In-House QA vs. Outsourcing: How Growing Tech Companies Should Think About Testing

As engineering organizations scale, quality assurance becomes both more critical and more complex. Releases accelerate. Architectures sprawl. The surface area for defects expands faster than most teams expect.

At some point, every growing technology company runs into the same tension:

Should we build QA capacity in-house, outsource it, or assemble something in between?

This article breaks down the most common QA delivery models and explores their trade-offs.

Why QA Models Matter for Growing Tech Companies

It often starts with a release that takes longer than expected. Or a bug that slips to production and erodes customer trust. Or an engineering team that begins quietly working around QA because “they’re overloaded right now.”

At early stages, quality problems are survivable. Customers forgive rough edges. Engineers patch fast. But as growth compounds, those same shortcuts turn into drag:

• Releases slow because regression testing becomes unpredictable

• Engineers lose confidence in what’s safe to ship

• QA becomes a bottleneck

• Leadership starts asking why velocity and quality feel at odds

  
  

This is the moment where the QA model stops being an operational detail and becomes a strategic decision.

Choosing the right model shapes how your organization thinks about risk, ownership, and scale. By choosing the right model today, you avoid the pain of changing course down the road.

Model 1: Fully In-House QA Team

What It Looks Like

You hire dedicated QA engineers or testers as full-time employees embedded within product or engineering teams.

Pros

• Deep product and domain knowledge

• Tight collaboration with engineering and product

• Strong ownership and accountability

• Easier alignment with internal processes and culture

  
  

Cons

• Fixed capacity: QA bandwidth does not scale with release spikes or roadmap shifts

• Knowledge stagnation risk: Teams often reuse familiar tools and patterns long after better approaches exist

• Recruiting, onboarding, and retention costs

• Difficult to justify specialists (performance, security, accessibility) at smaller scales

  
  

With this model, strong QA leadership is essential. Without it, teams tend to over-invest in brittle automation or under-invest in risk-based testing. In-house QA works best for organizations with stable roadmaps, deep domain complexity, and the patience to build long-term testing maturity.

Model 2: QA as a Shared Responsibility

What It Looks Like

Testing is distributed across the team. For example, product managers validate workflows, engineers write tests, and designers review UX consistency.

Pros

• Low incremental cost

• Strong sense of shared ownership for quality

• Effective for small, highly autonomous teams

  
  

Cons

• QA is often deprioritized under delivery pressure

• Inconsistent depth and coverage

• Limited expertise in edge cases and non-functional testing

• Does not scale well as systems and teams grow

  
  

AI tools can make this model more viable by assisting with test creation and validation, but they do not replace the need for QA strategy. This approach is common in early-stage companies, and often stay in place until quality issues force a rethink.

Model 3: Offshoring and Nearshoring

What It Looks Like

QA work is handled by teams in lower-cost regions, either directly or through a vendor. Nearshoring is an alternative that offers closer time zones and cultural alignment, but far less common in practice than offshoring. 

Pros

• Cost efficiency at scale

• Access to large pools of testers

• Ability to flex capacity up or down more easily than hiring full-time staff

• Supports extended or near-continuous testing cycles

  
  

Cons

• Communication and feedback loop delays

• Potential gaps in product context or quality expectations

• Requires strong documentation and process discipline

• Risk of QA becoming execution-focused rather than insight-driven

  
  

While contract QA seems flexible, it is effective only when paired with disciplined management. Poorly defined engagements often balloon in size while delivering diminishing returns. This model works best for mature products with stable requirements and leaders experienced in managing distributed QA teams.

Model 4: AI QA Tools and Agents

What It Looks Like

There has been an explosion of AI-powered QA tools or agents to generate tests, maintain automation, or analyze coverage.

Pros

• Faster test creation and maintenance

• Potential to reduce manual regression effort

• Scales more efficiently than human-only approaches

  
  

Cons

• Requires careful evaluation and vendor selection

• Implementation effort is often underestimated

• Needs ongoing monitoring, tuning, and governance

  
  

This model is frequently oversold as autonomous QA. In reality, AI tools shift where effort is spent. Teams still need QA expertise to define workflows, evaluate results, and adapt strategies as systems evolve. Without that foundation, AI introduces false confidence rather than real coverage.

Model 5: AI-Enabled, Embedded QA Partnerships

What It Looks Like

Instead of buying tools or staffing testers in isolation, QA is delivered as an integrated system: experienced QA professionals embedded with your team, powered by AI, and accountable for outcomes, not just activity.

This model blends:

• Strategic QA leadership

• Flexible human expertise

• AI-driven automation and optimization

• Continuous adaptation as products evolve

  
  

Pros

• Combines flexibility with deep product context

• Scales capacity without fixed headcount

• Leverages AI without requiring internal procurement or maintenance

• Focuses on coverage, risk, and outcomes—not just test counts

  
  

Cons

• Requires a high-trust partnership

• Demands transparency and collaboration

• Not interchangeable with low-cost vendors

  
  

This approach is still relatively rare—but it represents a “best of all worlds” model for scaling teams that want adaptability, affordability, and modern AI capabilities.

Tessana’s Unique Approach

At Tessana, we built our model around a simple belief: growing engineering teams should not have to choose between speed, quality, and flexibility.

Our AI-enabled QA partnership delivers:

• Comprehensive test coverage across manual, automated, and exploratory testing

• White-glove service, with experienced QA professionals embedded in your workflows

• Self-healing automation that adapts as your product changes

• AI-driven prioritization to focus effort where risk actually lives

  
  

Instead of asking you to manage tools, vendors, or fluctuating capacity, we take ownership of end to end quality.

If your team is feeling the strain of scaling without confidence, the next step isn’t choosing in-house or outsourced QA. It’s choosing a model designed for how modern software is actually built.

Talk with our team to see how AI-enabled QA can evolve with your product, and deliver on the promise of higher software quality.