Between roughly 2017 and 2023, the case for moving lab infrastructure to the cloud became the default answer. Cost curves favored it. Hyperscaler capacity was expanding faster than most organizations could build on-prem, and cloud's OpEx consumption model was financially attractive against on-prem CapEx commitments with long depreciation cycles. Vendors were actively steering customers toward SaaS. Labs that resisted were often told, correctly for the time, that they were fighting the current.
That era of clean deployment calculus is ending. The conditions that made cloud the obvious answer have shifted enough that, for some labs, the analysis they ran three or five or seven years ago no longer holds without revisiting. The question isn't whether cloud was right then — for many labs, it was. The question is whether the deployment decision you made under one set of conditions still matches the reality you operate in today.
Molecular workflows are complex systems of systems. Any decision in the end-to-end chain needs to reflect that — and deployment is one of the decisions that touches every layer of the chain at once.
This piece is not an argument that on-prem is right and cloud is wrong. Both have real, defensible use cases for different customers. The purpose of this guide is to give lab leadership a structured way to re-run the analysis for their specific context — without defaulting to the conclusion they reached in a different environment.
I'm not an IT architect, and this is not a piece about how to configure a network or build a disaster recovery plan. What I can offer — from a career spent at the intersection of lab operations and software product management — is a structured way to think about the tradeoffs before your IT team owns the build. The goal is to help you ask the right questions, not to second-guess anyone's technical execution.
What's changed
Four forces have moved since most regulated labs last evaluated their deployment strategy.
The gap between cloud total cost of ownership (TCO) and on-prem TCO for steady-state workloads has narrowed meaningfully. The cloud-is-always-cheaper narrative has been publicly challenged in adjacent sectors — Andreessen Horowitz's widely-cited "The Cost of Cloud, a Trillion Dollar Paradox" (Wang and Casado, 2021) argued that cloud economics invert at scale for predictable workloads; 37signals publicly repatriated from AWS in 2022–2023 citing multi-million dollar annual savings; Dropbox disclosed roughly $75M in infrastructure savings in its 2018 S-1 from an earlier partial repatriation. Hyperscaler gross margins are significant, and customers ultimately pay them. For labs with predictable, high-volume, steady-state compute, the math has gotten closer. Not an inversion — but closer.
Data sovereignty and residency requirements are live conversations in jurisdictions where they weren't five years ago. National genomics programs, regional privacy frameworks, and broader geopolitical uncertainty have all pushed "where does our data physically live" from a compliance checkbox to a strategic question. This matters less for a US-only clinical reference lab with strict domestic data boundaries. It matters more — often materially — for labs operating across borders, supporting federal or defense-adjacent customers, or participating in sovereign research programs.
Purpose-built silicon has made on-prem AI inference economically viable in ways it wasn't a few years ago. For labs investing in AI-assisted variant interpretation, image analysis, or QC monitoring, the assumption that these workloads must live in the cloud is no longer automatic. Training and model development still favor cloud; inference increasingly doesn't have to. The practical challenge for most clinical labs isn't compute — it's architecture. Most labs operate on top of heavily fragmented systems: LIMS, middleware, instrument software, integration layers, and reporting tools that were never designed to present a unified substrate for new capabilities to plug into. Local inference is technically viable; getting it into a clinical workflow cleanly often isn't. For labs that have already rationalized their data and integration surface, this force is live and material. For labs still stitching together siloed systems, the hurdle is architectural, not economic.
Most regulated clinical labs that built or refreshed on-prem infrastructure in the past three to five years are mid-cycle on those investments. There is no natural forcing function pushing migration, and the CapEx pain of a forced migration is substantial. For these labs, the question is not "should we migrate?" — it's "when our current infrastructure reaches end-of-life, what's the right next move — and is our current vendor forcing that move to the cloud whether we want it or not?" The latter question is increasingly what triggers the search for flexibility in the first place.
None of these forces, individually, is decisive. Taken together, they add up to a conclusion worth taking seriously: the analysis is never settled.
Translating the environment into your decision: eight factors to weigh
The forces above describe the environment. What follows are the factors that translate that environment into decisions for your specific lab. None of them has a universal answer. Each should be evaluated against your lab's reality, and the weight each carries will differ by organization.
What kind of lab are you, and what kind of work do you do? A clinical diagnostic lab operates under different pressure than a translational research core, a commercial reference lab, a hospital-affiliated lab, or an academic core facility. Volume tier matters. SLA obligations to clinical partners matter. Payer mix matters for commercial operations. This factor isn't about regulatory compliance — it's about the operating context the deployment decision has to fit inside.
What specific regulations apply to your work and your data? CLIA and CAP for clinical work in the US. State licensure layered on top (NY CLEP, California, Florida, Washington have their own regimes). HIPAA for PHI. 21 CFR Part 11 for anything tied to FDA submissions or laboratory-developed tests. GDPR for anything touching EU subjects. Jurisdictional data residency laws increasingly entering the picture globally. More regulation doesn't automatically mean on-prem is the answer — modern cloud providers can meet most of these requirements — but it does raise the bar for how carefully you evaluate what you're handing to a third party, and what your audit posture looks like when something goes wrong.
What volume are you running today, and what shape does it take? A lab running steady-state clinical panels at predictable volume is a fundamentally different compute consumer than a lab with seasonal variation or unpredictable spikes. Cloud pricing rewards elasticity; on-prem rewards predictability. Many labs have both kinds of workload, which is often where hybrid enters the conversation most meaningfully.
Where are you in your current refresh cycle? Recently refreshed on-prem infrastructure represents committed capital with useful life remaining — migrating away from it has a real cost that should be quantified, not hand-waved. Infrastructure approaching end-of-life is a different conversation: you're making the decision fresh, without the drag of existing investment. The most honest deployment decisions happen at natural refresh points.
Do you have the in-house IT bench to run on-prem reliably in 2026? This is the factor most often undersold. Running modern on-prem infrastructure requires specific capabilities: Linux administration, database management, networking, patching, backup and disaster recovery, security operations. Layered on top of that is the integration workload labs can't opt out of — instrument connectivity, LIS/EHR interfacing, middleware orchestration, reporting pipelines — much of which falls more heavily on your IT team under on-prem or hybrid deployment than under fully vendor-managed cloud. Hospital-affiliated and mid-size commercial labs frequently cannot hire to this standard, or cannot retain talent once hired. Cloud doesn't eliminate these responsibilities, but it shifts some of them to a third party. An honest assessment of what your IT function can sustain is essential before the deployment conversation is meaningful.
Where are you physically operating, and what does that imply? Jurisdictions with strict data residency rules, countries with sovereign genomics initiatives, and regions with unreliable internet infrastructure all face different deployment math than a US lab with stable connectivity. I've worked with labs in regions where internet reliability was inconsistent enough that cloud-dependent workflows were operationally risky — it wasn't a philosophical preference, it was a practical constraint. If your connectivity can't be trusted during peak clinical hours, the deployment question changes before you've even started discussing cost.
Where is the lab going in the next three to five years? Static volume behaves differently from aggressive growth. Single-site operations behave differently from multi-site federations. Labs planning cross-border data flows, international expansion, or acquisition rollups have different constraints than labs optimizing the footprint they have. A deployment decision made for today's lab often fits poorly for tomorrow's.
What are your institution's financial goals, and what does the deployment decision do to your unit economics? For a commercial lab, you need enough margin per sample or per test to absorb the total cost of bringing compute in-house — including hardware, facilities, staffing, and the operational overhead that doesn't show up on a TCO spreadsheet. For a hospital-affiliated lab, the bar may be break-even or cost recovery rather than margin. Either way, this starts with a deep understanding of your current unit economics. Deployment decisions that look clean on a TCO comparison can still erode margin when the full operational footprint is accounted for — and decisions that look expensive on paper can pay back quickly if they unlock throughput, reliability, or reimbursement that was previously constrained.
None of these factors stands alone. A well-staffed, multi-site, growth-oriented research lab in a mature jurisdiction has a different answer than a steady-state regional clinical reference lab with a lean IT team. The point isn't the answer. It's the structured conversation.
Evaluate by vertical, not at the organizational level
One note before the summary: the deployment decision isn't monolithic. Different components of the end-to-end lab workflow carry different switching costs, different operational footprints, different data sensitivities, and different vendor dynamics. A single deployment strategy for the whole lab is rarely the right answer.
Evaluate by vertical, not at the organizational level. The right deployment for your LIMS may not be the right deployment for your analysis pipelines, your reporting stack, or your workflow coordination layer.
Where the lines tend to fall
Without being prescriptive — your lab is not a generic lab — here is where the analysis tends to point.
Larger regulated clinical labs running steady-state volume; labs with strict SLA obligations to clinical partners; labs in jurisdictions with sovereignty or residency requirements; labs with mature in-house IT operations; labs with sufficient margin per test to absorb infrastructure costs; and labs in regions with unreliable connectivity or cross-border data flow constraints.
Mid-size commercial labs that span both profiles — steady-state clinical work alongside growing research or AI initiatives. The question for these labs is not "cloud or on-prem," it's "which components live where, and why."
Research and translational labs with variable or burst workloads; labs with small IT footprints that cannot sustain on-prem operational complexity; labs with multi-site federation as a core workflow; labs pursuing aggressive scaling where CapEx planning can't keep pace; and labs where cross-institutional collaboration is structurally embedded in the science.
Re-run the analysis
The purpose of this guide is not to change your deployment. It's to make sure you've revisited the decision with current inputs.
Most labs I've worked with last evaluated cloud versus on-prem under materially different conditions — different cost curves, different geopolitical assumptions, different vendor dynamics, different local compute economics, different operational staffing realities. The analysis that produced the right answer in 2020 or 2022 may not produce the right answer in 2026. Sometimes it still does. Sometimes it doesn't. Either way, the exercise is worth running.
The pendulum is always in motion. Don't migrate on momentum in either direction. Make the decision on merits, with current inputs, for the lab you actually are.
- Wang, S. & Casado, M. (2021). The Cost of Cloud, a Trillion Dollar Paradox. Andreessen Horowitz.
- 37signals / DHH. Public writings on cloud repatriation, 2022–2023.
- Dropbox, Inc. Form S-1 Registration Statement (2018). Infrastructure optimization disclosures.
Founder, HelixWrks Advisory. 12+ years spanning clinical genomics data, molecular IVD implementation, and software product strategy across LIMS, lab automation middleware, LIS/EHR integration architecture, and end-to-end workflow strategy. Decision-point only — not implementation, configuration, or integration execution.