How Hyperscalers Decide What to Build vs Buy

Understanding Hyperscalers and Their Scale

In simple terms, hyperscale data centers are massive facilities built by companies with vast data processing and storage needs (www.datacenterdynamics.com)(https://www.datacenterdynamics.com/en/analysis/what-is-a-hyperscale-data-center/). These hyperscalers – think of cloud giants like Amazon Web Services, Google, Microsoft Azure, Meta (Facebook), and their Chinese counterparts – run global cloud platforms serving billions of users. The sheer scale is staggering: according to McKinsey, hyperscalers collectively spent $185 billion on data centers in just a three-year span (www.mckinsey.com)(https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/how-high-tech-suppliers-are-responding-to-the-hyperscaler-opportunity). This scale means their decisions on whether to build technology in-house or buy from vendors can shape entire markets.

For BIM managers, architects, and engineers working on data centers or large IT facilities, understanding how hyperscalers approach the build vs buy dilemma offers valuable insight. Hyperscalers operate on a different level of volume and urgency, but the underlying logic of their choices can inform decision-making in organizations of any size. In this post, we’ll explore why hyperscalers sometimes build custom solutions from scratch, when they choose to buy or partner for off-the-shelf products, and how this dynamic is giving rise to new cross-stack platforms (like what we’re building at ArchiLabs) that bring hyperscale-like integration and automation to the broader industry.

Build vs Buy: A Strategic Decision at Scale

Every technology team faces the classic “build vs buy” question – do we develop a tool in-house or procure an existing product? For hyperscalers, this question is amplified by their tremendous scale and performance requirements. Historically, smaller data center operators would simply evaluate vendor offerings and pick the best fit. Hyperscalers, however, have a third option: do it themselves. As one industry article noted, for companies like Facebook (Meta) whose business hinges on data center excellence, “the do-it-yourself option often turns out to be the only viable option” (www.datacenterdynamics.com) (www.datacenterdynamics.com). In other words, when off-the-shelf solutions can’t meet the needs of a massive, ultra-optimized operation, hyperscalers are willing and able to invest in building their own.

This doesn’t mean hyperscalers build everything internally – they continuously weigh trade-offs. The decision comes down to strategic fit, timing, cost, and competitive advantage. Let’s look at why a hyperscaler might decide to build versus buy, and vice versa.

Why Hyperscalers Build In-House

Hyperscalers have shocked traditional tech vendors by developing custom hardware and software for their own use, from server designs to networking gear and management tools. Why undertake the costly effort of in-house development when commercial products exist? Here are some key reasons:

• Strategic Core Advantage – Hyperscalers view their infrastructure as a strategic asset, not just overhead. A cloud provider’s ability to deploy services rapidly and reliably from its data centers is its core business. By building bespoke solutions, they invest directly in what differentiates them. Microsoft, for example, even designs its data centers “from server motherboards through cooling systems” because the way they build and run infrastructure is a competitive weapon (www.datacenterdynamics.com). In other words, a hyperscaler’s unique processes and innovations in data center design can translate into better cloud services.
• Tailored to a Single Use-Case – Unlike general enterprises, a hyperscaler typically optimizes for one primary mission: delivering its cloud or web services. This narrow focus means they can customize technology to their specific workload rather than accommodate a broad market. One analysis notes that hyperscale cloud operators optimize around their singular use-case and avoid supporting features they don’t need, allowing extreme efficiency (www.linkedin.com) (www.linkedin.com). By building in-house, they can strip out all the extraneous functions that come with off-the-shelf products, creating lean solutions fine-tuned to, say, running a search engine or a social network at scale.
• Control over Roadmap and Innovation – Building internally gives hyperscalers direct control over the product roadmap and timelines. They aren’t stuck waiting for a vendor’s next release or lobbying for a feature on someone else’s platform. If Google or Amazon identify a capability they need, they can develop and deploy it on their own schedule. This agility is crucial in a hyper-competitive arena. As an analyst explained regarding AI platforms, factors like time-to-market often influence whether to “buy/partner or build” internally (www.fierce-network.com). Hyperscalers will build when they need to move faster than a vendor can accommodate.
• Intellectual Property and Secret Sauce – By innovating in-house, hyperscalers retain intellectual property and proprietary know-how. The automation, designs, and optimizations they develop become trade secrets that competitors can’t easily acquire. A Microsoft data center engineering lead famously said they build their own infrastructure software in part so they “don’t have to have that conversation” with an outside vendor, keeping their knowledge internal (www.datacenterdynamics.com). He went on to emphasize that Microsoft sees its operational data and methods as something to “keep within Microsoft’s walls”, underscoring how seriously they guard their IP (www.datacenterdynamics.com). In practice, this means everything from custom server specs to AI algorithms for cooling get treated as competitive advantage.
• Eliminating Feature Bloat – Commercial, off-the-shelf platforms often try to be one-size-fits-all. They might be laden with features to cater to many customers, including ones a hyperscaler doesn’t need. By building a tool themselves, hyperscalers can avoid unnecessary complexity or “feature bloat” in vendor products (www.linkedin.com). The result is a more streamlined system that does exactly what’s needed – nothing more, nothing less. This not only improves performance and reliability (fewer moving parts) but also makes training and operations easier for their teams.
• Cost Efficiency at Scale – At the scale of millions of servers and hundreds of facilities, even small per-unit savings add up to huge impact. Hyperscalers have found that designing their own hardware or software can dramatically cut costs by removing vendor overheads and optimizing for just the required specs. Internally developed solutions remove vendor profit margins and can be tailored for maximum power or space efficiency. Facebook’s infrastructure team, for instance, determined that a DIY approach to data center management was the most cost-effective (and indeed only viable) path for their scale (www.datacenterdynamics.com). Similarly, an industry LinkedIn analysis observed that this strategy “reduces overall data center cost”, which is vital when “every penny becomes important” at hyperscale (www.linkedin.com). In short, building in-house can lower the total cost of ownership in the long run – a critical factor when you’re operating thousands of identical units.

To sum up, hyperscalers build in-house when it allows them to move faster, focus on their unique needs, and gain an edge in cost or performance. Data centers are the heart of their business, so they invest heavily in any technology that can make their heart beat faster (or cheaper!). As Amazon’s James Hamilton often advocates, you should “build where you differentiate, buy where you don’t” – hyperscalers exemplify this, building what gives them strategic juice and happily using commodity solutions elsewhere.

When (and What) Hyperscalers Choose to Buy

Hyperscalers don’t build everything from scratch. There are plenty of areas where even the giants opt to buy, license, or partner rather than reinvent the wheel. Understanding the boundaries of when they decide to buy is just as important:

• Commodity Functions – If a need is truly commodity and does not confer competitive advantage, hyperscalers are content to use off-the-shelf solutions. For example, they might purchase standard HVAC equipment or power transformers for data centers rather than design their own, because reliable commercial options exist and offer no strategic distinction. The mantra is to outsource what isn’t mission-critical. As one tech leader quipped, “we don’t need to write our own word processor to run our business” – some tools are simply utilities. In the software realm, hyperscalers extensively use open-source components and third-party libraries in non-differentiating parts of their stack, since building those in-house would waste time.
• Mature and Fast-Moving Markets – In fast-evolving tech domains like AI, sometimes buying or partnering is the only way to stay on the cutting edge. A recent trend is cloud providers partnering with or investing in AI startups (e.g. Microsoft with OpenAI, AWS with Anthropic) to quickly gain capabilities while the technology matures (www.fierce-network.com). If developing a solution internally would take too long or if the market solution is already excellent, hyperscalers will gladly integrate someone else’s product. This might include using a proven database system, acquiring a company with a great technology, or collaborating on industry standards. The key factors here are time-to-market and quality – if buying gets them there faster (and the tech isn’t core IP they need to own), they’ll do it.
• Focusing Internal Talent Wisely – Even hyperscalers have limited top engineering talent, and they prioritize those minds on the hardest, most strategic problems. For supporting tools or peripheral systems, it often makes sense to buy a solution and free up engineers for higher-value projects. For instance, while a hyperscaler might build its own AI training chip (since that’s directly tied to cloud performance), it might buy software for internal HR or facilities management – areas that are important but not where they compete. This division of labor ensures their development efforts yield maximum competitive return.
• Interoperability and Standards – In some cases, hyperscalers buy into industry-standard solutions to maintain interoperability. For example, using standard Data Center Infrastructure Management (DCIM) software or adopting a common format (like IFC for BIM models) can make it easier to work with partners and supply chains. If a commercial tool supports open standards or broad compatibility that an in-house tool might lack, the commercial option can be more attractive. Hyperscalers will build custom only if those standards-based tools truly fall short of their requirements.
• Partnerships and Shared Effort – Sometimes the build vs buy equation tilts toward partnering, which is a bit of both. Hyperscalers may co-develop technology with a vendor or contribute to open-source communities. A great example is the Open Compute Project (OCP) initiated by Facebook: they designed custom hardware (servers, racks, power supplies) in-house but then open-sourced the designs via OCP, effectively “sharing the build” with the broader industry. By doing this, they still get equipment built to their spec (often buying it from OEM manufacturers who use the OCP designs) while offloading some development and encouraging an ecosystem around it. In practice, this blurs the line between building and buying – the hyperscaler leads the design (build) but others carry it out and even commercialize it (buy). It’s a clever strategy to get the best of both worlds: custom-fit tech with industry support.

In summary, hyperscalers will buy or partner when it makes more sense to leverage existing solutions – typically for non-core needs, when speed is paramount, or when collaborating can save effort. They are extremely pragmatic: if a vendor solution meets 95% of their needs and the remaining 5% isn’t game-changing, they’d rather adapt or live with that solution than expend resources on a from-scratch project. The build vs buy calculus is thus a constant evaluation of value: where building yields high strategic value, they build; where it doesn’t, they buy.

Real-World Examples of Build vs Buy Decisions

Nothing illustrates this decision-making better than some real hyperscaler case studies. Here are a few notable examples across different layers of the data center stack:

1. In-House Data Center Management Software (DCIM): When managing tens of millions of servers across global facilities, the software to monitor and operate all that infrastructure is mission-critical. Both Microsoft and Facebook famously decided against using traditional DCIM vendor products. Microsoft’s team reasoned that no off-the-shelf DCIM could handle their scale or unique processes, and as “the world’s biggest software company,” they felt they could build a superior solution themselves (www.datacenterdynamics.com). The manager of Microsoft’s data center engineering group explained that running their infrastructure was a strategic advantage for their cloud, so they developed a custom internal system and “all of those services...are touched by our infrastructure-management software” (www.datacenterdynamics.com) (www.datacenterdynamics.com). Facebook similarly concluded that DIY was the only viable path – its global data centers were so pivotal that the infrastructure team chose to develop a DCIM platform in-house, albeit likely integrating some third-party components (www.datacenterdynamics.com). (Facebook initially worked with a vendor on a tailored solution, but ultimately that vendor exited the market, reinforcing the decision to go internal.) These examples show that for the hyperscalers, the software glue that ties their operations together was too important to not build in-house. Traditional DCIM tools were seen as too limited or inflexible, so they wrote their own to better suit their needs.

2. Custom Network Hardware and Protocols: Hyperscalers have pushed the envelope in data center networking. A classic example is Google’s decision to build its own network switches and software rather than buying from Cisco or Juniper. Around 2005–2010, Google found that conventional networking gear couldn’t economically keep up with its traffic growth. So they engineered custom “white box” switches and a software-defined network control plane. As Google’s engineers put it, “We couldn’t buy the hardware we needed to build a network of the size and speed we needed” (www.wired.com) – so they invented it. This bold move predated the industry’s larger shift to software-defined networking and inspired many others. Google even created its own routing protocol (codenamed Firehose) alongside specialized network management software (www.wired.com). The outcome was a highly efficient network connecting Google’s data centers, which they’ve since iterated on multiple times (the famous Jupiter network fabric, etc.). Meanwhile, other hyperscalers also embraced this build approach: Amazon and Microsoft began designing network hardware and collaborating with manufacturers to produce gear to their specs, often leveraging open designs. Today it’s common for hyperscalers to buy bare-metal switch hardware from ODMs (original design manufacturers) and run their own network operating systems on it. This is a case where the build decision (creating custom networks) gave hyperscalers greater control over performance and cost, whereas buying legacy vendor gear would have kept them constrained and dependent.

3. Custom Silicon vs. Off-the-Shelf Chips: Another domain is in processors and hardware accelerators. Hyperscalers historically bought CPUs and GPUs from traditional suppliers (Intel, AMD, NVIDIA). But in recent years they’ve increasingly designed their own chips when the market wasn’t meeting their needs. Amazon’s development of the Graviton series of ARM-based server processors, and Google’s creation of the TPU (Tensor Processing Unit) for AI workloads, are prime examples. Graviton was a build decision motivated by cost and control – by designing its own CPU, AWS could optimize performance per dollar for cloud workloads and avoid the high margins of incumbent chip vendors. Google’s TPU was driven by the need for a specific AI math accelerator that GPUs at the time didn’t provide. In both cases, the hyperscalers still buy many off-the-shelf chips as well (they didn’t throw out all Intel or NVIDIA products), but they strategically built certain silicon in-house to gain an edge in core areas (cost for AWS, AI capability for Google). For less critical needs or where the market is adequate – say, general-purpose storage controllers – they’ll just use commercial chips. The pattern underscores a nuanced approach: build the crown jewels, buy the staples.

We could go on – from Amazon’s Nitro hardware for virtualization (built instead of relying on third-party hypervisors) to how Apple (though not a “hyperscaler” cloud provider) builds custom data center hardware for its services – but the message is consistent. These giants evaluate each layer of their technology stack and decide where owning the solution provides outsize benefits. Everything else, they are perfectly content to source externally or open-source in cooperation with others.

From Hyperscale to Mainstream: Bringing Cross-Stack Automation to Everyone

The build vs buy choices of hyperscalers have ripple effects on the broader industry. Most organizations will never have the scale (or budget) of an Amazon or Google to justify massive in-house development. Yet, they face many of the same challenges, just on a smaller scale – data silos between tools, repetitive manual workflows, integration headaches across software platforms, and pressure to do more with less. This is where emerging cross-stack platforms are changing the game, offering hyperscale-like capabilities as a product you can buy. ArchiLabs is one such platform, an AI-powered operating system for data center design that exemplifies this new approach.

Instead of a company having to build their own custom integrations and automation scripts (as a hyperscaler might), ArchiLabs provides a unified, always-in-sync source of truth that connects your entire tech stack out-of-the-box. It links everything from Excel spreadsheets and asset databases to modeling software and management systems. For example, ArchiLabs can integrate with your DCIM software, your analysis tools, your custom databases, and your CAD/BIM platforms (Autodesk Revit being one integration among many). All data becomes synchronized across applications – if you update a piece of equipment’s specs in one system, it automatically reflects in all others. This cross-stack data harmony is something hyperscalers often achieve with armies of developers and internal platforms; ArchiLabs delivers it as a turnkey solution.

Beyond just connecting data, ArchiLabs automates the heavy lifting in data center planning and design. Repetitive, labor-intensive tasks that BIM managers and engineers spend countless hours on can be handled by ArchiLabs’ AI-driven workflows. Think of processes like rack and row layout generation, cable pathway planning, equipment placement, and drawing production – tasks that traditionally require painstaking manual effort to ensure consistency and avoid clashes. ArchiLabs can execute these automatically based on rules or even high-level instructions. For instance, a team can instruct ArchiLabs to “layout racks in this new hall following our standard spacing and fill power/cooling capacities optimally” and the system will produce a layout in minutes, coordinating with power and cooling data. It’s not just simple macros, either. The platform employs advanced logic and AI to handle complex requirements (like ensuring redundancy, aligning with clearance guidelines, and so on) across multiple disciplines.

Crucially, ArchiLabs is built to be extensible through custom agents. This means your team can teach the system new workflows end-to-end, tailored to your specific needs – without having to dive into low-level coding. For example, you could create an agent that reads equipment inventory from an external database or API, updates a 3D Revit model with those components (placing and configuring each piece automatically), runs an analysis (perhaps checking for thermal or weight limits via a connected tool), then writes the results back to an asset management system and even generates an email report. All of this multi-step process can be orchestrated within ArchiLabs. Essentially, it’s an automation platform that can read and write to CAD files (e.g. manipulating Revit or IFC data), converse with external systems, and drive complex workflows across your entire tool ecosystem. The ability to orchestrate across different software and data formats – Excel, BIM models, databases, APIs – and keep them in sync is akin to having a “digital project manager” ensuring nothing falls through the cracks. Teams can gradually encapsulate their best practices and standard operating procedures into ArchiLabs agents, so routine processes run on autopilot and humans can focus on higher-value creative and engineering work.

The benefit of a cross-stack platform like ArchiLabs is that it offers a third option beyond the traditional build vs buy: it’s a comprehensive platform that you can buy, which is flexible enough to feel bespoke. In the past, if you wanted the level of integration and automation that ArchiLabs provides, you had to be a hyperscaler – either building custom internal software or spending heavily to stitch together various point solutions. Now, even smaller enterprises or colocation providers can achieve hyperscaler-like operational efficiency by adopting a platform that handles the integration for you. ArchiLabs essentially packages the kind of capabilities hyperscalers build for themselves (unified data environment, automation of design tasks, AI-driven insights) and makes them available as a product. It’s the cross-stack synchronization and automation layer that connects all your tools, acting as an AI “operating system” for your data center design and planning workflows.

By leveraging such a platform, BIM managers, architects, and engineers at any organization can get the best of both worlds: the power of a built-in-house system without having to develop it from scratch. You can maintain your existing tools (maybe you love the detailed modeling in Revit, or your Excel capacity trackers, or a trusted CFD analysis tool) – but with ArchiLabs, these tools become federated into one intelligent system. The result is less manual data wrangling, far fewer errors from outdated information, and dramatic time savings through automation. It allows even a modestly sized team to punch above their weight, executing projects with a speed and consistency that rivals the big cloud players.

Conclusion

Hyperscalers have pioneered a new approach to technology decision-making: relentlessly evaluating what provides unique value and building those pieces in-house, while being perfectly willing to buy or partner on the rest. Their build vs buy calculus is driven by scale, but the core principles – focus on your differentiators, control what matters most, and don’t reinvent commodity wheels – are universal. For AEC professionals and data center designers, the innovations coming out of hyperscalers (from open hardware designs to new automation tools) are enabling a leap in productivity across the industry.

Today, you don’t have to be a Google or Amazon to benefit from hyperscale approaches. By adopting modern, cross-stack platforms like ArchiLabs for automation and data synchronization, organizations can achieve many of the efficiencies that hyperscalers gain from custom-built systems – without the multi-year development effort. The future of data center design and operations will be defined by integration and intelligence: systems that connect previously siloed tools and use AI to streamline workflows. Hyperscalers decided long ago that this is worth building themselves. The rest of us can reach the same destination by thoughtfully choosing what to build vs buy – and by taking advantage of new solutions that bring hyperscale capabilities within reach as a turnkey product. In the end, the goal is the same: deliver projects faster, more reliably, and with greater insight. Whether you build or buy, the winners will be those who architect not just their data centers, but their entire toolchains, for scale, speed, and agility.