The AI landscape in 2026: OpenAI API ($30/1M output tokens), Anthropic Claude ($75/1M), open-source Llama 3 (free, self-hosted), fine-tuned models (upfront cost, zero marginal cost). Every AI team faces the same strategic question:
Should we pay per token, build our own infrastructure, or fine-tune open models?
According to the AI Strategy Report 2025, 52% of companies regret their initial AI infrastructure decisions, 38% migrate vendors within the first year, and 61% underestimate the engineering effort required for self-hosting.
But teams that run quarterly strategy retrospectives make data-driven build-vs-buy decisions, optimize costs by 45-60%, and avoid costly migrations.
This guide shows you how to run AI strategy retrospectives that evaluate vendor lock-in, calculate true costs (not just API prices), and make strategic decisions based on your usage patterns and engineering capacity.
Table of Contents
- The Build vs Buy Decision Framework
- Cost Analysis: API vs Self-Hosted vs Fine-Tuned
- Vendor Lock-In Considerations
- Fine-Tuning ROI Calculation
- AI Strategy Retrospective Framework
- Tools for AI Infrastructure
- Case Study: Company Migrating from OpenAI to Self-Hosted
- Action Items for Strategic AI Decisions
- FAQ
The Build vs Buy Decision Framework
Option 1: Buy (API Services)
Providers: OpenAI, Anthropic, Google, Cohere
Pros:
- ✅ Zero infrastructure (pay-per-use)
- ✅ Instant access to latest models (GPT-5, Claude 4, etc.)
- ✅ Managed scaling (handle any load)
- ✅ Fast time-to-market (integrate in days)
Cons:
- ❌ Variable costs (scale with usage)
- ❌ Vendor lock-in (APIs change, pricing changes)
- ❌ Data privacy (send data to third parties)
- ❌ Rate limits (throttling during peaks)
When to buy:
- Early stage (validating use cases)
- Low-medium volume (<10M tokens/month)
- Need latest models (competitive advantage)
- Limited engineering resources
Option 2: Build (Self-Hosted Open Models)
Models: Llama 3, Mixtral, Gemma, Qwen
Pros:
- ✅ Fixed costs (hardware + engineers)
- ✅ No vendor lock-in (control your infrastructure)
- ✅ Data privacy (nothing leaves your servers)
- ✅ Unlimited usage (no rate limits or per-token costs)
Cons:
- ❌ High upfront cost (hardware, setup, engineering)
- ❌ Engineering overhead (deployment, monitoring, optimization)
- ❌ Model lag (open models 6-12 months behind frontier)
- ❌ Complexity (scaling, reliability, maintenance)
When to build:
- High volume (>100M tokens/month)
- Data sensitivity (regulated industries, competitive data)
- Cost optimization (predictable, high usage)
- Strong ML engineering team
Option 3: Fine-Tune (Customized Model)
Approaches: Fine-tune GPT-4, fine-tune open models
Pros:
- ✅ Optimized for your use case (better quality)
- ✅ Lower inference costs (smaller, specialized models)
- ✅ Competitive moat (unique capabilities)
- ✅ Potential for dramatic improvements (50-80% better on specific tasks)
Cons:
- ❌ High upfront cost ($5K-50K+ for data + training)
- ❌ Time investment (4-12 weeks from start to production)
- ❌ Ongoing maintenance (model updates, retraining)
- ❌ Data requirements (need 500-10K quality examples)
When to fine-tune:
- Domain-specific use case (legal, medical, technical)
- High volume + specific task (economics justify)
- Quality ceiling (off-the-shelf models not good enough)
- Have quality training data
Decision Tree
Start here:
│
├─ Are you validating the use case? (Unsure if AI will work?)
│ └─ YES → Buy (OpenAI/Anthropic API)
│
├─ Do you have <10M tokens/month usage?
│ └─ YES → Buy (API costs < self-hosted)
│
├─ Do you have >100M tokens/month?
│ └─ YES → Consider Build or Fine-tune
│ │
│ ├─ Do you have ML engineering team (3+ engineers)?
│ │ └─ YES → Build (self-hosted open models)
│ │ └─ NO → Stay with APIs, optimize costs
│ │
│ └─ Is quality sufficient with off-the-shelf models?
│ └─ NO → Fine-tune (improve quality + reduce cost)
│ └─ YES → Build (cost optimization only)
│
└─ Are there regulatory/privacy requirements?
└─ YES → Build (keep data on-premises)
Cost Analysis: API vs Self-Hosted vs Fine-Tuned
True Cost Calculation
API costs (variable):
Cost = Tokens × Price per token
Example (GPT-4 Turbo):
- 100M tokens/month × $0.03/1K output tokens = $3,000/month
- 12 months = $36,000/year
- 3 years = $108,000
Scales linearly with usage
Self-hosted costs (fixed + variable):
Upfront:
- Hardware: $20K-50K (4-8 GPUs)
- Setup: $10K-30K (2-4 weeks engineering)
- Total upfront: $30K-80K
Ongoing:
- Infrastructure: $2K-5K/month (cloud GPUs or on-prem power)
- Engineering: $15K-30K/month (1-2 FTE engineers)
- Total ongoing: $17K-35K/month = $204K-420K/year
3-year total: $642K-1.34M
Break-even: When API costs > self-hosted costs
Example: $35K/month API costs = break-even with self-hosted
Fine-tuned costs (upfront + lower variable):
Upfront:
- Data collection/labeling: $5K-20K
- Training: $2K-10K (compute)
- Engineering: $10K-30K (2-4 weeks)
- Total upfront: $17K-60K
Ongoing:
- Inference: 50-80% cheaper than base model (smaller, optimized)
- Retraining: $5K-15K every 6-12 months
- Maintenance: $3K-8K/month engineering
Example: Fine-tuned GPT-4 mini
- Base API: $0.60/1M output tokens
- Fine-tuned: $2.40/1M (training) + $1.20/1M (inference)
- If usage >10M tokens: Fine-tuned cheaper long-term
Break-Even Analysis
Example scenario: 50M tokens/month usage
Option 1: OpenAI API (GPT-4 Turbo)
Cost: 50M × $0.03/1K = $1,500/month = $18K/year
3 years: $54K
Option 2: Self-hosted (Llama 3 70B)
Upfront: $50K (setup)
Ongoing: $25K/month = $300K/year
3 years: $50K + $900K = $950K
Break-even: Never (API much cheaper at this volume)
Option 3: Fine-tuned (GPT-4 mini)
Upfront: $30K (fine-tuning)
Inference: 50M × $0.012/1K = $600/month = $7.2K/year
3 years: $30K + $21.6K = $51.6K
Winner: Fine-tuned (saves $2.4K vs API, $898K vs self-hosted)
Volume-Based Recommendations
Low volume (<10M tokens/month):
- Winner: API (lowest total cost)
- API cost: ~$300-3K/month
- Self-hosted cost: $17K-35K/month (overkill)
Medium volume (10-100M tokens/month):
- Winner: API or fine-tuned (depends on quality needs)
- API cost: $3K-30K/month
- Fine-tuned: Often 50-70% cheaper after upfront investment
- Self-hosted: Still expensive unless data privacy required
High volume (>100M tokens/month):
- Winner: Self-hosted or fine-tuned
- API cost: $30K+/month ($360K/year)
- Self-hosted: $204K-420K/year (break-even at ~50-100M/month)
- Fine-tuned: Cheapest if quality sufficient
Vendor Lock-In Considerations
What is Vendor Lock-In?
Definition: Dependence on a single vendor makes switching costly.
AI-specific lock-in risks:
1. Prompt engineering (prompts optimized for GPT-4 don't work on Claude)
2. API dependencies (code tightly coupled to OpenAI SDK)
3. Model-specific behaviors (users expect GPT-4 style responses)
4. Cost increases (vendor raises prices, you have no alternative)
5. Service degradation (rate limits, API changes, deprecations)
Measuring Vendor Lock-In
Lock-in score (1-10):
lock_in_factors = {
"prompt_portability": 3, # Prompts work across vendors? (1=no, 10=yes)
"api_abstraction": 4, # Abstraction layer? (1=tightly coupled, 10=abstracted)
"model_substitutability": 2, # Can switch models without quality loss? (1=no, 10=yes)
"cost_sensitivity": 8, # How much would 2x cost increase hurt? (1=minor, 10=catastrophic)
"data_portability": 7, # Can export fine-tuning data? (1=no, 10=yes)
}
lock_in_score = 10 - mean(lock_in_factors.values())
# Score 7-10: High lock-in (risky)
# Score 4-6: Medium lock-in (manageable)
# Score 1-3: Low lock-in (portable)
Reducing Vendor Lock-In
Strategy 1: Abstraction layer
# Bad (tightly coupled to OpenAI)
import openai
response = openai.ChatCompletion.create(model="gpt-4", messages=...)
# Good (abstracted)
class LLMProvider:
def generate(self, prompt):
if self.provider == "openai":
return openai.ChatCompletion.create(...)
elif self.provider == "anthropic":
return anthropic.messages.create(...)
# Easy to switch providers
Strategy 2: Multi-vendor approach
# Use different vendors for different use cases
providers = {
"chatbot": "openai", # GPT-4 for conversational
"summarization": "anthropic", # Claude for long docs
"classification": "cohere", # Cohere for structured tasks
}
# Not locked into single vendor
Strategy 3: Prompt portability
# Maintain vendor-agnostic prompt library
prompts:
customer_support:
description: "Helpful customer support agent"
openai_version: "You are a helpful assistant..."
anthropic_version: "You are Claude, a helpful assistant..."
fallback_version: "Generic prompt that works anywhere"
# Can switch vendors without rewriting all prompts
Fine-Tuning ROI Calculation
When Fine-Tuning Makes Sense
Good use cases:
- Domain-specific terminology (legal, medical, technical)
- Consistent style/tone requirements (brand voice)
- Structured outputs (JSON, specific formats)
- High-volume, repetitive tasks (classification, extraction)
- Quality ceiling (off-the-shelf models not good enough)
Poor use cases:
- General conversation (off-the-shelf models excel)
- Low-volume (<1M tokens/month)
- Rapidly changing requirements (fine-tuned models rigid)
- Insufficient training data (<500 examples)
Fine-Tuning Cost-Benefit Example
Scenario: Customer support AI, 20M tokens/month
Option A: GPT-4 Turbo (no fine-tuning)
Quality: 78% user satisfaction
Cost: 20M × $0.03/1K = $600/month
Upfront: $0
Total (1 year): $7,200
Option B: Fine-tuned GPT-4 mini
Quality: 87% user satisfaction (domain-specific training)
Upfront cost: $25K (data collection + training + engineering)
Inference cost: 20M × $0.012/1K = $240/month (smaller model)
Total (1 year): $25K + $2,880 = $27,880
Break-even: 6 months ($600/month savings × 6 = $3,600, doesn't cover $25K yet)
Year 2: Only $2,880/year (vs $7,200/year) = $4,320 savings/year
ROI decision:
- Year 1: Fine-tuning costs more ($27.8K vs $7.2K)
- Year 2+: Fine-tuning saves $4.3K/year
- Quality: Fine-tuned is significantly better (87% vs 78%)
Verdict: Fine-tune if:
- ✅ Quality improvement is worth upfront cost
- ✅ Expect to use for 2+ years (payback period)
- ✅ Have quality training data available
AI Strategy Retrospective Framework
Run quarterly AI strategy retrospectives (every 3 months).
Pre-Retrospective Data Collection
2 weeks before:
[ ] Calculate current API costs (total spend, per-use-case breakdown)
[ ] Estimate token usage by feature (where are tokens going?)
[ ] Assess quality satisfaction (are current models good enough?)
[ ] Research alternatives (new models, pricing changes, open-source options)
[ ] Survey engineering team (capacity for self-hosting? interest in fine-tuning?)
Retrospective Structure (90 min)
1. Cost analysis (20 min)
Current state (Q1 2026):
- Total AI spend: $12,500/month
- Primary vendor: OpenAI (85%), Anthropic (15%)
- Usage: 42M tokens/month
- Cost per token: $0.030 (blended)
Breakdown by use case:
- Customer support: $6,200/month (50%)
- Content generation: $3,800/month (30%)
- Research synthesis: $2,500/month (20%)
Trends:
- Usage growing 15%/month (compounding)
- Projected 12-month cost: $287K
- Projected 24-month cost: $890K (if growth continues)
Discussion:
- Is current spend sustainable?
- What happens if usage 2x or 5x?
- Are we optimized? (right models for right tasks?)
2. Quality assessment (15 min)
Quality by use case:
- Customer support: 82% user satisfaction (good)
- Content generation: 74% acceptance rate (acceptable)
- Research synthesis: 91% accuracy (excellent)
Limitations:
- Customer support: Generic responses, doesn't capture brand voice
- Content generation: Inconsistent style, requires heavy editing
- Research synthesis: Hallucinations on edge cases (9% error rate)
Could fine-tuning help?
- Customer support: YES (brand voice, domain terminology)
- Content generation: YES (consistent style)
- Research synthesis: MAYBE (accuracy is already high)
3. Vendor lock-in assessment (10 min)
Current lock-in score: 7.2/10 (high risk)
Factors:
- Prompts optimized for GPT-4 (hard to port to Claude)
- Code tightly coupled to OpenAI SDK
- Users accustomed to GPT-4 response style
- Cost: 85% of spend with single vendor
Mitigation steps:
- Abstract API calls behind interface layer
- Test critical features with Claude (validate portability)
- Maintain prompt library with cross-vendor versions
4. Strategic options analysis (25 min)
Option 1: Status quo (keep buying API)
Pros: Simple, zero effort, latest models
Cons: Costs growing fast, vendor lock-in
12-month cost: $287K
24-month cost: $890K
Option 2: Fine-tune customer support (highest spend)
Upfront: $30K (data + training + eng)
Ongoing: $2,500/month (inference)
12-month cost: $30K + $30K = $60K (saves $44K vs status quo)
24-month cost: $90K (saves $64K)
Quality: Expected 82% → 88% satisfaction
Decision: Worth it? (saves money + improves quality)
Option 3: Self-host for high-volume use cases
Upfront: $60K (hardware + setup)
Ongoing: $22K/month (infrastructure + eng)
12-month cost: $324K (more expensive than API!)
24-month cost: $588K (still more expensive)
Decision: NOT worth it at current volume
Option 4: Hybrid (fine-tune support, optimize others)
- Fine-tune customer support (saves $44K/year)
- Migrate content generation to GPT-4 mini (saves $15K/year)
- Keep research on GPT-4 Turbo (quality critical)
Total savings: $59K/year
Upfront investment: $30K
ROI: 6-month payback
5. Decision and action items (20 min)
Decision: Pursue Option 4 (hybrid approach)
Action items:
[ ] Q2: Fine-tune customer support model
- Collect 2K support conversations (Owner: Support + ML, Due: Week 2)
- Label examples with quality ratings (Owner: Support leads, Due: Week 4)
- Train fine-tuned model (Owner: ML, Due: Week 6)
- A/B test vs baseline (Owner: ML + Product, Due: Week 8)
- Deploy if quality >85% (Owner: Eng, Due: Week 10)
[ ] Q2: Migrate content generation to GPT-4 mini
- Test quality on 100 examples (Owner: Content team, Due: Week 2)
- If quality acceptable, roll out (Owner: Eng, Due: Week 4)
[ ] Q3: Implement API abstraction layer
- Design LLM provider interface (Owner: Eng lead, Due: Week 2)
- Implement for OpenAI + Anthropic (Owner: Eng, Due: Week 6)
- Test critical features with Claude (Owner: QA, Due: Week 8)
[ ] Q4: Re-evaluate based on results
- Review fine-tuning ROI (Did we save money? Improve quality?)
- Assess if self-hosting makes sense (Has volume grown enough?)
- Make next strategic decision
Tools for AI Infrastructure
API Providers
1. OpenAI
- $10-30 per 1M tokens (model-dependent)
- GPT-4, GPT-4 Turbo, GPT-4 mini
- Fine-tuning available
- Best for: General use, latest models
2. Anthropic
- $15-75 per 1M tokens
- Claude 3 (Opus, Sonnet, Haiku)
- 200K context window
- Best for: Long documents, nuanced tasks
3. Google Gemini
- $0.50-7 per 1M tokens
- Gemini Pro, Ultra
- Multimodal (vision, audio)
- Best for: Cost-sensitive, multimodal
Self-Hosting Platforms
4. vLLM
- Free (open-source)
- High-throughput inference
- Supports Llama, Mixtral, etc.
- Best for: Production self-hosted inference
5. Ollama
- Free (open-source)
- Easy local model running
- 30+ models supported
- Best for: Development and testing
6. Together AI
- $0.20-2 per 1M tokens
- Managed open models (Llama, Mixtral)
- Easier than self-hosting
- Best for: Open models without infrastructure hassle
Fine-Tuning Platforms
7. OpenAI Fine-Tuning
- $8-120 per 1M training tokens (model-dependent)
- Fine-tune GPT-4, GPT-4 mini
- Managed training
- Best for: Quick fine-tuning on OpenAI models
8. Anyscale
- $3-5 per 1M tokens (fine-tuned inference)
- Fine-tune open models (Llama, Mistral)
- Ray-based training
- Best for: Fine-tuning open models at scale
9. HuggingFace AutoTrain
- $0 (DIY) or managed ($$$)
- Fine-tune any open model
- Custom training pipelines
- Best for: Custom fine-tuning workflows
Cost Monitoring
10. Helicone
- Free (limited), paid from $99/month
- Real-time cost tracking across vendors
- Budget alerts
- Best for: Multi-vendor cost visibility
Case Study: Company Migrating from OpenAI to Self-Hosted
Company: Enterprise SaaS, 150M tokens/month, data-sensitive industry
Initial state (Month 0):
Vendor: 100% OpenAI (GPT-4)
Cost: 150M × $0.03/1K = $4,500/month = $54K/year
Quality: 84% user satisfaction
Issue: Data privacy concerns (customers worried about sending data to OpenAI)
Strategic decision: Migrate to self-hosted open models
Migration Journey
Month 1-2: Planning and setup
Costs:
- Hardware (8× A100 GPUs): $120K
- Engineering (2 FTE × 2 months): $60K
- Total upfront: $180K
Activities:
- Select model: Llama 3 70B (best open model)
- Deploy infrastructure: vLLM on Kubernetes
- Benchmark quality: Llama 3 vs GPT-4 on test set
Quality comparison:
GPT-4: 84% user satisfaction
Llama 3 70B (off-the-shelf): 76% satisfaction (8% drop)
Fine-tuned Llama 3 70B: 82% satisfaction (2% drop, acceptable)
Decision: Fine-tune Llama 3 to close quality gap
Month 3-4: Fine-tuning and deployment
Fine-tuning costs:
- Data collection: $15K (annotate 5K examples)
- Training: $8K (compute)
- Engineering: $30K (1 FTE × 1 month)
- Total: $53K
Results:
- Fine-tuned model: 82% satisfaction (vs 84% baseline)
- Quality gap: 2% (acceptable for privacy gains)
Month 5: Production deployment
Rollout:
- Week 1: 10% traffic to self-hosted
- Week 2: 25% traffic
- Week 3: 50% traffic
- Week 4: 100% traffic (full migration)
Monitoring:
- Performance: P95 latency 2.8s (vs 2.1s with OpenAI, acceptable)
- Quality: 82% satisfaction (stable)
- Incidents: 2 minor (API timeouts, resolved quickly)
Results (Month 6 onwards)
Cost savings:
OpenAI (before): $4,500/month = $54K/year
Self-hosted (after):
- Infrastructure: $3,500/month (GPUs, networking)
- Engineering: $15K/month (1 FTE ongoing)
- Total: $18.5K/month = $222K/year
Year 1 total: $180K (upfront) + $222K (ongoing) = $402K
- OpenAI would have been: $54K
- Overspent by $348K in Year 1 (ouch!)
Year 2 total: $222K (vs $54K OpenAI)
- Still overspending by $168K/year
Break-even: Never at this volume!
Strategic outcome:
Financial: Lost money (self-hosting more expensive)
Privacy: Won (data never leaves company servers)
Customer trust: Increased (enterprise customers prefer on-prem)
Contract wins: 3 major deals citing data privacy ($2M+ revenue)
ROI calculation:
- Cost increase: $348K (Year 1)
- Revenue increase: $2M+ (new contracts)
- Net benefit: $1.65M+ (positive ROI despite higher costs)
Key learnings:
- Self-hosting isn't always cheaper: At 150M tokens/month, OpenAI was cheaper
- Non-financial benefits matter: Data privacy enabled new revenue
- Quality gap is real: Open models lag frontier models (but fine-tuning helps)
- Engineering overhead is significant: 1 FTE ongoing (underestimated this)
- Strategic decisions aren't always about cost: Sometimes pay more for strategic reasons
Action Items for Strategic AI Decisions
Month 1: Baseline Assessment
[ ] Calculate current AI costs (total, by use case, by vendor)
[ ] Estimate token usage trends (growth rate, projections)
[ ] Assess quality by use case (user satisfaction, accuracy)
[ ] Identify pain points (cost, quality, vendor lock-in)
[ ] Document current infrastructure (models, prompts, APIs)
Owner: Product + Eng leads
Due: Month 1
Month 2: Strategic Options Analysis
[ ] Model current costs 12-24 months forward (assume growth)
[ ] Calculate break-even for self-hosting (at what volume?)
[ ] Identify fine-tuning candidates (high-volume, quality ceiling)
[ ] Research vendor alternatives (pricing, features, quality)
[ ] Assess engineering capacity (can we self-host? fine-tune?)
Owner: Strategy team
Due: Month 2
Month 3: Pilot and Validate
[ ] Run pilot: Test alternative model/vendor on subset of traffic
[ ] Measure quality impact (compared to baseline)
[ ] Calculate actual costs (not just estimates)
[ ] Get team feedback (engineering, product, users)
[ ] Make go/no-go decision based on data
Owner: Full team
Due: Month 3
Quarterly: Strategy Retrospective
[ ] Review costs (actual vs projected, trends)
[ ] Review quality (user satisfaction, accuracy)
[ ] Assess strategic position (vendor lock-in, alternatives)
[ ] Make strategic decisions (stay, migrate, fine-tune)
[ ] Update roadmap based on decisions
Owner: Leadership + Product + Eng
Due: Every quarter
FAQ
Q: At what token volume should we consider self-hosting?
A: Very rough guidelines (depends on many factors):
<10M tokens/month: API only (self-hosting not worth it)
10-50M tokens/month: API or fine-tuned API (self-hosting likely too expensive)
50-200M tokens/month: Depends on engineering capacity and data sensitivity
>200M tokens/month: Self-hosting often cheaper (if you have ML engineering team)
Key factors beyond volume:
- Engineering capacity (do you have 2-3 ML engineers?)
- Data sensitivity (regulatory requirements?)
- Quality needs (are open models good enough?)
- Growth trajectory (scaling up or stable?)
Test: If API costs >$50K/month, seriously evaluate self-hosting.
Q: Should we fine-tune on OpenAI or self-host an open model?
A: Depends on volume and control needs:
Fine-tune on OpenAI (e.g., GPT-4 mini):
- ✅ Easier (managed training and inference)
- ✅ Better base quality (GPT-4 > most open models)
- ✅ Faster time-to-market (weeks vs months)
- ❌ Still vendor lock-in (dependent on OpenAI)
- ❌ Ongoing API costs (lower, but still per-token)
Self-host fine-tuned open model (e.g., Llama 3 70B):
- ✅ Full control (no vendor lock-in)
- ✅ Fixed costs (no per-token charges)
- ✅ Data privacy (never leaves your servers)
- ❌ Engineering effort (significant)
- ❌ Lower base quality (Llama 3 < GPT-4)
Decision matrix:
| Factor | OpenAI Fine-Tune | Self-Hosted Open |
|--------|------------------|------------------|
| Volume | <100M tokens/month | >100M tokens/month |
| Eng capacity | Limited (1-2 engineers) | Strong (3+ ML engineers) |
| Data sensitivity | Low-medium | High (regulated) |
| Time-to-market | Fast (2-4 weeks) | Slow (2-3 months) |
| Quality needs | High | Medium-high |
Q: How do we future-proof against vendor price increases?
A: Multi-pronged approach:
1. Abstraction layer (technical):
# Don't hardcode vendor
response = llm_provider.generate(prompt)
# Easy to switch if OpenAI 2x prices
if openai_too_expensive:
llm_provider = AnthropicProvider()
2. Multi-vendor strategy (operational):
Use cases by vendor:
- High-value, complex: OpenAI GPT-4
- Medium-value: Anthropic Claude or Google Gemini
- High-volume, simple: Self-hosted Llama 3 or fine-tuned mini
If one vendor raises prices, shift traffic
3. Contractual (legal):
Enterprise contracts with:
- Price lock for 12-24 months
- Volume discounts (the more you use, the cheaper)
- Renegotiation clauses
4. Strategic reserves (financial):
Budget assumption: Assume 20-30% annual AI cost increase
Reserve budget for migrations ($50K-200K)
Monitor vendor pricing trends closely
Q: What if fine-tuned model needs retraining frequently?
A: Retraining costs make fine-tuning less attractive:
Stable use cases (retrain 1-2x/year):
- Customer support (domain stable)
- Classification (categories don't change)
- Extraction (format consistent)
- Verdict: Fine-tuning worth it
Changing use cases (retrain 4-12x/year):
- Content generation (style trends change)
- Current events (world knowledge outdated)
- Rapidly evolving domains
- Verdict: Stay with frontier models (always up-to-date)
Cost of retraining:
Each retraining cycle:
- New data collection: $3K-10K
- Training: $2K-8K
- Testing: $2K-5K
- Deployment: $1K-3K
- Total: $8K-26K per retrain
If retraining >4x/year: $32K-104K/year ongoing
This erodes fine-tuning savings
Mitigation:
- Build retraining pipelines (automate, reduce cost)
- Use few-shot learning instead (no retraining needed)
- Hybrid: Fine-tune for stable core, prompt for changing edges
Q: How do we know if an open model is "good enough" for our use case?
A: Run head-to-head comparison:
Step 1: Define "good enough"
Example criteria:
- User satisfaction: >80%
- Task completion: >85%
- Accuracy: <10% error rate
- Response time: <5s P95
Step 2: A/B test
Traffic split:
- 50% to GPT-4 (baseline)
- 50% to Llama 3 70B (test)
Run for 1 week, 1000+ samples each
Step 3: Compare metrics
Results:
GPT-4:
- User satisfaction: 84%
- Task completion: 88%
- Accuracy: 7% error rate
Llama 3 70B:
- User satisfaction: 78% (6% lower)
- Task completion: 82% (6% lower)
- Accuracy: 12% error rate (5% higher)
Decision: Llama 3 is NOT good enough (fails criteria)
Options:
- Fine-tune Llama 3 (improve to 82% satisfaction, 9% error)
- Use GPT-4 mini instead (cheaper than GPT-4, better than Llama 3)
- Stay with GPT-4 (quality is worth cost)
Q: What's the risk of OpenAI/Anthropic shutting down or changing APIs?
A: Real but manageable risk:
Mitigation strategies:
1. Abstraction (reduces switching cost):
- Wrap API calls in interface layer
- Test switching to backup vendor quarterly
- Maintain prompt library for multiple vendors
2. Diversification (reduce single-vendor risk):
- Use 2-3 vendors for different use cases
- Never put 100% of usage on single vendor
3. Strategic reserves (financial buffer):
- Budget $50K-200K for emergency migration
- Plan assumes 3-6 months to switch vendors fully
4. Monitoring (early warning):
- Track vendor health: uptime, support responsiveness, API changes
- Subscribe to vendor updates, engage with account managers
- Participate in vendor communities (early warning of issues)
5. Contractual (legal protection):
- Enterprise contracts with SLAs
- Advance notice clauses (90-180 days for major changes)
- Refund clauses if service degraded
Realistic risk assessment:
- OpenAI/Anthropic shutting down: <5% (very low, they're well-funded)
- API breaking changes: 20-30% (happens, but usually with migration path)
- Pricing increases: 60-80% (likely over 3-5 years)
Biggest risk: Not vendor shutdown, but becoming uncompetitive if you don't optimize costs.
Conclusion
AI infrastructure decisions are strategic, not just technical. Buy vs build vs fine-tune impacts costs, quality, vendor lock-in, and engineering capacity for years.
Key takeaways:
- Calculate total cost of ownership: API costs vs self-hosted (infrastructure + engineering)
- Volume determines strategy: <10M tokens = API, >100M tokens = consider self-hosting
- Fine-tuning ROI depends on volume and quality needs: High-volume + quality ceiling = fine-tune
- Reduce vendor lock-in proactively: Abstraction layers, multi-vendor, prompt portability
- Run quarterly strategy retrospectives: Costs and landscape change fast
- Non-financial factors matter: Data privacy, compliance, customer trust
- Start with APIs, optimize later: Don't self-host prematurely (validate use case first)
The teams that master AI strategy retrospectives in 2026 will make data-driven decisions, avoid costly migrations, and optimize costs while maintaining quality.
Related AI Retrospective Articles
- AI Product Retrospectives: LLMs, Prompts & Model Performance
- AI Feature Launch Retrospectives: Shipping LLM Products
- AI Team Culture Retrospectives: Learning & Experimentation
- LLM Evaluation Retrospectives: Measuring AI Quality
Ready to make strategic AI infrastructure decisions? Try NextRetro's AI strategy retrospective template – evaluate costs, vendor lock-in, and build-vs-buy tradeoffs with your team.
