficiency |
| Funding cadence | Milestone-led; technical and commercial gates may require separate rounds | Often metric-led; growth, retention, margin and acquisition efficiency influence round timing |
| Universal funding benchmark | Not established across sectors and geographies | Not established across sectors and geographies |
A founder should not copy a headline funding number from another company. The amount needed is the cost of reaching the next independently verifiable milestone, plus enough time and contingency to survive delays. A medical device, a satellite component and an industrial battery may all be called deep tech but have radically different equipment, testing and working-capital needs. The same is true for a consumer collaboration app and an enterprise security platform classified as SaaS.
How long each type takes to mature
BCG’s published figures show a consistent early and middle-stage time penalty for deep tech:
| 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 | About 95 months | About 75 months |
These are figures from BCG’s sample, not a timetable for every company. McKinsey likewise reports that moving 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. The pattern makes sense: early rounds absorb scientific uncertainty; once the design, manufacturing process and customer use case are proven, financing can become more like a conventional growth story.
Why deep-tech timelines stretch
- Technical iteration: A failed experiment can require a new architecture, material or process rather than a minor software release.
- Interdependent technologies: The World Bank notes that a solution may need three or more key technologies to work together. A delay in one subsystem can hold back the entire product.
- Physical testing: Hardware prototypes, environmental tests, reliability runs and pilot production take calendar time and cannot always be parallelized.
- Regulation and standards: Safety, quality and sector approvals can require documented evidence, audits and controlled trials before sales scale.
- Supply-chain learning: A laboratory result may use small-batch materials that are unavailable, too expensive or inconsistent at production volume.
- Customer adoption: Industrial buyers may need integration, procurement approval, site testing and workforce training before placing a meaningful order.
SaaS can shorten the feedback loop because a team can ship a change to many users quickly. Enterprise procurement, data migration and security reviews can still lengthen its sales cycle, but those constraints usually appear alongside measurable usage and revenue rather than before any market signal exists.
Risk: different stacks of uncertainty, not a simple ranking
Deep tech combines technical, manufacturing, regulatory, financing and market-adoption risk. The World Bank emphasizes that development paths are less predictable and that several technologies may need to function together. Investors may have no comparable revenue or customer-acquisition data until commercialization.
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SaaS removes much of the laboratory and physical-asset risk, but the remaining execution risks are material. A product can attract trials and still lose customers to churn; a low price can produce unprofitable growth; a security breach can end an enterprise pipeline; and a cloud or distribution platform can change its rules or economics. Calling SaaS “low risk” hides the factors that actually determine outcomes.
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| Risk area | Deep tech exposure | SaaS exposure | What an investor should test |
|---|---|---|---|
| Technology | Core feasibility, reliability and integration of multiple technologies | Product performance, scalability and integration with existing systems | Independent test data, failure modes, roadmap and technical talent |
| Manufacturing and delivery | Tooling, suppliers, yield, quality control, inventory and field service | Cloud capacity, support operations and implementation resources | Unit economics at realistic volume and evidence of repeatability |
| Regulation | Potentially extensive approvals, testing and documentation before sale | Security, privacy and sector-specific compliance | Named requirements, responsible owners and a dated approval plan |
| Market adoption | New category education, long pilots and integration into physical workflows | Competition, switching costs, procurement and customer retention | Reference customers, buying process, renewal evidence and objections |
| Financing | Large pre-revenue spend and risk of a round arriving before a milestone | Risk of raising to subsidize inefficient acquisition or weak retention | Runway to the next proof point and a downside financing plan |
Returns, dilution and exit routes
What the historical return figure does and does not say
McKinsey reports that, since 2003, deep-tech investments delivered a superior net annual average internal rate of return of 16% after fees and costs, compared with 10% for traditional technology investments. This is a portfolio-level result from the analysis McKinsey cites. It is not a forecast for an individual startup, a guarantee that every deep-tech fund outperforms SaaS, or a promise to founders that their ownership will be worth more.
The OECD describes venture-capital targets of 35% to 40% IRR for fast-growing companies with large markets and sustainable competitive advantage. A target return is a fund-selection and portfolio-construction hurdle, not an expected return for each investment. A small number of very large exits can drive an entire fund’s result while many companies fail or return little.
Why deep tech can support large outcomes
A defensible scientific breakthrough can create high barriers to entry, valuable patents, specialized know-how and a strategic position in a large industrial market. If the product becomes a standard component or enables a new market, the eventual revenue base can be substantial. That upside compensates investors for years of technical and commercial uncertainty.
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Exit routes to examine
- Strategic acquisition: An established manufacturer, pharmaceutical company, energy firm or technology platform may value the IP, team or production capability.
- Licensing: The startup can license a validated invention while a larger partner handles manufacturing and distribution.
- Secondary or growth transaction: Later investors may buy early shareholders’ stakes when the company has meaningful commercial traction.
- Public listing: Possible for companies with sufficient scale, governance and investor demand, but timing and geography affect feasibility.
For SaaS, acquisitions and public listings are also possible, but recurring revenue, net retention, gross margin and predictable sales performance usually make valuation comparisons easier before an exit.
Who funds deep tech and when
Research grants and public programs
Grants are especially useful before revenue because they can fund feasibility work without immediate equity dilution. Public programs may support laboratory research, prototypes, demonstrations, workforce development or first-of-a-kind deployment. Eligibility, matching-fund rules, eligible costs and application dates vary by country and program; verify the live terms directly with the administering body before relying on an award in a cash plan.
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Specialist deep-tech venture capital
Specialist investors can assess scientific evidence, recruit technical advisors and tolerate longer holding periods. In a diligence process, ask how the fund defines a technical milestone, how much follow-on capital it reserves, whether it has funded regulated manufacturing and which exit routes it has actually used.
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Strategic corporate partnerships
A corporate partner can provide paid pilots, test facilities, distribution, engineering support or a route into a regulated supply chain. The trade-off may include exclusivity, rights to improvements, customer concentration or a lower valuation. Put those provisions next to the cash value when comparing offers.
Venture debt and growth equity
Venture debt is generally more appropriate after technical and commercial risk have been reduced and the company can support repayment under a downside case. Growth equity fits a later stage when production, revenue and market demand are more predictable. Using debt to fund an unresolved scientific question can turn a delay into a solvency crisis.
The European Innovation Council framework illustrates a blended approach that combines a public grant with patient equity. It is an example of financing design, not a guarantee of eligibility or an endorsement of a particular application outcome.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A milestone-based financing plan for deep tech
- Define the next proof point. Write the technical result, test standard, customer evidence or regulatory decision that would materially reduce risk. Avoid vague goals such as “continue development.”
- Cost the work package. Include people, equipment, materials, external laboratories, certification, insurance, tooling, travel, overhead and contingency. Separate one-time capital expenditure from recurring operating expense.
- Map dependencies. Identify which experiments, suppliers, approvals and customer pilots must happen in sequence and which can run in parallel. Use the longest dependency chain to set the funding window.
- Match the instrument to the risk. Use non-dilutive grants for eligible research, equity for uncertain development and commercialization, strategic money for access to facilities or customers, and debt only when repayment is supportable.
- Raise beyond the headline milestone. A round should cover the milestone, the time needed to measure it and a credible next financing process. A technically successful test is not useful if the company runs out of cash before it can repeat the result or win a customer.
- Prepare investor evidence. Keep a traceable record of test conditions, negative results, intellectual-property ownership, manufacturing assumptions, customer interviews and regulatory correspondence.
- Reprice dilution against time. Taking more equity early may reduce later financing risk; taking too little can force an emergency round after a delay. Model ownership under several timelines rather than assuming the best case.
A practical SaaS financing cadence
- Pre-seed: Validate the problem, build a narrow product and test whether a specific customer segment will use it.
- Seed: Establish repeatable onboarding, early revenue or usage, a credible pricing model and evidence that customers stay engaged.
- Series A and later: Show that growth can scale with improving retention, gross margin and acquisition efficiency. Enterprise products should also document implementation time, pipeline quality and security controls.
- Each round: Fund the next measurable growth stage, not an indefinite expansion of headcount. Keep enough runway to learn from a failed channel or pricing experiment.
SaaS founders should avoid treating recurring revenue as proof of a good business by itself. Revenue acquired at an unsustainable discount, with high support costs or weak renewals, can increase the amount of capital required rather than reduce it.
How to decide which model fits an investment thesis
Choose a deep-tech path when
- The core advantage depends on a defensible scientific or engineering result.
- The founding team can access the laboratories, specialist talent and facilities required to validate it.
- Customers have a costly problem that justifies pilots, integration and a longer buying process.
- The company can identify grants, strategic partners or specialist investors for the pre-revenue period.
- Founders and investors can tolerate milestone slippage and a longer path to liquidity.
Choose a SaaS path when
- The customer problem can be solved with software that can be shipped and improved quickly.
- The team can reach a defined market without owning physical infrastructure.
- Usage, conversion, retention and margin can be measured early enough to guide spending.
- The sales motion and security requirements are understood for the target segment.
- The company can demonstrate a path to efficient recurring-revenue growth rather than growth purchased with discounts.
Some companies combine the models. A software layer controlling specialized equipment, for example, may have SaaS-like recurring revenue but still depend on hardware certification and manufacturing. Investors should underwrite each risk stack separately instead of applying a generic software or hardware multiple.
Common financing mistakes and how to avoid them
Using a universal funding number
Published startup-round averages rarely describe the equipment, geography, regulation or stage of a particular company. Build a bottom-up budget to the next milestone and show the assumptions.
Calling a prototype a product
A demonstration in controlled conditions may not prove reliability, manufacturability, safety or customer value. State exactly what has been tested and what remains unproven.
Raising before the evidence is legible
Technical claims should be supported by test protocols, independent results where appropriate, intellectual-property status and a clear explanation of the remaining failure modes. Commercial claims need named customer problems, buying steps and evidence of willingness to pay.
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Grants can end before certification, production scale or sales begins. Plan the equity or strategic round that follows the grant and identify the milestone that makes that round possible.
Comparing IRRs with a company forecast
A portfolio IRR includes winners, write-offs, entry prices, exit timing, fees and cash flows across many investments. Do not present it as the expected return on one startup or as a substitute for scenario analysis.
Underestimating SaaS execution risk
Track cohort retention, gross margin, acquisition payback, security exposure and concentration in a cloud or distribution platform. A fast launch does not compensate for customers who do not renew.
The decision in one sentence
Deep tech is usually the better fit when a defensible technical breakthrough justifies a long, milestone-funded route to a large market; SaaS is usually the better fit when software can reach customers and produce reliable usage and revenue signals early. Deep tech requires more patience because validation, production and adoption arrive together, while SaaS offers faster feedback but remains exposed to fierce market and execution risk. Historical deep-tech portfolio returns are encouraging, yet they should influence portfolio construction—not override company-level evidence, financing discipline and a realistic exit plan.
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