AI Stack for Pre-Seed Founders

What to pay for now, what to delay. Based on the actual stacks of ~100 pre-seed founders in 2026.

Pay for now (~$150-250/month total)

Frontier reasoning model with long context — $20-200/mo

The spine of your AI stack. One model you trust enough to outsource thinking to: research drafts, contract review, customer-email rewrites, code review, strategy stress-tests. Pick the model whose voice you can stand reading every day, not the one with the highest benchmark score.

What to use it for daily:

First-draft anything (emails, specs, retros, investor updates).
Stress-test plans before you commit (have it argue against your decision).
Summarise long inbound (customer emails, board decks, contracts).
Pair-write strategy — the model is a tireless second brain.

What NOT to use it for:

Final decisions on hiring, fundraising, or anything irreversible.
Source-of-truth lookups on numbers (it will hallucinate).

Code assistant — $20-40/mo per engineer

Tab-completion is table stakes. The real productivity gain is from agentic editing — the assistant making multi-file changes you review. Expect 2-3x output on routine work, 0x improvement on hard problems.

The right choice depends on your editor habit. If your team is in VS Code, use a VS Code-native tool. If you're terminal-first, use a terminal-first tool. Tool choice matters less than committing to one and learning its shortcuts.

Transcription + meeting notes — $15-30/mo

Customer-call notes are the highest-leverage written artifact in a pre-seed company. A transcription tool that auto-summarises, extracts action items, and lets you search across calls is worth it the moment you do 3+ customer calls a week.

One vertical tool for your highest-leverage workflow — $0-50/mo

Pick the one workflow you do 10+ times a week. Get the best vertical AI tool for that. Examples:

Content / SEO: a writing tool that knows your brand voice.
Sales: a prospecting tool that pulls signal from intent data.
Support: an AI inbox that drafts replies from your knowledge base.

Don't subscribe to verticals for workflows you do twice a month.

Wait until seed

Multi-tenant inference platforms

Until you have ≥1k paying users and a fine-tuning need that's blocked by the model provider's APIs, dedicated inference is overkill. The latency / cost economics rarely beat off-the-shelf for low-volume early use.

Custom model hosting

You don't need a fine-tuned model for product features yet. The frontier base models are good enough for 95% of use cases. The 5% where you genuinely need fine-tuning is rarely the first 12 months.

Voice agent infrastructure

The cost-per-minute economics still don't work for most pre-seed companies. Either you're a voice-AI startup (in which case you're the platform, not the customer), or you can wait 12 months for the price to halve again.

Vector databases

Use the model provider's built-in retrieval (OpenAI file search, Anthropic Files API) until you hit the 5,000-document or 100MB ceiling. A managed vector DB is 4 hours of setup and an extra service to monitor — you don't need that complexity at pre-seed.

Workflow / agent orchestration platforms

For most workflows, a 50-line script and a cron job beats an orchestration platform. Add an agent framework when you have 5+ workflows that share infrastructure, not before.

How to evaluate any AI subscription

Three filters, in order:

Time saved per week × your hourly cost > monthly subscription × 2. The 2x is your hurdle rate; AI tools have switching costs that aren't on the receipt.
Does the workflow already exist? If you're not already doing the task manually, an AI tool to do it faster is a fake productivity gain — you're just spending faster on the thing you weren't going to do.
Can you cancel after 60 days without losing data or workflow? If not, the lock-in cost is hidden. Avoid until you're certain.

If a tool doesn't pass all three, cancel after the trial.

Three traps to avoid

Trap 1: AI sprawl

Stacking subscriptions creates context in 7 places, expertise in none. Pick one primary model. Make it the spine. Everything else is supplementary.

Trap 2: Treating AI output as final draft

Reviewing AI output well takes 50-80% of the time it would take to do the work yourself. The actual productivity gain is in the first-draft step. If you can't critically review the domain (legal, medical, financial), don't ship AI-generated work in that domain. Hire a human for the review.

Trap 3: Buying the hype tool of the month

Every two weeks a new AI tool trends. Most don't survive 6 months. Wait 90 days before adopting anything that became famous in a launch tweet. The signal will get clearer.

What the stack looks like by stage

Solo founder, pre-MVP: 1 frontier model + 1 code assistant. ~$60/mo.

2-3 person team, MVP shipped: + transcription, + one vertical tool. ~$150-200/mo.

3-5 person team, first 10 customers: + a second model for diversity (different strengths), + a low-end paid tier on prospecting or support. ~$300-450/mo.

5+ people, post-seed: workflow platform makes sense, custom hosting if usage justifies. ~$1k+/mo, but each line has a job.

What to cancel today

Open your subscriptions. For each AI tool:

When did you last use it? If >14 days, cancel.
Did the last use save you 1+ hour vs the alternative? If no, cancel.
Could you achieve 80% of the value with your spine model + a custom prompt? If yes, cancel.

Most pre-seed founders cut 30-40% of their AI spend with this exercise. The remaining budget goes further when there's less to manage.