Technical Due Diligence Checklist for Startup Investments

Most investment decisions weigh the market, the founders, and the numbers in the data room. The software the whole plan depends on usually gets less scrutiny, partly because few investment teams have someone who can read a codebase. This technical due diligence checklist is the one we work through when funds and accelerators ask us to assess a startup before a round. It covers architecture, code quality, team risk, security, and IP ownership, plus what a useful report looks like and how long the process should take.

What technical due diligence covers (and what it does not)

Technical due diligence answers one question: can this product, and the team behind it, support the plan the investment assumes? The technical due diligence process examines the codebase, the architecture, the infrastructure it runs on, the engineering team and their working practices, the security posture, and who actually owns the intellectual property.

It does not tell you whether the product will find a market. It is not a financial audit, it is not legal due diligence, and it is not a line-by-line code review. A reviewer who promises to read every file is either looking at a very small product or overstating their depth. The useful work is sampling: read the critical paths - authentication, payments, data handling - trace how a feature moves from idea to production, and interview the people who built it.

It is also not a pass or fail exam. Every early-stage codebase has problems. The job is to separate problems that are normal for the stage from problems that threaten the investment thesis.

When investors should commission tech DD: stage and deal-size thresholds

There is no fixed rule, but a pattern has settled in practice:

Pre-seed: a full assessment rarely pays for itself. A structured technical conversation with the founders, ideally led by someone who has shipped software, usually surfaces enough.
Seed: a lightweight review makes sense when the technology is the thesis - a claimed AI capability, unusual infrastructure, or a product that already has paying users and real data.
Series A and beyond: a full assessment is standard, because the round is priced on the assumption that the product can scale and the team can grow around it.

On cost, published guides and provider pricing from 2025 and 2026 put due diligence for seed and Series A deals roughly in the $10,000 to $25,000 range overall, with growth-stage deals running higher. For technical due diligence specifically, UK providers publish ranges of roughly £20,000 to £150,000 depending on scope and company size. A common rule of thumb is that diligence should stay well under one percent of deal value. Treat all of these as ranges, not quotes - scope, codebase size, and timeline pressure dominate the price.

The technical due diligence checklist: architecture, infrastructure, and scalability

Start with the system as it actually exists, not the diagram in the deck:

Does the architecture diagram match what is deployed? Ask for the diagram, then verify it against the cloud console.
Where are the single points of failure - one database, one region, one cron job everything depends on?
What breaks first at ten times the current load, and can the team answer that without guessing?
Is the infrastructure defined as code, or would it be rebuilt from memory if an account were lost?
How do deploys work? Frequency, rollback path, and whether a staging environment exists at all.
What does the cloud bill look like per customer, and which line items grow with usage?
How deep does vendor lock-in go, and is it a deliberate trade-off or an accident nobody noticed?

Boring architecture is a good sign at this stage. A well-kept monolith worries us far less than a microservices estate built for imaginary scale by a team of three.

Checklist: code quality and technical debt

You cannot judge code quality from a tooling report alone, but a few checks predict most of the pain:

Are there tests where mistakes are expensive: money movement, authentication, data deletion?
Does continuous integration run on every change, and does the team actually trust it?
How old are the dependencies, and is anything critical pinned to an unmaintained version?
How much of the code was produced by contractors or AI tools without review, and can someone on the team explain every part that handles money or user data?
Is technical debt tracked anywhere, or only discovered by accident?

Technical debt itself is not a red flag. Taking shortcuts while searching for product-market fit is rational. What matters is whether the debt is known, contained, and priced into the roadmap. We wrote more about that distinction in our post on startup technical debt.

Checklist: team, process, and key-person risk

In a startup technical due diligence engagement, the interviews often tell you more than the repository:

What is the bus factor? If one person disappeared tomorrow, what stops working and for how long?
Who can deploy to production, and who holds the credentials for cloud, DNS, and payment providers?
Talk to engineers without the founders in the room. The gap between the two conversations is data.
How long does it take a new engineer to ship their first change? Onboarding time is a proxy for how much knowledge lives only in heads.
Does the hiring plan match the architecture? A plan to triple the team around a codebase only the CTO understands is a plan to stall.

Process should be proportionate to stage. We do not expect a seed-stage team to run like a bank, but we do expect code review, some form of issue tracking, and a deliberate answer to how they will grow. Our notes on scaling a startup engineering team cover what that growth usually demands.

Checklist: security, compliance, and IP ownership

This section produces the findings that most often change deal terms:

How are secrets managed? Check the repository history, not just the current state - keys committed two years ago are still keys.
Is access control real? Look for shared admin accounts, former contractors who still have access, and no offboarding process.
How is personal data handled, and does the practice match the privacy policy? For European deals, GDPR exposure is a price tag, not a footnote.
Has there ever been an incident, and how was it handled?
Did every contractor and early collaborator sign an IP assignment agreement? Deal reviews consistently list contractor code without assignment among the most common diligence findings.
Are open-source licenses respected? Copyleft code inside a proprietary product is a real liability, not a technicality.
Who owns the domains, app store accounts, and cloud accounts - the company, or a founder's personal email?

Red flags we see most often in early-stage codebases

After reviewing startups for funds and building 20+ products of our own over 18 years, the same items keep appearing:

Backups exist but a restore has never been tested. Untested backups are a hope, not a plan.
A single founder holds every production credential and there is no documented recovery path.
Features that were demoed as finished turn out to be stubs or manual processes behind a UI.
"Proprietary AI" that is a thin wrapper around a third-party API, presented as a moat.
Metrics in the pitch deck that the system has no way to measure.
Secrets in git history, shared databases between staging and production, or no staging at all.

Most red flags reshape terms rather than kill deals. Deal-terms studies from 2025 report that a large majority of private-company acquisitions now include special-purpose escrows tied to issues found in diligence; in venture rounds the same findings tend to surface as milestones, remediation budgets, or valuation adjustments. A finding only helps if it arrives with a cost attached, which is what the report is for.

What a useful DD report looks like: structure and go/no-go framing

A technical due diligence report that only lists observations is not worth the fee. The structure we deliver:

An executive summary a non-technical partner can read in five minutes.
Findings with severity ratings - critical, high, medium, low - so attention goes where it should.
A remediation estimate per finding: what it costs in time and money to fix.
A clear split between issues that should affect the round and issues that are normal post-investment work.
A go/no-go recommendation with conditions, not a hedge.

The report should also answer the technical due diligence questions an investment committee actually asks: can the product carry the growth in the model, what does it cost to fix what is broken, who could leave and sink it, and who owns the code. If those four are not answered plainly on the first page, the checklist was followed but the job was not done.

How long technical due diligence takes

Providers commonly quote two to four weeks for a standard assessment, and published guides from 2025 report rush surcharges in the range of 25 to 50 percent when investors need answers inside a closing window. In our experience the real driver is rarely codebase size. It is how quickly the startup grants access, schedules interviews, and answers follow-ups.

Our due diligence engagements for investors run in one to two weeks because the scope is fixed up front: code, architecture, infrastructure, and team interviews, under NDA from day one. A compressed timeline holds when the startup side is responsive, and we flag it early if it will not.

Where we fit

transfactor runs technical due diligence for VCs and accelerators: code, architecture, and infrastructure review plus team interviews, delivered in one to two weeks as a written report with severity ratings and remediation costs. We work under NDA as standard and are ISO 9001:2015 and ISO/IEC 27001:2022 certified. Having shipped more than 20 products ourselves, we have seen most of the failure modes on this checklist from the inside, which is what makes them recognizable from the outside. Details are on our VCs and accelerators page.