Building vs Buying AI for Sports: A Business Decision Framework

Building vs Buying AI: A Sports Business Decision Framework

This decision appears in every sports organization's strategic planning: Should we build proprietary AI systems, or should we license from a vendor?

The answer isn't generic. A betting exchange with 200+ engineers can sustain proprietary ML platforms. A regional sportsbook with 50 people cannot. A rights holder with unique data assets benefits from owning models. A broadcaster without specialized data might be better served buying.

Yet most organizations default to the wrong answer. They build when they should buy (consuming resources, missing faster time-to-market). Or they buy when they should build (surrendering competitive advantage, losing data ownership).

This article provides a decision framework: how to evaluate the build vs. buy decision systematically, what factors matter most, and how to recognize when to transition from one model to another.

The Build vs. Buy Framework

Before jumping to a decision, evaluate your organization across seven dimensions. Your position on these dimensions determines the optimal path.

Dimension 1: Technical Talent Availability

Build requires:

• Machine learning engineers with 3-5 years of production experience
• Data engineers who can manage petabyte-scale pipelines
• Software engineers for model serving and infrastructure
• Product managers who understand both business and ML constraints

Current market reality:

• US average ML engineer salary: $180K-$250K
• Good senior talent is scarce; hiring takes 3-6 months per engineer
• Retention is challenging (tech company counteroffers, etc.)
• Building a team of 5 qualified ML engineers costs $1-1.5M annually in salary + benefits

Questions to ask yourself:

• Do we have 3+ ML engineers in-house today?
• Can we hire and retain top talent in our market (NYC, SF, London) or do we have to hire elsewhere (higher cost)?
• Do we have infrastructure/DevOps engineers who understand ML deployment?

Scoring:

• If you have 5+ senior ML engineers: Build is feasible
• If you have 2-4: Hybrid (partner for base layer, build specialized models)
• If you have 0-1: Buy

Dimension 2: Proprietary Data Assets

Build is attractive if you have:

• Unique player performance data (e.g., you're a rights holder with exclusive tracking data)
• Historical betting or interaction data spanning 5+ years
• Specialized context (injury databases, weather integration, tactical data) that competitors don't have
• The ability to continuously collect and improve this data

Build is risky if:

• Your data is similar to what competitors can access
• You don't have a data pipeline to continuously update models
• Your data is siloed across departments and difficult to integrate

Real-world example: La Gazzetta dello Sport benefits from proprietary Serie A data (access to Italian football clubs, exclusive injury databases). They can build defensible models that competitors struggle to replicate. A regional sportsbook without exclusive data would struggle.

Questions to ask yourself:

• Do we have data that competitors can't easily replicate?
• Can we legally and ethically monetise this data?
• Do we have systems to continuously update and improve our data?

Scoring:

• Unique proprietary data in multiple categories: Build is attractive
• Some unique data but mostly public sources: Hybrid
• Mostly public data accessible to all: Buy

Dimension 3: Competitive Differentiation Goals

Build makes sense if:

• Proprietary AI is core to your competitive strategy
• You're competing in a winner-take-most market (e.g., sportsbook margins are compressing; you need best-in-class odds to survive)
• You plan to monetise AI (licensing predictions to others)
• Your time horizon is 3-5 years (long enough to amortize development cost)

Buy makes sense if:

• AI is table stakes but not core differentiation
• You compete on other factors (brand, distribution, customer service, UI)
• Your time horizon is 12-18 months (you need speed over long-term ownership)
• You're risk-averse and prefer known costs over uncertain development

Real-world example: A global betting exchange (DraftKings, Betfair) might justify building because proprietary odds generation is a core competitive advantage, worth $10M+ investment. A local sportsbook would waste resources building the same system.

Questions to ask yourself:

• Is AI core to our 3-year strategy, or is it supporting?
• Are margins compressing in our category (suggesting we need best-in-class models)?
• Do we plan to monetise AI in other ways (licensing, selling data)?

Scoring:

• AI is core competitive differentiator: Build
• AI is important but not core: Hybrid
• AI is supporting infrastructure: Buy

Dimension 4: Budget and Runway

Build costs approximately:

• Year 1: $1.5-3M (team hiring, infrastructure, initial model development, often 0 revenue)
• Year 2: $1.5-2.5M (ongoing team, infrastructure, model improvements)
• Year 3+: $1.5-2.5M (sustaining, incremental improvements)
• Total 3-year commitment: $4.5-8M

Buy costs approximately:

• Year 1: $100K-$500K (depending on scope, number of sports, volume)
• Year 2: $150K-$750K (growing volume, additional features)
• Year 3: $200K-$1M (scale)
• Total 3-year commitment: $450K-$2.25M

The buy model is 2-4x cheaper than the build model. But it assumes no hidden costs (integration, training, support) and no switching costs if you become unhappy.

Questions to ask yourself:

• What's our runway? Do we have 3+ years of capital?
• What's our revenue model and time to profitability?
• Can we afford a $5-8M investment that might not directly generate ROI for 3-4 years?

Scoring:

• Well-funded, profitable, can afford 5+ year horizons: Build is feasible
• Moderately funded, need to prove ROI in 18-24 months: Hybrid
• Limited budget, need to conserve capital: Buy

Dimension 5: Time-to-Market Urgency

Build timeline:

• Planning and hiring: 2-3 months
• Data infrastructure setup: 2-4 months
• First models deployed to production: 4-6 months
• Optimisation and improvement: ongoing (6-12 months before competitive)
• Total: 6-12 months before you have competitive AI systems

Buy timeline:

• Vendor selection: 1-2 months
• Integration: 2-4 weeks
• Testing and tuning: 2-4 weeks
• Go-live: 4-8 weeks
• Total: 2-4 months

This 3-6 month speed advantage is massive. If you're competing in a rapidly moving market, it can be the difference between capturing a segment and missing it.

Real-world example: A sportsbook launching in a new state (e.g., Illinois) has 6-12 months to establish product/market fit before competitors arrive. Building proprietary AI takes 12+ months. Buying allows go-to-market in 2 months. The difference is winning or losing that market.

Questions to ask yourself:

• How much time do we have before competitive threats emerge?
• Are we in a market where speed to market determines winners?
• Can we wait 12+ months to go live?

Scoring:

• 24+ month runway before competitive threat: Build is feasible
• 12-18 month runway: Hybrid
• <12 month runway: Buy

Dimension 6: Complexity of Your Use Case

Simple use cases (good for buying):

• Basic prop bet pricing (apply margin to public data)
• Player performance prediction using standard statistics
• Simple injury risk flags
• Standard responsible gambling detection

Complex use cases (good for building):

• Multi-model ensemble that requires custom training on proprietary data
• Real-time tactical analysis requiring 20+ feature streams
• Custom player prop generation for niche markets
• Proprietary prediction systems that monetise to external customers

Questions to ask yourself:

• Can a standard AI platform (with minimal customization) handle our use case?
• Or do we need bespoke models for our specific context?
• Are we in a niche where off-the-shelf solutions don't exist?

Scoring:

• Standard use case with existing solutions: Buy
• Partially custom, some unique requirements: Hybrid
• Highly specialized niche with no standard solutions: Build

Dimension 7: Long-Term Strategic Vision

Build if:

• You envision AI as a core product you'll sell to other organizations
• You plan to remain in sports tech for 10+ years
• Owning your data and models is strategically important

Buy if:

• You're testing the market or uncertain about long-term commitment
• You plan to pivot or exit within 5 years
• Vendor partnerships are part of your go-to-market strategy

Questions to ask yourself:

• What does our 5-10 year vision look like for this company?
• Is AI ownership core to that vision, or a supporting tool?
• Do we want to be a technology vendor or a business operator?

Scoring:

• Long-term tech company building tech products: Build
• Long-term business operator using tech as tool: Buy
• Uncertain, testing: Hybrid

Scoring Methodology: Your Build vs. Buy Score

Sum your scores across the seven dimensions:

If you scored mostly "Build": Proprietary development is the right path. You have talent, unique data, long time horizon, and strategic reasons to own the technology. Invest in building.

If you scored mostly "Hybrid": Partner for baseline capabilities, but build specialized models on top. This is the most common path for mid-to-large organizations. Reduces risk, unlocks competitive advantages.

If you scored mostly "Buy": Outsource to a vendor. Focus your team on integration, configuration, and using AI to drive business results. Speed to market and cost control are your priorities.

Case Studies: Real Decisions

Case Study 1: DraftKings (Global Betting Exchange)

• Technical talent: 100+ engineers, 15+ ML specialists
• Proprietary data: 10+ years of betting history, millions of daily bets
• Competitive differentiation: Margins depend on odds accuracy; proprietary AI is core strategy
• Budget: $500M+ invested in tech infrastructure
• Time-to-market: Mature company, can afford longer development cycles
• Decision: Build

Rationale: DraftKings can justify building because they have scale, capital, unique data, and competitive need. Proprietary odds generation is defensible and generates real competitive advantage.

Outcome: Built proprietary models for prop pricing, player performance, and injury risk. Models feed into odds generation. Proprietary system is a competitive advantage against smaller operators.

Case Study 2: La Gazzetta dello Sport (Italian Sports Media)

• Technical talent: 10-15 engineers, 2-3 ML specialists
• Proprietary data: Access to Serie A clubs, Italian football ecosystem
• Competitive differentiation: Data journalism and insights drive readership; AI enhances both
• Budget: $5-10M annually in tech
• Time-to-market: Media company, can invest for 3-5 year payoff
• Decision: Hybrid

Rationale: Gazzetta has some unique Italian football data but limited engineering scale. Better to partner with an AI platform for base infrastructure, then build specialized models leveraging Italian football data.

Outcome: Partnered with FairPlay's FairPlay AI engine for core prediction capabilities, then built proprietary models for:

• Italian club performance analysis
• Serie A player prop optimisation
• Engagement scoring for editorial content

Result: Reduced development cost to $500K annually (vendor fees + specialized team), faster time-to-market, competitive differentiation through Italian football expertise.

Case Study 3: Regional Sportsbook

• Technical talent: 3-4 engineers, 1 data analyst
• Proprietary data: 2-3 years of regional betting history (limited)
• Competitive differentiation: Local market knowledge, customer relationships (not AI)
• Budget: $1-3M annually in tech
• Time-to-market: Need to launch in 6 months to compete with DraftKings/FanDuel
• Decision: Buy

Rationale: Small operator doesn't have capital, talent, or unique data to justify building. Speed to market is critical. A vendor solution can be deployed in 8 weeks.

Outcome: Licensed AI prop analysis platform, integrated with existing sportsbook in 8 weeks. Went to market with competitive prop offerings without building proprietary system. Used saved budget to invest in customer acquisition and local marketing (their actual competitive advantage).

The Hidden Cost of Building: What Most Organizations Underestimate

Organizations that decide to build often underestimate the true cost:

Hidden cost 1: Opportunity cost of engineering time. Your 5 best engineers spend 12+ months building an ML platform. That's 12 months they're NOT building customer-facing features. Revenue impact: often significant.

Hidden cost 2: Infrastructure and DevOps. Running production ML requires infrastructure (GPUs, data pipelines, monitoring). $500K-$1M annually. This is rarely budgeted upfront.

Hidden cost 3: Data quality and engineering. Models are only as good as data. Building data pipelines and ensuring data quality is 50% of the effort. Budget accordingly.

Hidden cost 4: Hiring and retention. ML engineers are expensive and hard to retain. Plan for 15-20% annual turnover, recruitment costs, and onboarding time.

Hidden cost 5: Model maintenance and drift. Models degrade over time as data distributions change. You need ongoing resources to monitor, retrain, and improve. This is not a one-time cost.

Hidden cost 6: Regulatory and compliance. If you're operating AI in regulated markets, you need audit trails, explainability, and compliance documentation. Build-vs-buy frameworks often ignore this.

The true build cost is often 40-50% higher than initial estimates.

Hybrid Models: The Sweet Spot

Most successful organizations (70%) use a hybrid approach:

Partner with a vendor for:

• Core prediction models (odds, player performance)
• Infrastructure (API, data pipelines, monitoring)
• Compliance and regulatory support
• 24/7 support and SLAs

Build proprietary systems for:

• Custom props leveraging unique data
• Specialized markets or edge cases
• Models that directly monetise (licensing to others)
• Competitive differentiation in specific domains

Example: FairPlay partnership model

An operator partners with FairPlay's FairPlay AI engine for:

• 1.1 billion predictions annually across 20+ sports
• Real-time prop pricing
• Player injury risk detection
• Engagement recommendations

Then builds proprietary models for:

• Custom state-specific props (e.g., Oklahoma-specific college football props)
• Niche markets they service uniquely
• Predictions they license to other operators

Result: 80% of core capability from vendor (faster, cheaper, less risk), 20% of competitive differentiation from proprietary systems (leverages unique data and market position).

Cost: $200-500K annually to vendor + $300-500K annually for proprietary team. Much cheaper and faster than building everything from scratch ($2-3M annually), but still captures differentiation.

When to Transition From Buy to Build

Some organizations start with buying, then transition to building once they've achieved scale and clarity. This is a valid path.

Transition signals:

• You've proved the market (3+ years of profitable operation)
• You have $10M+ annual revenue from AI-driven products
• You've hired 10+ ML engineers and have engineering leadership
• Your competitive landscape requires proprietary differentiation
• You've identified specific use cases where vendor solutions are limiting

Transition timeline: 6-12 months to move from vendor dependency to proprietary systems while maintaining operations.

Transition risk: If you transition too early, you waste resources. If you transition too late, you miss competitive advantages. The right time is when you have clarity on product-market fit and the capital to invest.

The Vendor Selection Checklist

If you decide to buy, here's how to evaluate vendors:

1. Accuracy: Request backtests on historical data. What's the prediction accuracy on your specific use cases?

2. Coverage: What sports, leagues, and markets do they cover? If you operate in 10 sports, can they service all 10?

3. Customization: Can they handle your specific prop types, markets, or regulatory requirements? Or do they force you into their constraints?

4. Data freshness: How often are predictions updated? Real-time is table stakes now (every 2-5 minutes).

5. Integration ease: How long to integrate into your stack? If it's 3+ months, that's a problem.

6. Cost structure: Per-sport licensing? Per-prediction API calls? Revenue share? Understand the economics at scale.

7. Support: Do they offer 24/7 support? What's their SLA for critical issues?

8. Moat and defensibility: Are they building defensible models, or are they just repackaging public data? Long-term, you want a vendor with its own moat.

9. Financial stability: Is the vendor VC-funded (might shut down if they don't reach fundraising milestones)? Profitable? Sustainable?

10. Roadmap alignment: Where are they investing for the next 2-3 years? Does it align with your needs?

Next Steps: Your Decision Framework

Use this framework to systematically evaluate the build vs. buy decision for your organization:

1. Score yourself on the seven dimensions (talent, data, differentiation, budget, time, complexity, vision).

2. Identify your bottleneck. Which single dimension is constraining your decision? Is it budget? Talent? Time-to-market?

3. Model the scenarios. Run the numbers for build, buy, and hybrid. What's the cost and timeline for each?

4. Align with strategy. Which path best supports your 3-5 year strategic vision?

5. Start with a pilot. Whether you build or buy, start with a limited pilot (one sport, one market) before scaling.

If you're evaluating buying, FairPlay's FairPlay AI engine powers prediction intelligence for 45+ regulated markets and 20+ sports. We generate 1.1 billion predictions annually, process 125 million daily price changes, and power partnerships with premium US sports publishers, La Gazzetta dello Sport, MARCA,.

Whether you're deciding to build or buy, we can help you think through the tradeoffs for your specific situation. Let's start a conversation.