Pre-Seed AI Stack — Under $200/mo
~$130–180/mo totalPre-seed is about learning fast and building proof. Your AI stack should accelerate research, coding, and customer discovery — not add operational overhead. Every tool here has a free tier or is under $25/user/month.
| Tool | Category | Price (2-person team) | Priority | Source |
|---|---|---|---|---|
| Cursor | AI coding | $40/mo (2 × $20) | Essential | cursor.com/pricing |
| Claude API (Haiku/Sonnet) | Product AI | ~$20-50/mo (usage-based) | Essential | platform.claude.com |
| Notion AI | Docs + knowledge | $20/mo (2 × $10) | Essential | notion.com/pricing |
| Linear | Project tracking | Free (up to 250 issues) | Essential | linear.app/pricing |
| Perplexity Pro | Research | $40/mo (2 × $20) | Optional | perplexity.ai/pro |
| Otter.ai | Meeting transcription | Free (300 min/mo) | Optional | otter.ai/pricing |
All prices verified from vendor pricing pages, May 9, 2026. Usage-based API costs estimated at moderate volume (10K-50K requests/month).
At pre-seed, don't build custom AI infrastructure. Use API calls. The biggest mistake is spending engineering time on AI plumbing when you should be finding product-market fit. Cursor for coding, Claude API for your product, Notion AI for thinking — that's enough stack for a $1M ARR company.
Seed Stage AI Stack — $1,000–5,000/mo
~$1,200–4,500/moAt seed, you have a working product and early customers. The AI stack expands to support a growing team, customer success, and more sophisticated product AI features like semantic search, personalization, or AI assistants.
| Tool | Category | Monthly Cost (10-person team) | Priority | Source |
|---|---|---|---|---|
| Cursor for Teams | AI coding (dev team) | $160/mo (8 × $20) | Essential | cursor.com/pricing |
| OpenAI or Anthropic API | Product AI | $300–2,000/mo (usage-based) | Essential | openai.com/api/pricing |
| Pinecone (Starter) | Vector database | $70/mo | Essential | pinecone.io/pricing |
| Linear (Standard) | Project tracking | $80/mo (10 × $8) | Essential | linear.app/pricing |
| Notion AI (Team) | Knowledge management | $100/mo (10 × $10) | Essential | notion.com/pricing |
| Intercom (AI features) | Customer support AI | $74/mo (Starter) | Optional | intercom.com/pricing |
| GitHub Copilot Business | AI coding (non-Cursor users) | $190/mo (10 × $19) | Optional | github.com/features/copilot/plans |
| Datadog (LLM monitoring) | AI observability | ~$150/mo (LLM observability add-on) | Optional | datadoghq.com/pricing |
Pricing as published on vendor pages, May 2026. API costs highly variable by product usage patterns.
Series A AI Stack — $5,000–50,000+/mo
~$5K–50K+/moAt Series A, AI is a core infrastructure concern. You're optimizing for reliability, cost efficiency at scale, compliance, and multi-model resilience. The decisions made here determine your AI cost structure for the next 3 years.
| Category | Recommended Solution | Est. Monthly Cost | Why at Series A |
|---|---|---|---|
| Foundation model API | Multi-provider (OpenAI + Anthropic + Gemini) | $2,000–20,000/mo | Resilience; optimize model per task type |
| Vector database | Pinecone Standard or Weaviate Cloud | $350–1,000/mo | Production-grade; SLAs; backups |
| AI gateway / proxy | LangSmith, PortKey, or Braintrust | $200–1,000/mo | Observability, rate limits, fallbacks |
| AI coding (full team) | Cursor Teams + GitHub Copilot Enterprise | $500–2,000/mo | Productivity across engineering team |
| Customer AI (support) | Intercom AI Agent or Zendesk AI | $500–3,000/mo | Deflect tier-1 support at scale |
| AI evaluation / testing | Braintrust or Ragas | $100–500/mo | Regression testing for model outputs |
| Security / compliance | Nightfall AI or Private AI | $200–2,000/mo | PII detection before data hits LLMs |
The most common Series A AI cost mistake: sending all LLM calls to the most expensive model. Implement a routing layer — classify queries by complexity and route 70-80% of traffic to Gemini Flash or Claude Haiku. Reserve flagship models for high-stakes decisions. This typically cuts AI infrastructure spend 50-70% with no user-visible quality regression on routine tasks.
Integration Notes
A few hard-won observations on building AI stacks that actually work:
Start with the API, not the platform. Before buying any "AI platform," implement the core AI functionality directly via API. You'll understand the cost structure, latency profile, and failure modes before paying platform premiums.
Build your prompt library before your RAG pipeline. Most startups jump to vector databases before they've exhausted what well-crafted prompts can do. A good prompt often eliminates the need for retrieval entirely. Evaluate after 30 days of iteration, not on day one.
Add AI observability before you scale. Once you're processing 100K+ AI calls/month, you need visibility into per-call cost, latency distributions, and failure rates. Tools like LangSmith or Datadog LLM monitoring are cheap insurance against silent cost explosions. Verify current pricing at smith.langchain.com/pricing (accessed May 2026).