Build vs Buy: Should You Create Your Own AI Translation QA Solution?

Every organization implementing AI-powered translation quality assurance faces the same question: should we build our own solution or buy an existing one? The answer depends on your specific needs, resources, and strategic priorities.

This guide provides a practical framework for making this decision, with honest assessments of both paths based on real implementation experience.

The Current Landscape

In 2025, you have more options than ever:

Build Options

Buy Options

Build: What It Really Takes

Let's be honest about what building requires:

Technical Requirements

1. AI/ML Expertise

You need engineers who understand:

• LLM prompt engineering and optimization
• Model evaluation and calibration
• Error handling for AI uncertainty
• Scaling and cost management

Minimum team: 1-2 senior ML engineers (6-12 months)

2. Linguistic Expertise

AI QA needs linguistic grounding:

• MQM error taxonomy implementation
• Severity calibration per content type
• Language-specific rule handling
• Translation quality domain knowledge

Minimum: 1 computational linguist or experienced LQA specialist

3. Infrastructure

Realistic Build Timeline

Month 1-2: Requirements, architecture, prototyping Month 3-4: Core evaluation engine development Month 5-6: UI/dashboard, integrations Month 7-8: Testing, calibration, pilot Month 9-10: Production hardening, documentation Month 11-12: Rollout, training, iteration 

Total: 9-12 months to production-ready

True Build Costs

Year 1 (Development)

Year 2+ (Maintenance & Operations)

Hidden Build Costs

What organizations often underestimate:

1. Calibration time: Getting AI QA to match human judgment takes months of iteration
2. Edge cases: Real content is messier than test data
3. Language expansion: Each new language pair needs calibration
4. Model updates: LLMs change; your prompts need updating
5. Opportunity cost: Engineering time diverted from core product

Buy: What You Get (and Don't Get)

Commercial solutions offer faster time-to-value but come with tradeoffs.

Typical Buy Timeline

Week 1: Evaluation and selection Week 2-3: Contract and setup Week 4-6: Configuration and integration Week 7-8: Pilot and calibration Week 9+: Production use 

Total: 2-3 months to production

True Buy Costs (SaaS Model)

For an organization processing 1M words/month:

Year 1

Year 2+

What Commercial Solutions Provide

Included:

• Pre-built MQM error taxonomy
• Multi-language support (50-100+ languages)
• Calibrated severity thresholds
• Dashboard and reporting
• API access and integrations
• Regular model updates
• Customer support
• Compliance and security certifications

May Not Include:

• Custom error categories
• On-premise deployment
• Deep customization
• Source code access
• Unlimited API calls
• Specialized domain models

Commercial Solution Limitations

1. Vendor dependency: Your QA workflow depends on external service
2. Limited customization: May not support niche requirements
3. Data concerns: Content sent to third-party for evaluation
4. Pricing changes: Costs may increase over time
5. Feature pace: You're dependent on vendor's roadmap

Decision Framework

Use this framework to evaluate your situation:

Factor 1: Volume and Scale

At very high volumes, the per-word cost advantage of build becomes significant.

Factor 2: Customization Needs

Factor 3: Technical Capability

Factor 4: Data Sensitivity

Factor 5: Strategic Importance

Hybrid Approaches

You don't have to choose pure build or buy. Consider:

1. Buy + Customize

Start with a commercial solution, extend with custom components:

┌─────────────────────────────────────────────┐ │ Commercial LQA Platform │ │ (Core evaluation, standard workflows) │ └─────────────────────┬───────────────────────┘ │ API ┌─────────────┴─────────────┐ │ │ ┌───────▼───────┐ ┌───────▼───────┐ │ Custom Rules │ │ Custom │ │ Engine │ │ Reporting │ │ │ │ │ │ - Domain │ │ - BI │ │ validation │ │ integration │ │ - Proprietary │ │ - Custom │ │ checks │ │ dashboards │ └───────────────┘ └───────────────┘ 

2. Build Wrapper, Buy Core

Use commercial AI APIs with custom wrapper:

# Your custom orchestration layerclassTranslationQA: def__init__(self): self.llm = OpenAI() # Or commercial LQA APIself.custom_rules = load_domain_rules() self.glossary = load_glossary() defevaluate(self, source, target, lang_pair): # Step 1: Apply custom pre-checks custom_issues = self.apply_custom_rules(source, target) # Step 2: LLM/API evaluation llm_evaluation = self.call_llm_qa(source, target, lang_pair) # Step 3: Custom post-processing final_result = self.merge_and_score(custom_issues, llm_evaluation) return final_result 

3. Progressive Build

Start with buy, gradually build components:

Phase 1: Commercial solution (month 0-12)

• Learn your actual requirements
• Build internal expertise
• Collect calibration data

Phase 2: Build supplementary components (month 12-24)

• Custom rules engine for domain-specific checks
• Integration layer optimized for your workflow
• Enhanced reporting and analytics

Phase 3: Evaluate full build (month 24+)

• Now you know true requirements
• Have calibration data
• Team has experience
• Make informed build decision

Real-World Decision Examples

Example 1: Translation Agency

Profile:

• 500K words/month across 15 clients
• Standard content types
• Small team, no ML expertise
• QA is operational need, not differentiator

Decision: Buy

Rationale: Volume doesn't justify build cost. No ML capability to leverage. Commercial solutions cover needs.

Example 2: Enterprise Software Company

Profile:

• 2M words/month for product localization
• Strong engineering team
• Highly specialized technical content
• Custom terminology requirements

Decision: Hybrid (Buy + Customize)

Rationale: Volume could justify build, but core needs are standard. Better to buy base solution and build custom rules for specialized terminology.

Example 3: Language Service Provider

Profile:

• 10M+ words/month
• QA accuracy is key differentiator
• Building AI capabilities is strategic
• Already have ML team

Decision: Build

Rationale: Scale provides cost advantage. QA is competitive differentiator. Have capability and strategic intent to build.

Example 4: Regulated Industry (Pharma)

Profile:

• 300K words/month
• Strict compliance requirements
• All content is regulated
• Must maintain audit trail

Decision: Buy (Enterprise/On-Prem)

Rationale: Volume doesn't justify build. But compliance needs require enterprise deployment with data controls. Select vendor with compliance certifications and on-prem option.

Common Mistakes to Avoid

When Building

1. Underestimating calibration: Budget 3-6 months just for calibration
2. Ignoring maintenance: Models need ongoing attention
3. Skipping linguistic expertise: AI alone isn't enough
4. Not planning for scale: Design for 10× your current volume
5. Building too much: Start narrow, expand based on needs

When Buying

1. Not piloting properly: Always test with your actual content
2. Ignoring total cost: Usage fees can exceed subscription
3. Undervaluing integration: Budget for integration work
4. Skipping calibration: Even SaaS needs tuning
5. Vendor lock-in: Plan for potential future migration

Making Your Decision

Use this checklist:

Build If:

• Volume > 5M words/month
• Have available ML engineering capacity
• QA is strategic differentiator
• Unique requirements not served by commercial tools
• Data sensitivity requires complete control
• Budget for 12+ month development timeline
• Committed to ongoing maintenance

Buy If:

• Volume < 2M words/month
• No ML expertise or capacity
• Standard QA requirements
• Need to deploy within 3 months
• Prefer predictable costs
• Want vendor to handle updates and improvements
• Don't want QA to distract from core business

Hybrid If:

• Standard needs with some customization
• Want to preserve future flexibility
• Building internal capability over time
• Volume is growing toward build threshold

FAQ

How much does it really cost to build AI translation QA?

A production-ready custom AI LQA system typically costs $300-500K in the first year (team, infrastructure, API costs) and $150-250K annually to maintain. These costs assume you have access to ML talent. If you need to hire and train, add 6-12 months and $100-200K.

Can I use ChatGPT/Claude directly for translation QA?

Yes, but raw LLM APIs require significant engineering to be production-ready: structured output handling, error recovery, caching, rate limiting, calibration, and integration. This is why "build" costs more than just API fees.

What's the minimum viable build?

At minimum, you need: (1) prompt engineering for MQM-based evaluation, (2) structured output parsing, (3) basic UI for results, (4) integration with your workflow. This takes 3-6 months with 1-2 engineers and produces a basic but functional system.

How do I convince stakeholders to buy instead of build?

Focus on: (1) time-to-value (3 months vs 12), (2) opportunity cost (what else could engineering work on?), (3) total cost comparison including maintenance, (4) risk of build failure or delay. Show that buying allows faster validation of the AI QA approach before committing to build.

When does build become cheaper than buy?

Typically at 5-10M words/month, depending on the commercial solution's pricing and your engineering costs. At lower volumes, buy is almost always more cost-effective. Create a detailed 3-year TCO comparison with your actual numbers.

Conclusion

The build vs buy decision for AI translation QA comes down to:

Build when: QA is strategic, you have capability, volume justifies investment, and you need unique features.

Buy when: You want fast deployment, predictable costs, standard features, and prefer to focus resources elsewhere.

Go hybrid when: You want the best of both—commercial reliability with custom extensions for your specific needs.

Most organizations should start with buy or hybrid, then evaluate build after gaining experience with AI QA in production. This reduces risk while preserving optionality.

Whatever you choose, remember: the goal is better translation quality, not building technology for its own sake. Choose the path that gets you there fastest with acceptable risk.

Ready to evaluate AI-powered translation QA? Try KTTC free and see if a commercial solution meets your needs before committing to build.