Building AI into Your Product Strategy
Evaluating AI Vendors vs Build
5 min read
One of your biggest AI decisions: build custom models or use vendor APIs? Here's how to decide.
The Build vs Buy Matrix
| Factor | Build In-House | Buy (API/Vendor) |
|---|---|---|
| Time to market | 3-12 months | Days to weeks |
| Upfront cost | High (team, infrastructure) | Low (pay per use) |
| Ongoing cost | Lower at scale | Higher at scale |
| Control | Full | Limited |
| Customization | Unlimited | Vendor constraints |
| Data privacy | Data stays internal | Data sent to vendor |
| Maintenance | Your responsibility | Vendor handles |
When to Build
Build when you have:
- Proprietary data that creates competitive advantage
- Unique use case not well-served by general APIs
- Scale that makes API costs prohibitive (>$50K/month)
- Privacy requirements that prevent data leaving your infrastructure
- Long-term investment timeframe (2+ years)
- ML expertise on your team
When to Buy
Buy when you have:
- Speed as the priority (need results in weeks, not months)
- Standard use case (summarization, classification, generation)
- Limited scale that keeps API costs manageable
- No ML team and don't want to build one
- Uncertain requirements that need experimentation
- Commodity AI needs (text generation, image recognition)
Vendor Evaluation Framework
Category 1: Technical Fit
| Criteria | Questions to Ask | Red Flags |
|---|---|---|
| Accuracy | What's the benchmark accuracy? Can we test on our data? | No benchmarks available |
| Latency | What's p50/p95/p99 latency? | No SLAs provided |
| Scale | Rate limits? Burst capacity? | Can't handle your volume |
| Languages | What languages supported? | Missing your key markets |
| Customization | Can we fine-tune? Prompt engineering support? | Rigid, no customization |
Category 2: Business Terms
| Criteria | Questions to Ask | Red Flags |
|---|---|---|
| Pricing | Per-token? Per-request? Volume discounts? | Unclear pricing model |
| Contract | Monthly? Annual? Exit terms? | Long lock-in, harsh penalties |
| SLAs | Uptime guarantee? Latency SLAs? | No SLAs or weak guarantees |
| Support | Response time? Dedicated support? | Email-only, slow response |
Category 3: Data & Security
| Criteria | Questions to Ask | Red Flags |
|---|---|---|
| Data usage | Is our data used for training? | Yes, without opt-out |
| Retention | How long is data retained? | Longer than needed |
| Compliance | SOC 2? GDPR? HIPAA? | Missing your requirements |
| Encryption | At rest? In transit? | No encryption standards |
Cost Calculation Template
API Cost Estimate
Monthly volume: _______ requests
Average input tokens: _______ per request
Average output tokens: _______ per request
Input cost: volume × input_tokens × price_per_input_token
Output cost: volume × output_tokens × price_per_output_token
Total: input_cost + output_cost
Example: GPT-5.4 Pricing (early 2026)
| Volume | Input Tokens | Output Tokens | Monthly Cost |
|---|---|---|---|
| 100K requests | 500 avg | 200 avg | ~$400 |
| 1M requests | 500 avg | 200 avg | ~$4,000 |
| 10M requests | 500 avg | 200 avg | ~$40,000 |
Build Cost Estimate
| Component | One-time | Monthly |
|---|---|---|
| ML Engineers (2 FTEs) | - | $50,000 |
| Infrastructure | $20,000 | $10,000 |
| Training compute | $50,000 | $5,000 |
| MLOps tools | $5,000 | $2,000 |
| Total Year 1 | $75,000 | $67,000/mo = ~$870,000 |
Break-even analysis: If API costs exceed ~$70K/month, building may be cheaper.
Vendor Comparison Checklist
Rate each vendor (1-5):
| Criteria | Vendor A | Vendor B | Vendor C |
|---|---|---|---|
| Accuracy on your data | |||
| Latency meets requirements | |||
| Pricing fits budget | |||
| Data privacy compliance | |||
| Customization options | |||
| Support quality | |||
| Financial stability | |||
| Integration ease | |||
| Total | /40 | /40 | /40 |
Risk Assessment
Vendor Risks
| Risk | Mitigation |
|---|---|
| Vendor price increase | Multi-vendor strategy, build readiness |
| Vendor discontinues service | Exit plan, data portability |
| Quality degradation | Continuous monitoring, fallback vendor |
| Data breach | Security audits, minimize data sent |
Build Risks
| Risk | Mitigation |
|---|---|
| Team turnover | Documentation, knowledge sharing |
| Model performance | Iterative development, benchmarks |
| Infrastructure costs | Cloud cost monitoring, optimization |
| Opportunity cost | Clear prioritization, phased approach |
Decision Framework
Use this flowchart:
Is data privacy critical?
├── Yes → Build (or self-hosted open source)
└── No ↓
Is this a standard use case?
├── Yes → Buy (faster, proven)
└── No ↓
Do you have ML expertise?
├── No → Buy (build expertise first)
└── Yes ↓
Is scale > $50K/month in API costs?
├── Yes → Build (cost savings)
└── No → Buy (speed to market)
Key Takeaway
Start with "buy" as the default for speed, then evaluate "build" if you have unique requirements, scale economics, or data privacy constraints. Most startups should buy first, build later.
Next: You've chosen a vendor or decided to build. How do you work effectively with ML engineering teams? :::