For a growth stage startup, the key milestone is successful scaling. Investors at this stage ask for incremental profit, making growth a priority. However, scaling a company is far from easy. In 2017, Beepi, an online used-car marketplace, shut down despite raising $150million in funding rounds. Inefficient scaling drained its cash reserve without giving returns. In 2018, Primary Data failed with Steve Wozniak as one of its leaders and after a $100mn funding round. The product was overly complex and not suited for scaling.
Building a product for early adopters is tough. But scaling it for the masses? That’s herculean. This shift demands structural foresight, financial runway, deep expertise, a fast-moving dev team, seamless cross-team collaboration, agility to pivot, and more on the tech front alone. Most startups struggle to synchronize all these moving parts into one cohesive strategy. And that’s before factoring in the business-side challenges. When they fail to strike the right balance, they don’t just stumble—they shut down.
Smooth product scaling in the growth stage requires a complete understanding of the challenges. From our experience developing tech products for over 200 startups, we have listed the following growth-stage startup challenges.
Growth Stage Startup Challenges
A proper understanding of these challenges can prevent reinventing the wheel during software product development and reduce cycle time.
Lack of Scalable Architecture
In the early stages, startups prioritize achieving product-market fit and acquiring paying customers quickly. This leads to compromises in tech stack selection. Sometimes, startups also lack a clear product vision to understand how the product would shape up in the future. This is especially common when there is no tech expert or startup expert to guide the team forward.
A tech stack is crucial as it directly influences customer experience. Poor choices in programming languages, frameworks, and tools can hinder scalability, performance, and development speed. This, in turn, creates roadblocks during product launch, maintenance, and business expansion.
The need for rapid scaling emerges when an early-stage startup enters the growth stage. It makes problems with inadequate tech stacks evident by creating performance bottlenecks. The solutions are costly and time-consuming fixes, including downtime, expert intervention, and financial investment.
Trouble with Feature Velocity & Technical Debt
The pressure of scaling sometimes overshadows the need for code quality. Growth-stage startups initially experience no visible impact. But over time, piled-up technical debt makes systems difficult to maintain. This leads to longer development cycles, more bugs, and a decline in adaptability.
Technical debt can also arise if the tech team adopts an outdated tech stack or misses crucial tool updates. Startups often work with open-source technologies to save costs. However, missing two to three updates can lead to system slowdown and integration hassles.
Inadequate Top Tech Talent
While scaling, a growth-stage startup must ramp up its team size. This team should have expert engineers who have knowledge of the domain, skills in other technologies, the ability to own the product from end to end, and adaptability skills to quickly meet the changing market needs.
Hiring such engineers is a time-consuming task. Their numbers are few, and the demand is high. Big techs also compete for the same engineers. The entire hiring process can take 2-3 months. Startups cannot stall their product development process for such a time span.
But retaining these talents is equally important. Most of these engineers stay because of the quality of work they get, work-life balance, and remuneration. Balancing these three aspects can divert attention from core business goals.
Inefficient Cross-Team Collaboration
Startups often start with just a handful of members. As they grow, they hire more people and divide their resources into teams to operate smoothly. If a tech team grows too large, they break down teams with team leads to manage them, often leading to misalignments.
There are two types of alignments- one where teams are aligned with product requirements and the second where teams are aligned with each other. When multiple teams develop features, aligning them with the product goal becomes a challenge. Dependency issues can arise, where delays in one team’s feature delivery impact others. There can also be instances where two teams attempt to solve the same problem differently, leading to duplicated efforts, wasted resources, and integration challenges when their solutions need to work together.
Problem with Security & Compliance at Scale
Security isn’t just a concern for startups—it’s a make-or-break factor that directly impacts customer trust. A single data breach can lead to devastating financial losses and erode brand credibility overnight. In 2023, Okta suffered a breach affecting over 300 customers, wiping out $2 billion in market cap—a stark reminder of the stakes.
As startups scale, they experience an explosion in user data, system complexity, and attack surface. More data means greater vulnerabilities and the risk of breaches skyrockets without a dedicated security team. The fallout? Loss of customer trust, damage to brand reputation, and shaken investor confidence.
The challenge intensifies with third-party dependencies. Many startups outsource tech development offshore, and without strict regulatory controls and airtight NDAs, these alliances can turn into security liabilities. The rise of AI-driven automation further complicates things—without proper governance and safeguards, AI systems can open new doors to unauthorized data access.
Be Customer-Driven Or Vision-Driven Development?
Growth-stage startups face a critical dilemma: They need loyal, satisfied customers to scale, but catering too much to individual demands can overcomplicate the product, making it sluggish and directionless.
This tension plays out in two contrasting approaches:
Customer-driven development – An “outside-in” strategy where products evolve based on direct customer feedback and market needs.
Vision-driven development – An “inside-out” approach where the product follows a pre-defined company vision, prioritizing internal goals over immediate customer requests.
Each path comes with risks. Customer-driven development can lead to incremental tweaks instead of game-changing innovation, limiting long-term differentiation. Meanwhile, vision-driven development might build something groundbreaking that fails to resonate with real market demands.
Mounting Infrastructure & Cloud Costs
Cloud costs can quickly escalate as a growth-stage startup’s user base expands. This often happens due to a lack of visibility into resource usage, over-provisioning to account for uncertain future demands, and failure to optimize costs using features like reserved instances or spot instances.
Cloud infrastructure costs are of two types—infrastructure costs (20%) and maintenance costs (80%). Infrastructure costs include computing, storage, and networking. Maintenance costs depend on DevOps engineers and the SRE Team. For a growth-stage startup with 100 software developers, the cloud cost is around $1mn per year. If not optimized, this cost could shoot up, affecting the ROI.
Sluggish Development Due to AI/ML
AI is now a go-to solution for many companies. AI features are also getting more funding from investors. In the US alone, AI startups got around $11.4 billion in Q3’24 funding. However, AI integration is complex as it requires data pipelines, model training, and constant fine-tuning. Growth-stage startups often don’t plan well before jumping on the AI bandwagon. This can slow down core product releases.
AI-powered features require data pipelines, model training, and constant fine-tuning, adding extra complexity layers. Growth-stage startups don’t have enough data, which can trigger a cold start. Their long iteration cycles involve continuous training, validation, and tuning. These models must be retrained with new data every time performance drops.
Training AI models requires powerful GPUs, storage for large datasets, and cloud-based AI processing, significantly increasing infrastructure costs. Growth-stage startups are not financially flexible, and there is no immediate ROI.
Arduous Customer Onboarding
Managing rapid customer growth and onboarding presents challenges for growth-stage startups, often stretching their technical and operational capacities. As user numbers surge, customer support becomes a bottleneck, with overwhelmed teams struggling to handle an influx of queries. This can lead to frustrated users and higher churn rates.
Onboarding friction, whether due to complex sign-up flows, identity verification (KYC), or unclear UX, is also challenging. It can result in users dropping off before fully engaging with the product.
On the technical side, a sudden spike in users can lead to system outages, slow load times, and API failures if backend systems aren’t optimized for high demand. Poor database management, inefficient caching strategies, and lack of autoscaling capabilities can significantly impact performance. As complexity grows, ensuring a seamless onboarding experience while maintaining stability, security, and compliance becomes increasingly difficult.
No Plan for Pivoting
Sometimes, pivoting is the only way to stay relevant. The need for pivoting can arise for multiple reasons. Many times, products fail to resonate well enough after a certain period. Customers slowly start backing out, which can be disastrous for a startup. Emerging market trends can also call for an immediate shift. For instance, Cruise, the autonomous car division of General Motors, started delivering food in San Francisco during COVID-19.
Streamlining processes and improving scalability can be crucial in overcoming operational challenges. Changes in team dynamics, leadership, or strategic priorities can also trigger the need for a pivot. However, pivots always come with the risk of disrupting engineering efforts and customer trust.
Solutions for Growth-Stage Startup Challenges
These growth-stage startup challenges manifest differently for each startup, so their solutions should be tailored. However, a broad understanding of how these problems can be solved can help mitigate the challenges.
Scale Architecture Without Losing Agility
A modular, microservices-based architecture is an effective approach to scaling. Instead of monolithic systems, startups can break their applications into loosely coupled, independent services. Each service can be developed, deployed, and scaled independently. This will help teams work in parallel and innovate quickly. If the team size is under 100, a modular monolith may be a better choice, ensuring that new features or updates do not require massive code changes while reducing risk and downtime.
Cloud-native technologies like Kubernetes, serverless computing, and containerization provide scalability and resilience. These solutions allow automatic scaling based on demand, which reduces infrastructure costs and improves resource efficiency. Cloud-native architectures support continuous deployment and automation to ensure agility even as systems grow. However, careful planning is required, as cloud adoption can also burden a product with incremental costs. Startups may consider a hybrid approach to optimize costs while maintaining agility.
Logging, monitoring, and distributed tracing are essential for maximizing observability. These measures help capture system events and errors for quick debugging and performance analysis, track system health and performance metrics, and provide end-to-end visibility across microservices. They also help in diagnosing latency issues and optimizing workflows.
Manage Feature Velocity & Technical Debt
Tech debt sprints are an effective way to manage evolving tech needs. Dedicated cycles focused on refactoring, improving code quality, and addressing accumulated technical liabilities can help growth-stage startups avoid long-term bottlenecks. If startups delay addressing technical debt, they risk losing three to four months per year in rework.
Enforcing engineering OKRs (Objectives and Key Results) ensures teams stay aligned with business goals while maintaining a disciplined approach to code maintainability. By periodically refactoring critical codebases, engineering teams can prevent performance bottlenecks, save engineer bandwidths, and reduce the cost of future changes.
Platform engineering can play a crucial role in automating repetitive tasks such as infrastructure provisioning, CI/CD processes, and dependency management. By reducing manual overhead, teams can focus more on feature development while ensuring that tech debt doesn’t pile up unnoticed. Automation enhances developer productivity and fosters consistency and reliability across deployments. This approach ensures that innovation continues at a rapid pace without compromising system stability or long-term maintainability.
When using open-source software, startups should be cautious about version upgrades. Transitioning more than two major versions at once can waste up to 25% of the team’s yearly bandwidth.
Hiring and retaining top tech talent
Competitive salary is not enough to attract and retain top engineering talent. They look for a strong engineering culture where engineers are encouraged to take ownership, experiment, and solve complex problems. As per Harvard Business Review, companies with a positive culture experience a 65% lower turnover rate than those with a weaker culture. A positive culture ensures higher engagement and productivity. Providing autonomy, recognizing contributions, and promoting a fail-fast, learn-fast mindset creates an environment where engineers feel valued and motivated to push technological boundaries.
Beyond culture, allowing engineers to work on cutting-edge problems—whether in AI, distributed systems, or blockchain—keeps them intellectually stimulated and engaged. Engineers thrive when they can build solutions that drive real-world impact, making the company a workplace and a platform for meaningful innovation. One good way of doing this is by hiring like-minded people. Startups should onboard people or teams whose growth plans match their product vision.
To build a strong engineering team, startups must not only foster a positive culture but also choose the right software development services for startups to ensure they have the expertise and scalability needed to innovate and grow effectively.
Empowering Team Collaboration
Adopting a product mindset aligns business goals with customer needs. Instead of engineering working in silos, this approach integrates engineering teams with product management, design, and other stakeholders to build solutions that deliver real value.
One key mechanism to achieve this is using structured RFCs (Request for Comments)—formalized documents that outline proposed changes, their impact, and rationale. RFCs encourage transparency, collaboration, and peer review, ensuring that architectural decisions are well thought out and aligned with long-term objectives. This structured approach helps teams make informed decisions while avoiding technical debt and misalignment.
To further streamline collaboration, cross-functional squads bring together engineers, product managers, designers, and other key contributors to work as a single unit focused on specific goals. These squads operate autonomously but remain closely aligned with the overall company strategy, ensuring rapid iterations and innovation without sacrificing coordination. Both synchronous and asynchronous communications make this kind of collaboration even better. For offshore teams, maintaining clarity in product expectations, reducing scope creep, conducting weekly sync-ups, providing a dashboard with full visibility into development progress, and implementing productivity tracking tools are essential to ensure smooth collaboration.
Improving Security
Shifting security left with DevSecOps ensures that security is integrated early in the development lifecycle rather than being an afterthought. This approach embeds security into CI/CD pipelines, enabling continuous monitoring, vulnerability scanning, and real-time threat detection. Conducting regular audits helps identify risks proactively, ensuring that security measures evolve alongside the application.
Adopting a zero-trust architecture strengthens security by enforcing strict access controls, authentication, and verification at every layer, reducing the risk of breaches. Startups can leverage automation to consistently track and enforce policies. Automated compliance tracking tools streamline reporting, detect misconfigurations, and ensure adherence to regulatory frameworks without manual overhead.
Before onboarding a tech partner, startups should sign strict NDAs and check for standard compliances like SOC 2. These will reduce the chances of data leaks and security breaches.
Develop a two-speed road map
One track should focus on short-term customer requests and quick iterations, while the other should drive long-term strategic innovation. The short-term track ensures rapid responsiveness to market demands, feature enhancements, and bug fixes, keeping customers engaged and satisfied. The long-term track guarantees foundational improvements, emerging technologies, and architectural evolution for sustained growth and differentiation.
Data-driven decision-making is essential to effectively prioritizing both tracks. Leveraging customer insights—from feedback, behavioral analytics, and usage patterns—helps teams make informed choices about what to build and refine.
AWS is an example of a company that balances short-term customer-driven improvements with long-term innovation. The launch of AWS Lambda in 2014 was a strategic move perfected after years of R&D. However, in 2023, AWS introduced price reductions for EC2 instances and new instance types to meet immediate customer demand for cost efficiency and performance.
Optimize cloud infrastructure
Optimizing cloud costs without sacrificing performance requires a FinOps approach where finance, engineering, and operations merge to manage cloud spending efficiently. By implementing FinOps, teams gain visibility into usage patterns, identify cost-saving opportunities, and align cloud expenditures with business goals. This enables proactive budgeting and resource allocation without compromising scalability or agility.
Auto-scaling policies can help optimize costs further by dynamically adjusting infrastructure based on real-time demand, avoiding over-provisioning, and reducing waste. Using spot instances or alternative cloud providers such as Fly.io and Backblaze can offer better cost management while maintaining reliability.
Plan better for AI/ML integration
Growth-stage startup tech teams should start with LLM APIs like OpenAI or Google Gemini to quickly roll out AI-powered features with minimal overhead. These APIs enable fast experimentation and user feedback, which developers can use to validate use cases without deep ML expertise. If AI features prove valuable, teams can gradually transition to fine-tuned models, optimizing for domain-specific needs, cost efficiency, and performance gains. For broader needs, teams can opt for contextual inputs or deploy a hybrid model based on where they stand regarding data to maximize AI gains.
Establishing an MLOps pipeline early is crucial to sustaining AI integration without disrupting agility. Automating model training, deployment, and monitoring streamlines workflows, reduces manual effort, and prevents AI bottlenecks. A well-structured MLOps pipeline ensures that data preparation, retraining, and versioning happen seamlessly, keeping AI models reliable and scalable.
Ensure Smooth Customer Onboarding
Digital onboarding is crucial, as 90% of companies lose potential customers during this process. A seamless onboarding experience guarantees customer retention and satisfaction. Automating customer onboarding with self-serve dashboards allows users to explore product features at their own pace while reducing their dependency on support teams. These dashboards provide personalized onboarding journeys, progress tracking, and quick access to essential resources, ensuring a frictionless start.
In-app guidance, such as interactive tutorials and tooltips, helps users navigate key functionalities in real-time. This makes complex workflows intuitive and reduces the learning curve.
AI chatbots can provide instant assistance, answer common questions, troubleshoot issues, and guide users through setup steps to further enhance onboarding. These bots can analyze user behavior, proactively offer suggestions, or escalate issues to human support when necessary. By combining automation with AI-driven personalization, companies can deliver a scalable, efficient onboarding experience that minimizes churn and accelerates user adoption.
Be pivot-ready
Growth-stage startups must be flexible enough to adapt quickly to market changes, customer needs, or strategic shifts without disrupting operations. A modular architecture enables this agility by allowing teams to independently swap, upgrade, or replace components. It reduces the risk of major overhauls.
Feature flagging enhances adaptability by enabling controlled rollouts and A/B testing. It allows teams to experiment with new ideas without full-scale deployment. This approach ensures that pivots can be validated with real user feedback before making irreversible changes, minimizing risk and maximizing learning.
Equally important is transparent communication with customers throughout the pivot process. Keeping users informed about upcoming changes, gathering their feedback, and setting clear expectations helps maintain trust and engagement. Whether it’s through release notes, beta programs, or direct conversations, involving customers in the evolution of a product ensures smoother transitions and better adoption.
Growth-stage startup real-life examples
We have a leading mobile advertising platform as a customer. They were serving around 22 mn ad requests per day in 2009. Their study showed that by 2014, they would experience a 1200% growth. We had to scale up rapidly. As a solution, we scaled the database and infrastructure scaling, decoupled modules, reduced I/O operations, and introduced big data analytics for faster processing. Our solution not only supported the needs but also enabled an 1800% growth from 2013 to 2014 without the need for any architectural changes.
Netflix started in 1997 as a DVD rental service delivered via mail. This innovative solution countered Blockbuster’s brick-and-mortar rental stores. However, they quickly realized the content consumption patterns were changing with the rise of digital media. In response, it pivoted to streaming video on demand in 2007, which was groundbreaking. Today, it has become a leader in the online streaming category. The company also pioneered algorithms that understand user preferences and suggest programs accordingly.
To support its expansion, DoorDash needed to optimize route optimization, real-time demand prediction, and personalized recommendations. To address this, it implemented an end-to-end MLOps pipeline and streamlined its AI. The change reduced the time to deploy new models from weeks to days, improved delivery time, and increased customer engagement.
Key Takeaways
Scaling is crucial for growth-stage startups, but it should never become a distraction from core business needs. We’ve seen companies invest excessive time in scaling their engineering efforts or expanding their teams to support growth, only to lose focus on the business side.
- The simplest way to avoid this pitfall is to bring in a tech partner who can handle the complexities of scaling—managing tech decisions and team expansions or contractions—while freeing up valuable bandwidth for product promotion and strategic growth.
- Adopt a product mindset and plan for long-term sustainable growth rather than short-term agility.
- Implement proper practices to identify tech debt at the earliest and take necessary measures to mitigate it.
- Avoid buzzwords. Adopt technologies only after conducting a thorough analysis of use cases; otherwise, fancy tech can derail the product development process.
- The expertise and startup experience of the engineering team are crucial. The team should not consist of specs-driven coders but rather engineers who can take ownership of the product and do what is necessary.
