Comparison · Strategy
Best LLM for startups (cost-aware)
Startups need speed, but blind flagship usage burns runway. The best LLM strategy is usually a routed portfolio: mini/flash for breadth, premium for moments that move revenue.
Portfolio thinking
Buy capability, not brand. Map features to minimum viable model tiers and promote only when metrics justify it.
Starter tier pricing snapshot
| Model | Provider | Input | Output |
|---|---|---|---|
| GPT-4o mini | OpenAI | $0.0002 / 1K in | $0.0006 / 1K out |
| Gemini 2.5 Flash | $0.0001 / 1K in | $0.0003 / 1K out | |
| DeepSeek Chat | DeepSeek | $0.0001 / 1K in | $0.0003 / 1K out |
| Claude 3.5 Haiku | Anthropic | $0.0008 / 1K in | $0.0040 / 1K out |
Evaluation discipline
Instrument token histograms from week one. Cheap analytics debt becomes expensive surprises at scale.
When to splurge on flagship models
Revenue-critical demos, enterprise pilots, and safety-sensitive flows deserve premium headroom—budget them explicitly.
Negotiation and credits
Cloud marketplaces and startup programs change effective rates—record discounts as comments in config for your team.
Decision checklist
| Stage | Model strategy |
|---|---|
| Pre-PMF | Mini/flash defaults, heavy logging, manual review |
| Early revenue | Introduce routing + budgets per customer segment |
| Scale | Dedicated capacity, caching, eval automation |
FAQ: Startup LLM choices
Short answers mirror the structured data on this page for search engines and readers.
- Should startups self-host?
- Only if you have rare compliance needs or huge steady volume—otherwise managed APIs win time-to-market.
- How much buffer should we add?
- Twenty percent for early products is common; tighten as histograms stabilize.
- What metric matters most?
- Cost per successful task, not cost per token.
- Single vendor or multi-vendor?
- Start single to move fast; add redundancy before enterprise renewals force risk reviews.