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Infrastructure has become a decisive factor in how organisations scale, innovate and remain resilient. As digital products grow, many teams encounter rising cloud costs, slower delivery cycles and increasing technical debt: signals that existing infrastructure is no longer keeping pace with demand.
To understand how technical leaders are responding, we conducted an exclusive survey of senior engineering and infrastructure decision-makers responsible for scaling modern systems. The findings show a clear shift in priorities: organisations are focusing less on adopting new tools in isolation and more on modernising legacy foundations, strengthening observability, improving automation consistency and introducing platform-level standards.
This report distils the most important trends, constraints and priorities shaping infrastructure strategies over the next 12 months. It is designed to help technology leaders assess their current position, identify the most critical scaling risks, and make more deliberate infrastructure decisions that support sustainable growth without sacrificing speed or reliability.
This report is based on original and exclusive research conducted by Imaginary Cloud. We have surveyed senior technical professionals responsible for infrastructure, platform engineering and digital delivery, including CTOs, VP Engineering, Heads of Platform and DevOps, and senior engineering leaders.
Respondents represented a mix of scale-ups, enterprise organisations and regulated teams, spanning growing product teams to large organisations managing complex distributed systems. Most respondents were based in Europe, with additional input from globally distributed teams.
The survey combined structured quantitative questions with qualitative insights. Findings reflect recurring patterns and strategic signals rather than isolated opinions.
As organisations scale, infrastructure challenges rarely appear in isolation. The survey shows that delivery slowdowns, rising costs and reliability issues typically stem from interconnected constraints that compound as systems, teams and architectures grow.
Rather than a single technical limitation, most technical leaders are navigating a combination of architectural debt, operational inconsistency and scaling processes that have not evolved at the same pace as their products.
Legacy systems are the most frequently cited constraint on scaling infrastructure. In practice, this often reflects tightly coupled architectures, outdated components and accumulated technical debt.
Where legacy constraints dominate, teams struggle to improve automation, observability and cost efficiency without addressing underlying architectural limitations.
Responses across automation and delivery maturity suggest many organisations operate with inconsistent tools and workflows across teams. This fragmentation increases cognitive load, slows incident response and makes standardisation difficult as scale increases.
Survey data on observability confidence indicates that many organisations lack a unified view of system health across distributed services. Without reliable signals, teams become more risk-averse, slowing delivery as complexity grows.
Many organisations still rely on manual or partially automated infrastructure processes. As infrastructure grows, these dependencies become structural bottlenecks that reduce predictability and increase operational risk.
Budget constraints and cost visibility remain significant concerns. Without clear ownership and governance, cloud complexity often amplifies other scaling challenges rather than enabling flexibility.
Scaling challenges reinforce each other. Legacy systems slow automation, inconsistent practices weaken observability, and manual processes increase both operational risk and cost inefficiency.
The survey indicates that infrastructure challenges evolve in predictable ways as organisations grow. Rather than facing the same issues at every stage, teams encounter different constraints depending on their infrastructure maturity, delivery practices and operational discipline.
Understanding current maturity helps technical leaders prioritise realistic improvements and avoid investing in capabilities that their organisation is not yet positioned to adopt effectively.
Survey responses show clear variation in how infrastructure strategy and automation are implemented across organisations.
These responses suggest that while many organisations are actively modernising, a significant proportion are still constrained by legacy environments or incremental change strategies.
Taken together, the data indicates that full automation is still emerging rather than universal, with many teams operating in transitional states that introduce operational friction at scale.
While every organisation’s environment is unique, survey responses consistently cluster around four broad maturity patterns. These patterns are defined less by specific tools and more by how teams manage complexity, ownership and standardisation.
At this stage, infrastructure is primarily focused on speed and experimentation. Systems are typically managed directly by product teams, with limited formal governance.
Typical characteristics include:
Primary focus areas:
Organisations in this phase begin to experience the operational impact of growth. Systems become more distributed, teams expand, and coordination costs increase.
Typical characteristics include:
Primary focus areas:
At this level, infrastructure is increasingly treated as an internal product designed to support development teams at scale. Dedicated platform capabilities begin to emerge.
Typical characteristics include:
Primary focus areas:
The most mature organisations treat infrastructure as a continuously evolving capability rather than a fixed system. Decision-making is data-informed and tightly aligned with product strategy.
Typical characteristics include:
Primary focus areas:
The data indicates that infrastructure maturity is not defined by tool adoption alone, but by how effectively organisations manage complexity, standardise practices and balance innovation with operational stability.
Teams that clearly understand their current maturity level are better positioned to prioritise improvements that deliver measurable impact, rather than pursuing broad or premature modernisation efforts that increase risk without clear return.
The survey indicates a shift in how technical leaders approach infrastructure design. Rather than treating infrastructure as a supporting function that reacts to growth, organisations are increasingly designing infrastructure deliberately to enable growth, balancing scalability, reliability and cost from the outset.
Survey responses highlight several recurring strategies that organisations associate with more predictable scaling outcomes.
Leaders avoid overengineering for peak demand. Instead, they prioritise architectures that can grow in controlled steps.
What this looks like in practice:
Why it matters:
Incremental design reduces the need for disruptive refactoring as demand increases.
Cost optimisation shifts from reactive cost-cutting to intentional governance embedded in infrastructure decisions.
What this looks like in practice:
Why it matters:
Cost becomes part of infrastructure quality—alongside reliability and performance.
Automation is treated as a baseline capability, not an optimisation.
What this looks like in practice:
Why it matters:
Automation limits variability, reduces human error and improves delivery predictability at scale.
Leaders prioritise visibility before expanding complexity.
What this looks like in practice:
Why it matters:
Without observability, teams slow delivery as complexity increases to manage risk.
Rather than large-scale rewrites, leaders remove constraints gradually.
What this looks like in practice:
Why it matters:
Incremental modernisation improves scalability without disrupting delivery momentum.
Scalable infrastructure strategies emphasise modular design, embedded automation, strong observability, cost-aware decision-making and continuous management of technical debt.
Organisations that adopt these principles proactively are better positioned to scale delivery and infrastructure in parallel, rather than allowing operational constraints to emerge as a by-product of growth.
Platform engineering refers to the practice of building and operating internal platforms that provide standardised infrastructure capabilities, shared tooling and self-service workflows for development teams.
In the context of scaling, platform engineering is less about introducing new technology and more about how infrastructure capabilities are delivered and governed as organisations grow.
Survey signals across automation maturity, observability confidence and scaling constraints suggest that platform approaches become relevant when infrastructure complexity begins to outpace team coordination.
Platform engineering typically becomes valuable when:
At this stage, informal or team-specific infrastructure practices no longer scale effectively.
Survey insights indicate that platform initiatives often face organisational rather than technical challenges.
Common barriers include:
Organisations that treat the platform as an internal product—with dedicated ownership, feedback loops and continuous improvement—are better positioned to achieve sustainable adoption.
Pplatform engineering becomes relevant when coordination, consistency and governance become limiting factors to scale. Its success depends less on tooling and more on alignment with developer needs and organisational maturity.
As infrastructure scales, partial automation increasingly becomes a constraint rather than a stepping stone. Survey responses indicate that environments combining scripts, manual approvals and inconsistent pipelines introduce variability that becomes harder to manage as teams and systems grow.
Partially automated environments often exhibit:
As scale increases, these dependencies reduce predictability and slow delivery, even when tooling exists.
Rather than an advanced capability, Infrastructure as Code functions as a baseline requirement for consistency at scale. Treating infrastructure changes as software changes improves traceability, repeatability and control without increasing process overhead.
Common practices associated with this approach include:
These practices reduce configuration drift and make infrastructure behaviour more predictable as delivery frequency increases.
As automation matures, delivery pipelines act as the coordination layer between development and operations. Survey responses suggest that organisations investing in delivery maturity focus on embedding validation and controls directly into pipelines rather than relying on manual oversight.
Practices commonly prioritised include:
These capabilities support frequent releases while limiting operational risk.
As infrastructure scales, technical leaders increasingly focus on control, sustainability and prioritisation, rather than introducing additional tools or complexity.
Survey responses indicate that organisations performing well at scale embed observability and cost awareness into everyday decision-making, while aligning infrastructure investment with clear strategic priorities.
Rather than treating governance and prioritisation as separate concerns, leaders manage them as interconnected capabilities that shape how infrastructure evolves over time.
Observability is increasingly treated as a control mechanism rather than a purely operational capability. Survey data related to monitoring confidence suggests that teams with reliable visibility into system behaviour are better positioned to scale infrastructure without increasing risk.
In practice, observability supports scale by:
Without these signals, teams tend to slow delivery and limit change as complexity grows.
Cost management is no longer addressed solely through periodic optimisation. Survey responses indicate a shift towards embedding financial awareness directly into infrastructure design and operations.
This approach commonly includes:
By embedding cost awareness into decision-making, organisations improve sustainability without constraining delivery speed. This approach closely mirrors the FinOps Framework for cloud cost governance.
Historically, infrastructure costs were addressed only after budgets were exceeded. Survey insights suggest that many organisations are now adopting continuous governance models that promote shared accountability.
Common characteristics of this shift include:
These practices improve predictability and reduce financial risk as infrastructure grows.
Survey responses show varied maturity in how organisations factor sustainability and cost into infrastructure decisions:
This distribution suggests that while sustainability and cost are on the agenda for most organisations, formalised governance practices are still evolving.
Survey respondents consistently identified recurring drivers of inefficient cloud spend, including:
These inefficiencies often compound other scaling constraints when governance does not keep pace with infrastructure growth.
Survey data suggests that effective organisations avoid treating governance as restrictive control. Instead, governance is increasingly embedded into delivery workflows through automation and standardisation.
Examples of governance practices that support scale include:
By integrating governance into day-to-day delivery, organisations maintain control without reducing agility.
This mirrors best practices outlined in the Google SRE approach to reliability and risk management.
A recurring theme across responses is the need to balance optimisation with operational stability. Survey data suggests that prioritising short-term cost reduction alone often introduces reliability risk or degrades developer experience.
More effective approaches include:
This balance supports long-term scalability without undermining system quality.
Survey data shows a clear shift in how technical leaders are planning infrastructure investments. Organisations are prioritising foundational improvements that reduce operational risk, improve predictability and support sustainable growth.
These priorities reflect a move away from experimentation towards consolidation and maturity.
Taken together, the data indicates that organisations are focusing on strengthening the foundations required for scale, rather than pursuing novelty or large-scale transformation. Leaders are addressing the constraints that slow delivery today in order to support growth tomorrow.
Infrastructure strategies for the coming year are defined by discipline and focus. Teams that invest in modernisation, automation consistency, observability and cost governance are better positioned to scale predictably while maintaining speed and reliability.
Survey insights show that successful infrastructure scaling is driven less by individual technology choices and more by deliberate, coordinated decision-making over time.
Organisations that scale effectively focus on reducing friction, improving predictability and aligning infrastructure decisions with business outcomes.
Enterprise teams
Focus on risk reduction, observability and standardisation across complex environments.
Scale-ups
Establish automation, deployment standards and basic governance early to avoid compounding technical debt.
Regulated environments
Embed auditability, traceability and controls directly into delivery workflows.
To scale infrastructure effectively:
Infrastructure scaling is an ongoing capability. Leaders who invest in strong foundations and incremental improvement are better positioned to scale sustainably without sacrificing speed or control.
Infrastructure drives innovation, efficiency, and sustainable growth. Organisations that modernise, automate, and improve observability scale faster, reduce costs, and boost developer productivity.
Ready to transform your infrastructure? Contact us today to unlock scalable, future-ready systems that turn your infrastructure from a cost centre into a growth enabler.
Scalable infrastructure is designed to grow with demand while maintaining reliability, performance and cost control. It supports increased workloads through automation, standardisation and modular design rather than manual intervention or one-off scaling fixes.
The most common barriers include legacy systems, inconsistent automation, limited observability and unclear ownership. These constraints often compound as systems and teams grow, reducing delivery predictability and increasing operational risk.
By standardising deployment workflows, automating infrastructure changes and embedding observability and governance into delivery processes. Consistency reduces friction and enables teams to scale systems and releases in parallel.
Platform engineering becomes relevant when infrastructure complexity outpaces team coordination. This typically occurs when standards vary across teams, manual processes increase risk, or engineers spend excessive time managing environments rather than building product features.
Modernisation should be considered when legacy systems slow delivery, limit automation, increase operational risk or drive disproportionate cost. Incremental modernisation aligned with ongoing delivery is generally more effective than large-scale rewrites.
Alexandra Mendes is a Senior Growth Specialist at Imaginary Cloud with 3+ years of experience writing about software development, AI, and digital transformation. After completing a frontend development course, Alexandra picked up some hands-on coding skills and now works closely with technical teams. Passionate about how new technologies shape business and society, Alexandra enjoys turning complex topics into clear, helpful content for decision-makers.
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