Build vs Buy Decisions for Cloud Platforms

When deciding whether to build in-house or adopt an external service, I weigh whether the capability is a core long-term competency, our current capacity and time to market, and whether an outside partner brings specialized expertise we lack. If institutional knowledge and tight control are critical, we build; if we need to scale quickly or free the internal team to focus on a major upgrade, we partner. One example is a client who kept their in-house team on version 2.0 while outsourcing maintenance and modernization of version 1.0 to our team. That choice aged well because it prevented the internal team from being overburdened and kept both versions on track, allowing the client to advance the new product without sacrificing stability for existing users.

Our build or adopt choice usually comes down to how quickly we can learn and improve. If an external service helps us move faster without locking us into one workflow, we adopt it. When a tool slows learning because it is rigid, we build a small internal option and improve it over time. Before committing, we also review privacy risk and the cost of leaving the tool later.

One decision that aged poorly was building our own internal task system too early. It looked efficient at first but it pulled attention away from improving how teams communicate. The tool started reflecting our old habits instead of helping us work better. We later moved to a widely used workflow platform and focused on clear standards that helped the team operate with more consistency.

I'm an SEO Content Strategist, and I use a simple rule to decide whether we should build or buy a tool. If a feature gives us a real edge over the competition, we build it. If it's just a basic tool we need to function, we buy it.
Before I make a move, I look at the total cost over two years. I compare the time spent by developers and the cost of maintenance against what a vendor would charge and the risk of being stuck with them. I choose to build in-house for our most important ideas or things that make us unique. I prefer buying ready-made tools for common tasks like email automation, where moving fast matters more than making it perfect.
Three years ago, we decided to build our own SEO dashboard rather than pay for expensive tools like Ahrefs. We wanted to save 120,000 dollars a year and have full control over how we track local search results. It was a huge success. Since we own the code, we've improved the tool five times faster than an outside company ever would. This custom setup has driven 40% of our traffic growth without any of the limits a vendor would impose.

When deciding to build in-house or adopt an external service, I start by pricing one unit of value, for example cost per signed report, and tag every run with tokens/GPU time, vector DB reads, storage, and egress so we see true cost per output and the hidden people costs. We only build when that total cost of ownership fits a clear payback window and a single product owner can meet one KPI with an error budget and a kill switch; otherwise we adopt an external service. One decision that aged poorly was optimizing solely for accuracy without a TCO metric, which led to rapid overspend as usage scaled. Since then we enforce budget guards, cap context, cache prompts, and move inference to the edge where practical to control costs.

One decision that aged well for us was refusing to build a custom reporting layer too early. We had the talent and the temptation, but we chose to wait. Instead, we first agreed on what success should look like across projects and defined how we measure a real win. Once those definitions were clear, we aligned our tracking and reporting around the same shared standards.

That order made a real difference for our team. If we build tools before agreeing on what matters, we usually lock in wrong assumptions and spend months fixing them. By waiting, we reduced rework and helped everyone speak the same language about performance. Later, when we automated parts of the workflow, the rules were already clear and consistent for every project.

The build-versus-buy decision is one of the most consequential calls a team makes, and it almost never gets the scrutiny it deserves. Most of the time, the choice is driven by ego or convenience rather than clear-eyed analysis. Engineers want to build because building is interesting. Executives want to buy because buying feels faster. Neither instinct is reliable on its own.

The framework I use comes down to three questions. Is this capability a core differentiator for our business? Do we have the team to build it and, more importantly, maintain it for years? And what's the true total cost of ownership in both directions, not just the sticker price?

If the answer to the first question is no, the conversation should be very short. Buy it. You don't need a custom-built internal tool for something that dozens of mature vendors have already solved. Your billing system, your email infrastructure, your monitoring stack—these are not where you win in the market. Every hour an engineer spends maintaining a homegrown solution for a commodity problem is an hour stolen from the work that actually creates value.

If the answer is yes, it's a differentiator, then you build. But you go in with eyes open about the long tail of maintenance, documentation, hiring, and on-call burden that comes with owning it forever.

One decision that aged poorly for us was building a custom analytics dashboard in-house. At the time, we convinced ourselves that no external tool could match our specific data model. That was technically true and strategically irrelevant. We spent four months building it with two engineers. Then we spent the next two years patching it, adding features users kept requesting, and debugging edge cases every time our data schema changed. Meanwhile, the commercial tools we'd dismissed kept shipping improvements weekly with entire teams behind them. By the time we migrated to an off-the-shelf platform, we calculated that the internal build had cost us roughly three times what the subscription would have over the same period, and the bought product was better.

The lesson was humbling. The question was whether we should keep building it. That distinction is everything.

Build if it sharpens the thing the client is paying for. Buy if it is back-office plumbing. One decision that aged well was skipping a home-built time tracker after we found gaps between hours logged and output. We moved to an external platform with reporting, and that gave us visibility without making admin software our second business.

Running an MSP for nearly 30 years, my default has always been: buy when it's infrastructure, build when it's culture. Platform decisions are reversible. People decisions aren't.

The clearest example of "buy aging well" was our Azure migration practice. Instead of building proprietary cloud tooling, we leaned fully into Microsoft's stack--Intune, EMS, Azure AD--and deployed it at scale, including a 40,000-user rollout for a major South African bank. That decision compounded into five Microsoft Solution Partner designations, which directly win us enterprise deals today.

The cultural side is where I'd push back on the "always buy" instinct. Our Dreams Program--helping employees set and achieve personal goals--was something we built entirely in-house. No vendor sells that. It's why acquired companies like Real Time Consultants and iTeam actually retained their people post-acquisition, which is where most MSP acquisitions quietly fail.

So my real framework: buy anything a vendor can maintain better than you at scale, build anything that defines *why* people choose you over the next MSP on the list.

The build versus buy decision requires brutal honesty about your team's core strengths. I've learned this through years of building technology companies, and it's clear that not every technical challenge needs an in-house solution.

When we needed a demand capture platform, I knew my team had the technical ability to build one from scratch. But that would've been a mistake. Our expertise lies in AI and generative algorithms, not platform development. Partnering with an external service provider freed us to innovate where we truly excel.

Sometimes building in-house is absolutely the right choice. At SponsorCloud, we created our own investment management platform despite several existing market solutions. Our unique business model required a level of customization that off-the-shelf products couldn't deliver.

I've found success by tightly aligning these decisions with our team's expertise and strategic goals. As our capabilities grow and market solutions evolve, I regularly reassess these choices to ensure they still serve our needs.

Our strict rule is to purchase or integrate any capability that is not one of our core differences. Creating unique infrastructure to support capabilities such as payment gateways, notification systems, or identity management is almost always a bad decision. You will end up with a lot of technical debt because you created a capability that you could have found an existing solution to use instead.

For example, we spent months building out our own notification engine because we thought we required full control. We were completely wrong. It was a huge drain on time and resources for our best engineers who could have been working on solutions that would have been of real benefit to our customers. We should have integrated a third-party service from day one. We now view our engineering time as our most expensive asset and we only invest in building capabilities that our competitors cannot duplicate easily.

Often, the smartest build decision will be the one you decide not to make. Every line of custom code you write is another line you will have to maintain, secure, and update through out time. Focus your team's energy on solving the specific problems that will ultimately make your offering unique.

I tried to build my own CRM seven years ago. It fizzled. We started it, got excited, then client work took over, and the project quietly died. The idea was right, but the timing was wrong.

Fast forward to 2025, and AI coding tools had completely changed the equation. I took another run at it, starting with small prompts in Cursor and eventually speccing out full features with Claude Code. Within months, we had a working CRM that replaced both HubSpot and FreshBooks. It handles our pipeline, client communication, and billing in one system built exactly for how we operate.

HubSpot was designed for enterprise companies with tens of thousands of contacts. We have maybe a thousand. Half the features didn't apply to us, and we were paying enterprise prices for the privilege of ignoring them. FreshBooks started as small business software but grew into something more expensive and less focused over time. We were spending well over a thousand dollars a year on tools that didn't actually fit.

But here's the thing. I'm not anti-SaaS. In the same year I built the CRM, I also tried replacing StatusCake with a custom website-monitoring tool. Got it running on eight servers, had it checking our 200+ client sites. It worked, mostly. But StatusCake has servers across the globe, sends alerts within seconds, and costs us under $100 a month. My homegrown version needed constant tweaking. A site would go down, and I wouldn't get the alert for seven minutes, even though I'd set it to five. That gap matters when a nonprofit's donation page is offline during a fundraiser.

The decision comes down to two things. First, how complex is the ongoing maintenance? Our CRM runs quietly. When a bug pops up, we fix it and move on. The monitoring tool was a different animal, always needing attention. Second, are you competing against infrastructure you can't replicate? StatusCake's global server network and dedicated engineering team aren't things I can match from my office.

The companies watching their stock prices drop right now got comfortable charging premium prices for software that a motivated team with AI tools can now build in weeks. That's a real shift. But it doesn't mean every SaaS product deserves to be replaced. It means the calculus has changed, and the honest answer is you have to evaluate each tool on its own merits.

I've had to make this call a lot scaling go-to-market from zero--both as CEO of AScaleX (global, cross-timezone delivery) and as CGO at NovoPayment, where we automated sales engines and expanded partnerships across the US + LATAM while supporting a $19M Series A (2022) and a $20M Morgan Stanley Expansion Capital investment (2024). My rule: build only if it's a compounding advantage tied to our differentiation; buy if speed-to-revenue, reliability, or integration breadth matters more than "ownership."

My decision filter is simple: (1) is it core to how we win trust/brand or close revenue, (2) can we ship a V1 in <30 days, (3) can an external tool plug into our stack cleanly (CRM + outreach + reporting), and (4) what's the failure mode--silent data rot, brand inconsistency, or workflow drag. If it touches customer experience at scale (personalization, lifecycle messaging), I bias toward proven platforms plus tight governance, because mismanagement kills performance faster than "imperfect features."

A decision that aged well: at NovoPayment we adopted external tooling for the sales/partner automation layer instead of building, so we could instrument pipeline, outreach, and attribution immediately and keep the team focused on partnerships and narrative. That speed and measurability mattered when we needed repeatable growth signals in a tough fintech market.

A decision that aged poorly: early on, I tried to "build" a content ops system out of spreadsheets + Slack conventions across time zones; it looked cheap but created version chaos and inconsistent handoffs that showed up as brand drift. Moving to a real project management system (we've used Jira successfully for complex backlogs) tightened QA, reduced rework, and made turnaround actually match the promise of rapid delivery.

With 20+ years curating unified comms and managed IT for SMBs under 200 users, we decide build-vs-buy by listing top risks like outages and growth, running 3-year TCO including downtime, then piloting with metrics like SLA uptime and resolution time.

Adopting external managed platforms aged well for a nationwide preschool takeover--we deployed speakers, voice/data network, APs, and cameras on one bill in under 10 days, on budget, versus their prior vendor's delays.

Building in-house Wi-Fi early on aged poorly; poor AP placement caused channel chaos and no monitoring, spiking tickets until we switched to cloud-managed with segmentation and telemetry for reliable coverage.

If you would have asked me this a few months ago, I'd have given a completely different answer, but now with Claude Code, all of the math on this has changed. The answer is almost always build in-house unless there is some kind of barrier to access issue.

In a very short period of time, we have been able to use Claude Code to not only create a custom CRM for our business, but also full social media, website, and SEO funnels that are 95% autonomous.

If you can build it and host it so easily, why buy it?

Josh Wahls, Founder, InsuranceByHeroes.com

Core vs. Context: The EHR Decision

As CTO of a nationwide private-pay therapy platform, my framework for the "build vs. buy" dilemma comes down to one question: Does this feature directly differentiate our concierge experience, or is it just operational plumbing?

If it is the secret sauce, we build it. If it is a solved industry standard, we buy it.

A Decision That Aged Well: Adopting an External EHR
When scaling our Management Services Organization (MSO), we needed a robust backend for scheduling, billing, and clinical notes. We could have built a proprietary patient portal to maintain total control over the code. Instead, we chose to integrate an established, third-party Electronic Health Record (EHR) system.

That decision aged incredibly well. Building and maintaining compliant healthcare infrastructure from scratch is a massive resource sink that would not have made our actual therapy services any better for the client. By offloading that operational baseline to a dedicated external service, we preserved our engineering bandwidth to focus on what actually drives our growth. It allowed us to dedicate our time to building custom AI workflows for our clinicians and executing Generative Engine Optimization (GEO) strategies to capture market share with large language models.

We bought our infrastructure so we could build our competitive advantage.

When we face a core platform need, we look at whether it is central to the user's outcomes and whether it must be tightly integrated with how the product works day to day. If it directly shapes behavior, consistency, and risk control, we prefer to build it so we can enforce the workflow and iterate quickly based on real usage. Early at Nvestiq, our first major decision was to build systems that enforce structured trade planning, predefined risk parameters, and real time visibility into cash flow and exposure, rather than treating those as add-ons. That choice aged well because it addressed the root problem we saw in early deployments: decision inconsistency under pressure driven by emotions, market noise, and fragmented data. By improving execution discipline first, we helped users protect capital and create a stable foundation before optimizing for performance.

With 20+ years designing IT infrastructure for Northeast Ohio SMBs, at Tech Dynamix we buy external services for core platforms when they deliver rapid scalability and benchmarks--like Microsoft 365 tools--reserving in-house builds for hyper-specific compliance integrations.

We weigh time-to-value, ongoing support costs, and anonymized insights against customization needs.

Adopting Microsoft's Copilot Benchmarks aged exceptionally well: it showed our clients' teams hitting 4x the industry AI pilot success rate (beyond 5% testing phase), enabling targeted Viva Insights training that boosted confidence from 33% to near-manager levels without building custom dashboards.

One poor call: early reliance on basic in-house monitoring before external proactive tools like Sophos; it led to preventable downtime until we switched, cutting response times dramatically.

As CEO of Impress Computers--a Houston MSP I've led since 1993--we decide build vs. buy for core platforms by scoring scalability, compliance demands (like GLBA for banking), and recovery speed against our 99.9% uptime SLA.

Adopting Microsoft's SharePoint and Teams over in-house file servers aged beautifully for construction clients needing jobsite sync and Procore ties.

It slashed disaster risks from on-prem failures, enabled anywhere access for 400-person teams, and cut sprawl from Dropbox/Slack--phased migrations kept production humming with zero downtime.

Running a third-generation industrial weighing company that also pioneered volumetric load scanning technology means I've had to make this call repeatedly--sometimes with real money on the line.

My framework is simple: if the need is core to our competitive differentiation, we build. If it's infrastructure we'd share with any other company in any other industry, we buy. The volumetric load scanning system we developed in-house is the clearest example of a build decision that aged well--no external vendor was solving that problem for open-top truck measurement, so we owned it and it became our identity.

The decision that aged poorly? Early on we tried to build our own rental fleet tracking and scheduling system internally. We burned months of internal resources on something that off-the-shelf field service software already solved. Switching to an external platform cut our admin overhead significantly and let our team stay focused on calibration and equipment expertise.

The honest filter I use now: "Are we building capability or avoiding a vendor conversation?" If the answer is the latter, that's usually a red flag that you're about to waste serious time.

My default is almost always "buy it" — and then I let the team argue me out of it. If it touches what makes us unique, we build. Everything else, we look for an existing solution first.
But sometimes the real answer is both.
We went through this with project tracking. As a software development provider, clients expect fast delivery without cutting corners on quality, so we need a clear picture of time versus progress. We started building our own tool from scratch. Pretty quickly we realized it was taking as much effort as what we normally build for clients — and at that point you have to ask whether the result justifies those hours compared to just paying for existing software.
So we moved to off-the-shelf — GitHub Project Boards, GitLab, Clockify for time tracking. Each solid on its own. But now our data was scattered across three systems and none of them could answer what we really needed: which tasks went beyond their estimates and why, and what performed well so we could replicate it.
That's when we built TeamRay — a small integration layer on top of all three that pulls data together into the analysis we actually care about. We didn't rebuild time tracking or project boards. We connected what already worked and added the intelligence that was missing.
The mistake was thinking it had to be one or the other. The answer that aged well: use mature tools for what they're good at and only build the piece that's truly yours.