DEV Community: Mir Zikir

What Actually Drives the Cost of Scaling a SaaS Product

Mir Zikir — Mon, 27 Jul 2026 10:32:21 +0000

Two proposals arrive for what reads like the same brief. One is three times the other. Neither team is lying.
This happens constantly, and founders usually resolve it by assuming the expensive one is padded or the cheap one is inexperienced. Sometimes that is true. More often the two teams are pricing different amounts of work, because they made different assumptions about what they will find when they open your system, and neither wrote those assumptions down.
Here is what actually moves the number.
1. The architectural starting condition
This is the single largest variable and it is invisible in a brief.
Two products can both work, both serve real customers, and both look identical from the outside, while one is three times more expensive to extend. The difference is whether the original architecture anticipated the direction you are now going.
A system built with a single enforcement point for tenant isolation can change its isolation model as a project. A system where isolation depends on every query including the right filter has to be audited line by line first, and the audit is the expensive part, not the change.
Any team quoting without inspecting this is quoting a guess. Any team that inspects it and then explains what they found is telling you something worth more than the quote.

2. How much of your data model is load-bearing debt
Not all technical debt costs the same. Debt in a feature nobody uses is free. Debt in your data model is the most expensive kind of debt there is, because everything sits on top of it and changing it means migrating live customer data without losing any of it.
The specific things that drive cost here: identifiers that were never designed to be stable, denormalised data that has drifted out of agreement with its source, columns holding several different meanings depending on a status field, and the absence of any historical record so you cannot reconstruct what a value used to be.
A team that opens your schema and goes quiet is not being difficult. They have just found where the budget is going.
3. The integration surface
Every external system your product touches is a source of cost that scales non-linearly. Not because integrations are hard to write, but because each one is a dependency you do not control, with its own rate limits, its own failure behaviour, its own versioning schedule, and its own support responsiveness when something breaks at your busiest hour.
Five integrations is not five times the cost of one. It is five times the surface area plus the coordination cost of failures that involve more than one of them at once.
When you compare two proposals, check whether both teams counted the same integrations, and whether either of them asked what happens when each one is unavailable.
4. Compliance, and when it arrives
Compliance requirements are cheap when designed in and very expensive when retrofitted. This is the most predictable cost surprise in the entire category.
Audit logging is the clearest example. Threading an audit trail through a write path that was not built for one means touching every operation that changes state. If you have a hundred such operations, that is a hundred changes, each needing review and testing, before you have a single new feature to show a customer.
Data residency has the same shape. So does the right to delete, if nobody designed for it and personal data has propagated into caches, logs, backups and analytics.
If you know an enterprise customer is coming, the compliance work is cheaper now than it will ever be again.
5. Decision latency on your side
The cost driver nobody puts in a proposal, and one of the largest in practice.
Engineering work stalls waiting for decisions. Which of these two behaviours is correct. Do we support the legacy import format. Who signs off on the new permissions model. Every day a decision sits unresolved is a day of paid capacity producing less than it could.
Teams that have done this before build decision points into the plan and name who owns each one. If a proposal in front of you does not identify who on your side decides what, that is a risk you are carrying without pricing it.
Why the hourly rate tells you almost nothing
An hourly rate is a measure of what an hour costs, not of how many hours the work takes. Two teams at very different rates routinely arrive at similar totals, because the more experienced team has seen the problem before and does not spend a fortnight discovering it.
Worse, hourly billing puts you and your supplier on opposite sides of the same question. Every hour saved is revenue lost for them. Nobody in that arrangement is deliberately dishonest, but the incentive is pointed the wrong way, and incentives win over intentions on a long engagement.
What to ask instead of the rate: what is the outcome, how is done defined, what happens if it takes longer than estimated, and who carries that. Those four answers tell you more about a supplier than any number on the first page.
How to compare two proposals honestly
Check whether both teams inspected the system or only read the brief. Only one of those is a quote.
Check whether the scopes actually match. Line them up item by item. They usually do not, and the difference is often the entire price gap.
Check what each proposal assumes it will find. If neither states its assumptions, ask. The answer is revealing.
Check who owns architectural decisions, and whether decisions get documented as they are made or explained at the end.
Check what is excluded. The exclusions list is more informative than the inclusions list.
The question worth asking before the budget question
Before you ask what it costs, get a defensible answer to what condition the system is actually in. Without that, every number you receive is a guess wearing a spreadsheet, and you have no basis for choosing between them.
A rough way to start is to put a number on your current debt position yourself. It will not be precise. It will be considerably more useful than a feeling, and it will change the quality of the conversation you have with any supplier afterwards.

Technical Debt in SaaS Companies: Why It's a Runway Problem, Not a Code Problem

Mir Zikir — Sat, 04 Jul 2026 08:45:21 +0000

Technical Debt in SaaS Companies: Why It's a Runway Problem, Not a Code Problem

Most founders find out they have a technical debt problem the same way. Not from an engineer raising a flag in standup. From a slipped deadline they can't fully explain.

A feature that should have taken two weeks takes six. A new hire spends their first month just figuring out why the codebase works the way it does, if it works at all. An enterprise prospect asks for SSO and the answer comes back: three months, not three weeks. The founder starts asking why, and the honest answer is usually some version of "we built this fast in year one, and we never went back."

That's technical debt. Not a code quality problem. A compounding tax on every sprint from here forward, and the bill comes due at the worst possible moments: the fundraise, the enterprise deal, the acquisition conversation.

We've sat on the other side of three acquisitions now, QFix's acquisition by Pinelabs, Carebeans' acquisition by QCS, and Fissara's acquisition by the McAuliffe Group in 2023. In every one, technical debt showed up somewhere in the process, either as something we'd already dealt with before diligence started, or as a line item that shaved value off the deal. That's the perspective this post comes from. Not theory. What actually gets checked, what actually gets discounted, and what actually needs to happen before it does.

The problem founders actually have

Here's the pattern we see over and over with funded SaaS founders. Product-market fit is starting to show up. Revenue is real. The board is happy. And underneath all of that, sprint velocity is quietly dropping.

It rarely shows up as a crash. Nobody wakes up to a server on fire. It shows up as slower releases. As growing coordination overhead between engineers who all touch the same fragile part of the codebase. As roadmap slippage that gets blamed on "prioritization" when the real cause is that every new feature has to route around three years of shortcuts. By the time the architecture is visibly broken, in the sense that something actually goes down, the damage has usually been compounding for a year or more.

Non-technical founders feel this acutely and describe it badly. They know something is wrong because velocity doesn't match the size of the team anymore. They ask their engineers what's going on, get an answer involving the word "complicated," and nod along because they don't have the vocabulary to push further. That's not a knock on non-technical founders. It's a structural problem: the people best positioned to see technical debt accumulating are the same people with the least incentive to flag it loudly, because flagging it means admitting some of it was their own shortcut.

Meanwhile the board doesn't ask about technical debt in board meetings. They ask about growth. So it sits there, invisible on every internal report, until it shows up somewhere that isn't invisible: a due diligence data room, a lost enterprise deal, a Series B term sheet that comes in lower than expected.

There's an emotional layer here too, and it's worth naming honestly because it's part of why the problem persists. Founders who raised a serious round carry a specific fear underneath the day-to-day: the fear of being the founder who burned the raise on the wrong things. Every sprint spent fighting the codebase instead of shipping feels like evidence for that fear, which makes founders less likely to stop and address it properly, and more likely to keep pushing forward and hope it resolves itself. It rarely does. And for non-technical founders specifically, there's a second layer: a quiet worry that they're not equipped to even judge whether their engineering team's explanation for the slowdown is legitimate or convenient. That's not a reason to distrust your engineers. It's a reason to get an outside, structured read on what's actually happening, because "trust me, it's complicated" is not a plan, and it's not something a board or an acquirer will accept either.

Why this actually happens

Every SaaS company with an MVP has technical debt, and that's not a criticism. Building fast and validating an idea before it's fully engineered is the correct call at the earliest stage. The mistake isn't taking on debt. The mistake is not knowing you took it on, and not having a plan for when it gets paid down.

Three root causes show up constantly:

The MVP was built to validate an idea, not survive a growth stage it hadn't earned yet. Founders build for the users they have, not the users they're raising money to acquire. That's rational. It's also why the architecture cracks exactly when the round closes and the team scales.

Nobody owns the debt. Product owns the roadmap. Engineering owns delivery. Nobody owns "the health of what we already built." Without ownership, debt only gets addressed reactively, after it's already caused a missed deadline or a lost deal, which is the most expensive time to address anything.

Founders don't have a way to translate technical debt into financial terms, so it never competes fairly against new features on the roadmap. "We should refactor the billing module" loses to "we should build the feature the biggest prospect asked for" every single time, because one of those has a dollar figure attached and the other doesn't. Until debt gets a dollar figure too, it will always lose that argument, right up until it costs far more than the refactor would have.

A framework for actually thinking about it

The most useful mental model we've found, the one we use in our own architecture reviews, is effective engineering capacity. Take your total engineering hours per sprint. Subtract the hours spent on maintenance, firefighting, and working around known fragile systems. What's left is what you're actually shipping new value with.

Healthy SaaS teams run effective capacity above 60 to 70 percent. We've walked into teams running below 40 percent, meaning more than half the team's time was going into standing still. At that point, hiring more engineers doesn't fix velocity. It just adds more people fighting the same fire, which is a specific kind of founder mistake: mistaking a debt problem for a headcount problem, and spending real money to solve it the wrong way.

A second concept worth every founder knowing: bus factor. It's the number of people who could disappear tomorrow before a critical system becomes unmaintainable. A bus factor of one, meaning one engineer holds the only real understanding of a core system, isn't just an operational risk. In our experience, it's one of the fastest ways to lose 20 to 40 percent off a valuation during diligence, because acquirers price in the real cost and risk of that person leaving.

A third, less discussed but just as expensive: orphan code, meaning code written by someone no longer at the company, especially from an earlier outsourced or contractor build. It's expensive for two reasons. Nobody fully understands it, which is an integration risk. And IP assignment on it is often murky or missing entirely, which acquirers treat as a deal-killer, not a discount item. We've seen technical due diligence stop cold over this exact issue.

Put together, this is how sophisticated buyers actually evaluate a SaaS codebase: not "is the code clean," but "what is our effective capacity if we buy this, what disappears if key people leave, and do we actually own what we're paying for."

What this looks like in a real acquisition

QFix is the clearest example we can point to. Before Pinelabs acquired it, the platform needed to support a genuinely multi-tenant environment at scale, onboarding institutions and merchants without every new customer requiring custom engineering work. That's a classic technical debt fork: build it properly once, or keep patching a single-tenant assumption for every new client. We built the modular architecture early enough that it held through 12 major releases and 50 minor releases over three years, every one on time and within budget. Security compliance, specifically PCI-DSS certification, was built into the platform from the start rather than retrofitted, which alone saved roughly 20 percent of development effort that would otherwise have gone into after-the-fact remediation. That kind of foundational decision doesn't show up on a pitch deck. It shows up nine months later when the platform is already serving 2 million users across 1,000-plus institutions without a rebuild.

Fissara is the inverse case, a company we joined mid-flight rather than from day one. When we started working with them, the platform was running multiple separate environments for multiple customers, a single-tenant pattern that had quietly become the single biggest driver of their infrastructure cost and the single biggest drag on development speed. Every new customer meant a new environment. Every new environment meant more surface area to maintain. We rebuilt it into a genuinely multi-tenant architecture, which cut infrastructure cost, brought the technology stack up to a standard buyers actually expect to see, and reduced go-to-market time for new customers by 30 percent. That work happened years before the McAuliffe Group acquired Fissara in 2023, and it's exactly the kind of debt that, left alone, would have shown up as a valuation discount instead of a clean deal.

Neither of these stories is about writing more elegant code. Both are about founders and engineering leaders making a deliberate call about which debt to pay down before it got expensive, instead of finding out the hard way during diligence.

The action plan: what to actually do about it, in order
If you're a funded founder reading this because velocity feels off, here's the sequence that actually works, not a generic "do a refactor sprint" suggestion.

First, get a real number, not a feeling. Run a two-week technical debt audit. The output should be a severity map, not a vibe. You want to know your effective engineering capacity, your bus factor on every critical system, and whether your IP assignment is clean across every contributor, current and former. Most founders skip this step because it feels like overhead. It's the single highest-leverage 80 hours you can spend, because everything after it depends on knowing what you're actually dealing with.

Second, fix documentation before you fix code. This sounds backwards, but it's the fastest lever available. Missing or outdated architecture diagrams, API documentation, deployment runbooks, and on-call playbooks are consistently the number one reason smaller SaaS deals stall in diligence, not because the code is bad, but because a buyer can't verify what they're buying fast enough to stay interested. Four focused weeks producing these four artifacts can move a company from "not ready" to "in the conversation" faster than almost anything else on this list.

Third, kill single points of failure. If one engineer is the only person who understands your billing system, your auth flow, or your core data model, that's not a hiring problem to solve later, it's a valuation problem sitting on your balance sheet right now. Cross-train two engineers on every system a founder would be uncomfortable losing overnight.

Fourth, automate what's still manual. Manual deploys and thin test coverage are table stakes issues for any company above roughly a million dollars in ARR. If deploys aren't gated by CI/CD and critical paths aren't covered by real tests, an acquirer or investor will find that in week one of diligence, and it reads as operational immaturity even when the product itself is strong.

Fifth, and only after the first four, prioritize new debt paydown against the roadmap using dollar terms. Once you can say "this refactor recovers 15 percent of effective capacity, worth roughly this many engineering hours per quarter," it competes fairly against new features instead of automatically losing to them.

None of this requires stopping the business to do a giant rewrite. Every founder we've worked with who tried the giant-rewrite approach regretted it. The sequence above works because it's targeted, it's sequenced by what actually moves the needle first, and it fits inside a normal sprint cadence.

Timelines vary by how deep the debt actually goes, and this is worth setting realistic expectations on rather than promising a magic six-week fix. Companies with a handful of concentrated, well-understood problem areas can typically move from "not ready for scrutiny" to "in solid shape" in three to six months of focused, sequenced work. Companies carrying deeper structural debt, an original build that was fully outsourced with no institutional knowledge retained, or years of untouched legacy modules, are usually looking at six to twelve months. Neither timeline is a reason to panic. Both are far shorter, and far cheaper, than doing this work reactively in the middle of a diligence process with a deal on the line and a buyer's clock running instead of your own.

The bottom line

Technical debt in SaaS companies isn't a sign you did something wrong early on. Every fast-moving SaaS company carries it. The founders who get burned aren't the ones who took on debt, they're the ones who never quantified it, never assigned ownership, and found out how much it cost them at the worst possible moment, in a due diligence data room or a stalled enterprise deal, instead of on their own terms.

Every sprint your team spends fighting your own codebase is a sprint your competitor spends shipping. That's the actual cost, and it compounds quietly until someone with a checklist and a valuation model makes it very loud.

FAQ

What is technical debt in a SaaS company, exactly? Technical debt is the gap between how a system was built and how it needs to work as the company scales. It accumulates every time a team ships a shortcut instead of the fully engineered solution, usually to hit a deadline or validate an idea quickly. It's not inherently bad. Unmanaged and unmeasured, it becomes expensive.

How do I know if my SaaS company has a serious technical debt problem? Watch for slowing sprint velocity relative to team size, new engineers taking longer than expected to become productive, features that keep needing rework shortly after shipping, and any system only one person truly understands. If you can't say with confidence what percentage of your team's time goes to new development versus maintenance, that's itself a signal worth investigating.

Does technical debt actually affect SaaS valuation during a fundraise or acquisition? Yes, directly. Buyers and investors evaluate effective engineering capacity, bus factor on critical systems, and the cleanliness of IP assignment across every contributor. Documentation gaps are one of the most common reasons smaller SaaS deals stall in diligence. Concentrated knowledge in a single engineer has been shown to discount valuations by 20 to 40 percent in real deals.

Should I pause feature development to fix technical debt? Almost never, and we'd push back hard on any advice that tells you to. The founders who try a full stop-and-rewrite approach usually regret it. The right move is a targeted, sequenced remediation plan that runs alongside your normal roadmap, starting with documentation and single points of failure, which deliver the fastest return with the least disruption.

When should a SaaS founder start thinking seriously about technical debt? The moment sprint velocity stops matching team size, or the moment a fundraise, enterprise deal, or acquisition conversation is realistically 6 to 18 months out. Technical debt is far cheaper to address on your own timeline than on a buyer's or investor's.