Product manager perspective: The open vs closed debate is really about product strategy, not just technology.
The Product Question
At the “Building AI Products” session today, speaker asked:
“Is your AI a feature or your product?”
That question determines your open vs closed strategy.
If AI is a FEATURE → Closed APIs
Examples:
- E-commerce site adding AI product recommendations
- CRM adding AI email drafting
- Accounting software adding AI receipt scanning
Why closed makes sense:
Fast time-to-market: Integrate API in weeks, not months- Focus on core product: Don’t distract engineering with ML ops
- Best-in-class UX: Closed models still lead on quality
- Predictable costs: Can estimate API costs per user
Your competitive advantage is NOT the AI model. It’s your core product + AI enhancement.
Spending 6 months building open source infrastructure = 6 months competitors are shipping.
If AI is your PRODUCT → Consider Open Source
Examples:
- AI-powered legal document analysis
- AI coding assistant
- AI medical diagnosis support
Why open source makes sense:
- Differentiation: Fine-tune on proprietary data = competitive moat
- Cost at scale: Your product IS the AI, so volume is huge
- Control: Can’t let OpenAI pricing changes destroy your margins
- Customization: Product experience depends on model behavior
If GPT-5 is 10% better than GPT-4, you get the benefit automatically.
But if GPT-5 pricing doubles, you’re at their mercy.
Real Product Example from the Session
Company A: Project management SaaS + AI task suggestions
- AI = nice-to-have feature
- Using Claude API
- Cost: $0.02 per user per month
- Strategy: Closed APIs forever, not even considering open source
Company B: AI-powered contract analysis tool
- AI = the entire product
- Started on GPT-4, moved to fine-tuned Llama 3
- Cost went from $2.50 per contract to $0.30 per contract
- Strategy: Open source + continuous fine-tuning = product moat
Same technology (LLMs), completely different strategies.
The “Build vs Buy” Framework for AI
Traditional software: Build vs buy
AI software: API vs self-host
Ask these questions:
1. What percentage of COGS is AI?
- <5%: Stay on APIs
- 5-20%: Evaluate open source
-
20%: Probably need open source at scale
2. Is model quality a competitive differentiator?
- No: Use best available API
- Yes: Consider fine-tuning open source
3. How fast is your AI usage growing?
- Slow/stable: APIs are fine
- 100%+ YoY: Open source economics improve
4. Do you have ML talent?
- No: APIs (hiring is hard and expensive)
- Yes: You have options
5. What’s your funding situation?
- Well-funded: Can invest in open source infrastructure
- Bootstrapped/lean: APIs keep burn rate lower initially
The Hybrid Product Strategy
Most innovative approach from the session:
Start with closed APIs (speed to market)
→ Validate product-market fit
→ Understand your AI workloads
→ Build internal ML capability
Transition to hybrid (optimize economics)
→ Move high-volume workloads to open source
→ Keep customer-facing on closed APIs (quality + safety)
→ Use open source for experimentation
End state: Strategic flexibility
→ Can switch models as better options emerge
→ Not locked into vendor pricing
→ Can customize for competitive advantage
The 94% Stat is the Key Insight
Remember @eng_director_luis mentioned:
94% of organizations using 2+ LLM providers
This is the winning strategy. Don’t put all your eggs in one basket.
Our product roadmap:
- Customer chat: Claude (best UX, safety)
- Document processing: Llama 3.1 fine-tuned (cost + customization)
- Internal tools: Mixture of models (choose best for each use case)
- Evaluation: GPT-4 as baseline comparison
Product Manager’s Decision Framework
Choose CLOSED if:
- Pre-PMF (ship fast, iterate, don’t get distracted)
- AI is supporting feature, not core product
- Small team (<50 people)
- AI costs <$50K/year
Choose HYBRID if:
- Post-PMF, scaling (economics matter now)
- AI costs >$100K/year and growing
- Have/can hire ML talent
- Some workloads are cost-sensitive, others quality-sensitive
Choose OPEN-FIRST if:
- AI IS your product
- Data sovereignty is regulatory requirement
- You’re ML-native team (ex-researchers, engineers)
- You have funding to invest 6-12 months
The Mistake I See Startups Make
Optimizing too early
I see pre-PMF startups saying “We’ll save money with open source!”
No. You’ll burn 3 months of engineering time and might run out of runway before finding PMF.
APIs are more expensive per token but cheaper in time to market.
Optimizing too late
I also see scale-ups burning $50K/month on APIs when they should have invested in open source 18 months ago.
Now they’re locked in, migration is harder, margins are compressed.
The right time to evaluate open source: When API costs hit $10-20K/month consistently.
Questions for @eng_director_luis
You mentioned:
Currently 100% on Claude/GPT-4 APIs
$180K/year and growing 40% Q/Q
Given 40% Q/Q growth, your API costs will be:
- Q4 2025: $250K/year run rate
- Q4 2026: $1M/year run rate
At $1M/year, open source is almost certainly cheaper. So your question isn’t “if”, it’s “when”.
@cto_michelle’s hybrid approach seems perfect for you. Start the transition now while costs are manageable.
Sources:
- SF Tech Week “Building AI Products” session (Day 3)
- CB Insights Foundation Model Divide report 2025
- Panel discussion: Company A and Company B case studies
- Hatchworks Open vs Closed LLMs Guide 2025 (product strategy section)