Side-by-Side Comparison
AI Deployment Models Compared
| Factor | Build (Custom/Self-Hosted) | Buy (SaaS/API) | Hybrid |
|---|---|---|---|
| Upfront cost | $150K–$1M+ | $0–$50K | $50K–$300K |
| Monthly ongoing | $5K–$30K | $2K–$50K (usage-based) | $3K–$25K |
| Time to production | 6–18 months | 2–8 weeks | 2–6 months |
| Customization | Full control | Limited to vendor config | High |
| Data privacy | Stays on-prem | Leaves your network | Core data stays local |
| API breakeven vs SaaS | Build breaks even at ~50K queries/day (~18–24 months) | ||
| Success rate | ~33% reach production | ~55–65% reach production | ~45–55% |
| Vendor lock-in | Low | High | Medium |
Decision Framework
The Real Build vs Buy Framework
Insiders don't ask "build or buy?" — they ask four questions:
Is this a commodity capability?
Translation, summarization, basic classification — problems already solved by existing services.
→ Buy. Don't re-solve it.
Is this your competitive advantage?
Proprietary process optimization, unique domain expertise, trade secrets in the workflow.
→ Build. Or fine-tune a model you own.
Will it need to change quarterly?
Fast-moving business logic, frequent retraining needs, evolving requirements.
→ Build. Vendor cycles won't match yours.
Does it touch regulated data?
HIPAA, SOX, ITAR, customer PII, financial records, EU AI Act "high-risk."
→ Build/self-host. Compliance cost of explaining third-party AI to auditors often exceeds build cost.
Enterprise AI Platforms
Platform Cost Transparency Scores
We evaluate AI platforms on how honestly they communicate total cost of ownership — not just features.
| Platform Type | Annual Cost | Transparency | What They Don't Tell You |
|---|---|---|---|
| Enterprise AI Platforms (Dataiku, DataRobot, C3.ai) |
$100K–$500K+/yr | C | For most SMBs, Python + HuggingFace + MLflow does 90% of this. You need the platform at 10+ models & 3+ data scientists. |
| Cloud AI Services (AWS SageMaker, Azure ML, GCP Vertex) |
$2K–$50K/mo | B | Usage-based pricing sounds good until you scale. Model the cost curve at 10×, 50×, 100× current volume before committing. |
| API-First LLM Providers (OpenAI, Anthropic, Google) |
$100–$10K/mo | A | Clear per-token pricing. But: volume commitments (1M+ tokens/day) get 40–60% discounts — they won't offer this, you have to ask. |
| Open-Source Stack (HuggingFace, MLflow, LangChain, pgvector) |
$0 (+ compute) | A | Free ≠ zero cost. You need engineers who know the stack. But it's the best insurance against lock-in. |
Negotiation leverage: Enterprise AI consulting firms operate at 40–65% gross margins. Having a working open-source prototype (Llama/Mistral) gives you negotiating power and an exit strategy. Vendors price differently when they know you can leave.
Insider Playbook
What Experienced Teams Actually Do
- Start with the workflow, not the model. Map the business process first. Identify the specific decision point AI will improve. Calculate the dollar value. If the math doesn't work at 50% accuracy improvement, the project doesn't start.
- Run a data audit before anything else. 2–4 weeks, $10K–$30K. 40–60% of AI projects fail at the data stage. This audit saves $200K+ in failed projects.
- Negotiate inference pricing before signing. API pricing is the most negotiable line item in enterprise software. Volume discounts of 40–60% are routine — but only if you ask.
- Keep a shadow model running. A simpler fallback model (rules-based or smaller LLM) alongside the primary one. Cost: 10–15% of primary. Insurance value: priceless.
- Budget 20% for evaluation infrastructure. Automated eval pipelines, A/B testing, human review workflows. This separates the 33% that succeed from the 67% that don't.
- Use open-weight models as leverage. Even if you go proprietary, a working Llama/Mistral prototype gives you an exit strategy and negotiating power.
Common Traps
Expensive Mistakes to Avoid
- Single-vendor lock-in. If your entire AI pipeline runs through one vendor's proprietary format, you're trapped. Use open formats: ONNX for models, standard vector DBs, portable prompt templates.
- Ignoring inference cost scaling. Your pilot: 100 queries/day, $3/day. At scale: 50K queries/day, $45K/month. API pricing is non-linear.
- Paying for enterprise platforms too early. $100K–$500K+/year for Dataiku/DataRobot/C3.ai when you have <3 models and <3 data scientists is pure overhead.
- No human-in-the-loop plan. AI that runs unsupervised will eventually embarrass your company. The cost of a $60K/year reviewer is nothing compared to one bad automated decision.
- Confusing AI with automation. If your problem can be solved with a rules engine or simple script — do that. Cheaper, faster, more reliable, easier to debug.