Blog Software Development Refactor, replace, or isolate? - 2026 CTO guide for modernizing legacy systems in scaleupsRefactor, replace, or isolate? - 2026 CTO guide for modernizing legacy systems in scaleups
Legacy systems in scaleups are rarely the result of negligence or poor engineering. In most cases, they are the side effect of a product’s early success as during the MVP phase, systems are built quickly to validate assumptions and keep the momentum. Those decisions are often correct at the time, but as the product moves into the next stage of the SDLC, they become a burden.
When the company reaches meaningful scale, reliability and operational efficiency start to matter more than delivery speed alone. At this stage, many teams discover that their systems encode assumptions that are no longer valid due to evolution of various areas such as customer segment, pricing models or compliance requirements.
This is typically the moment when engineering leaders feel stuck – modernization is clearly necessary, but the path forward is not. This article looks at how scaleups can evaluate modernization trade-offs in practice and decide which option suits their situation best.
Table of contents
What legacy means in a scaleup context
Technology in scaleups ages quickly because the early phase optimizes for speed and learning, not longevity. Architectural shortcuts with tight coupling are often tolerated and documentation lives only in people’s heads. All of this is rational to a certain point.
Problems appear later, when the organization grows and the cost of change increases. This leads to features that once took days start taking weeks, each deployment feeling risky and knowledge being concentrated around a few seasoned engineers. Data silos and rigid internal interfaces block new initiatives such as AI-driven features, advanced analytics, or large-scale integrations.
At this stage, the system is not failing outright, but it is quietly dictating what the business can and cannot do. This is where CTOs and leaders are forced into strategic trade-offs instead of purely technical decisions.
Refactor: evolving a system that still fits the business
Refactoring is often the least controversial option, but also the most misunderstood. In the legacy system context, it means improving internal structure, performance, and maintainability without changing how the system behaves externally. It is not about rewriting everything, but about making the existing system easier and safer to work with.
This approach makes sense when the system still supports the core business well, but suffers from growing technical debt that slows down delivery. It is particularly advisable when significant domain knowledge is embedded in the code and would be expensive or dangerous to recreate elsewhere. Teams that understand the system but are constrained by it often benefit the most from a structured refactoring effort.
Typical refactoring work starts with stabilizing the foundation. Adding regression tests around critical business flows reduces fear and enables change. Modularizing a monolith by introducing clearer boundaries and ownership can significantly improve velocity without introducing the operational complexity of distributed systems. Targeted performance improvements, especially around database access and caching, often result in immediate and measurable business benefits.
The strength of refactoring lies in its relatively low risk and its ability to deliver visible improvements quickly. It is easier to justify to stakeholders because it rarely creates a long period with no business value. However, refactoring has limits – it cannot fully compensate for a fundamentally flawed architecture or data model. In those cases, it often serves as a way to buy time rather than a permanent solution.
Replace: rebuilding or buying your way out
Replacement strategies come in two different forms. The first is rebuilding or rearchitecting a system in-house. The second is replacing custom software with a COTS or SaaS product.
Rebuilding from scratch makes sense when the existing system fundamentally blocks growth and when business requirements have branched off so far from the original assumptions that gradual change no longer works. Examples include systems that do not support modern deployment models or make it impossible to implement required features such as multi-region availability or advanced data processing.
The most probable hazards when fully rebuilding the system are long timelines, feature freezes, duplicated work, and the loss of undocumented business logic. These risks can be mitigated by breaking the rebuild into smaller, independently deployable components and by validating assumptions through pilots before expanding scope, but keep in mind, they never disappear entirely.
Replacing custom systems with SaaS or COTS products is a different way. This approach works best when the domain is not a source of competitive advantage and when the external alternative is mature and well-supported. Areas such as billing, CRM, HR, or internal analytics often fall into this category. The benefits are faster time to value and reduced operational burden, while the trade-offs include vendor lock-in, integration complexity, and limited customization.
In both cases, the replacement decision should be made under strategic focus, not by frustration alone.
Isolate: progressive modernization without stopping the business
Isolation strategies, often implemented using the strangler fig pattern, are designed for situations where the system is too critical to risk a big-bang change, but too constrained to just leave. Instead of modifying or replacing the legacy core directly, a new layer is introduced around it.
In practice, this means plugging an API gateway or proxy in front of the existing system so that all traffic flows through a controlled entry point. Then, new functionality is implemented alongside the legacy code, and specific routes or use cases are gradually redirected to the new components. Over time, as confidence grows, more traffic is shifted until the legacy parts can finally be retired.
This approach is particularly effective for revenue-critical systems with zero downtime tolerance or for architectures where internal refactoring would be extremely risky. It also aligns well with evolutionary architectures, allowing teams to move toward services and modern platforms without committing to an all-or-nothing transition.
Of course, there are challenges – integration layers can become complex and bring in new failure modes. Data synchronization between old and new systems requires careful design (especially when dual writes or backfills are involved). The process is also time-consuming by nature and demands discipline to avoid creating a new layer of chaos.
The reward in this scenario is control as each step delivers measurable improvements while keeping the business running.
Legacy systems decision matrix for scaleups
Legacy modernization is not about finding the “best” approach but choosing the least wrong option for a specific domain under real business constraints.
A decision matrix helps make those trade-offs explicit. Instead of debating refactor versus rewrite, it forces a domain-by-domain assessment based on factors that actually matter in a scaleup: business criticality, tolerance for downtime, delivery pressure, technical debt, and available skills.
The matrix should not be applied to the platform as a whole since different parts of the system carry different levels of revenue risk and strategic importance. Used correctly, the matrix shifts the conversation from architectural preferences to business outcomes making the decisions easier to explain and defend.
The table below is a practical tool for evaluating which strategy fits each domain and why:
| Dimension | Refactor | Replace – Rebuild | Replace – SaaS / COTS | Isolate / Strangler Fig |
|---|---|---|---|---|
| Business criticality | High – core revenue, customer-facing | Medium to high, but current system actively blocks growth | Low to medium, non-differentiating domains | Very high – revenue-critical, downtime intolerant |
| Fit with current business model | Still good, but slowing execution | Poor – assumptions no longer match reality | Good enough, industry-standard workflows | Poor internally, but externally stable |
| Technical debt level | Medium to high, but manageable | Very high, systemic, architectural | Irrelevant – debt outsourced | Very high, unsafe to touch directly |
| Tolerance for downtime | Low | Medium to high (planned cutovers) | Medium (migration windows) | Very low |
| Time pressure | High – improvements needed now | Lower – strategic bet | Medium – driven by vendor rollout | Medium to high – gradual relief |
| Delivery pressure (roadmap) | Must keep shipping continuously | Often causes feature freeze | Minimal impact if well-integrated | Continuous delivery required |
| Domain differentiation | High – embedded IP and business logic | High – core competitive advantage | Low – commodity functionality | High – but implementation is fragile |
| Team familiarity with system | High – team knows the code | Often low or fragmented | Low – shifts to integration skills | Mixed – legacy knowledge + new stack |
| Required skill set | Strong refactoring and testing discipline | Architecture, distributed systems, migration | Vendor management, integration | Architecture, platform, data consistency |
| Upfront cost | Low to medium | High | Medium (licenses + integration) | Medium to high |
| Ongoing cost | Gradually decreasing | Medium to high | Predictable subscription costs | Medium – dual systems for a while |
| Risk profile | Low to medium | High | Medium | Medium but controllable |
| Typical horizon | Short to mid-term | Mid to long-term | Short to mid-term | Mid to long-term |
| What you optimize for | Velocity and stability | Long-term scalability and flexibility | Speed and focus | Risk reduction and continuity |
For a monolith-specific view on scaling and modernization challenges, see our guide on scaling and modernizing monolithic apps.
Summary
Deciding whether to refactor, replace, or isolate is rarely the hardest part. The real challenge is turning that decision into a plan that improves the system while the business keeps moving. Most scaleups cannot afford long pauses in delivery, and few can take on modernization work without clear links to revenue and team capacity.
What usually helps is starting with a focused technical and business assessment rather than a rewrite plan. That means looking at systems by domain and understanding how each one affects delivery speed and operational risk. With that clarity, modernization becomes a string of manageable steps instead of a single high-stakes bet.
Boldare works with scaleups on this kind of groundwork. The goal is not to enforce a target architecture, but to help CTOs and product leaders map technical debt to business impact, choose where refactoring, isolation, or replacement makes sense, and build a modernization roadmap that runs alongside active product development.
If you are facing these trade-offs now, the next useful step is often a short, structured assessment that connects architecture decisions to delivery, risk, and cost. From there, it becomes much easier to modernize consciously without slowing the company down.
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