SAASPOCALYPSEverdict #CHATGPT-9B27
scanned 2026.04.28 · 14:50
subject of investigation
chatgpt.com
▸ LLM-powered conversational AI chatbot
verdict: DON'T
buildability score
4
/100
tier · don't
the blunt take
“You're not building ChatGPT. You're thinking about building ChatGPT. Those are very different things, and only one of them is funny.”
The chat UI is a weekend. The model is a decade and ~$100B in compute. OpenAI didn't ship a textarea — they shipped a paradigm shift backed by a supercomputer cluster. The wrapper is trivial; the thing inside the wrapper is the entire product.
cost breakdown.
their price ←→ your price
what they charge●
ChatGPT Plus
$20
/ user/mo
※ Free tier exists; Plus is $20/mo; Team/Enterprise tiers higher
annual:$240
what it costs you✦
01 · Pre-training compute (GPU cluster, years of runs)your tears
02 · RLHF + fine-tuning infrapriceless
03 · Safety & alignment research teampriceless
04 · OpenAI API (if you just wrap it instead)??? — scales with usage
05 · Vercel (hobby tier, for the chat UI wrapper)$0.00
06 · Supabase free (chat history)$0.00
07 · Domain$1.00
TOTAL / mo$1.00 + usage
▸ break-even:never — the free tier alone would cost you more to replicate than the GDP of a small nation
or, you know, use one of these.
if building feels spicy
option A
Wrap the OpenAI API
You can ship a ChatGPT-shaped UI in a weekend using the API. That's not building ChatGPT, but it's the closest sane path.
option B
Ollama + open-weight models (Llama 3, Mistral)
Run LLMs locally or on a cheap VPS. No API costs, no OpenAI dependency, genuinely self-hostable.
option C
LibreChat (self-host)
Open-source ChatGPT UI that supports multiple backends. Already exists. Docker compose up and go touch grass.
what'll actually be hard.
est. total: ∞
▸ Sam started in 2022 · Still training · Your GPU budget is not sufficient · Please go home
easy
medium
hard
nightmare
01
easy
Chat UI (the textarea + message bubbles)
Genuinely a weekend. Streaming responses via SSE. You've got this.
02
medium
Chat history + user auth
Supabase + a sessions table. Half a day, tops.
03
hard
Reliable, low-latency inference at scale
Even with open-weight models, serving thousands of concurrent users without melting your GPU is a real infra problem.
04
nightmare
Training a frontier model from scratch
Requires petaflop-scale compute, terabytes of curated data, and a team of ML PhDs. This is not a sprint task.
05
nightmare
RLHF / alignment
Reinforcement learning from human feedback requires massive annotation pipelines and is an active research area. Not a library you npm install.
06
nightmare
Safety, moderation, and not destroying society
OpenAI has a whole team for this. You have a weekend. These are not equivalent.
recommended stack · inferred
regulatory attorneys ($800/hr)~30,000 A100 GPUs (just to start)petabytes of training data + cleaning pipelinesyour remaining optimisma very understanding family
ready to build?
We'll email you the MVP guide. It won't be the original. But it'll ship.
▸ generated with love, by a heartless robotverdict v2.1 · saaspocalypse.dev