SaaS ideas your next customers are already looking for
100 SaaS startup ideas for the people who said they'd pay, from agency owners to dentists. Micro SaaS ideas, AI SaaS ideas and B2B SaaS ideas: find yours, build it, sign them up.
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Pain points in business: what people struggle with
Search the problems to solve, as people described them on Reddit and other platforms, in their own words.
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Costly GPU Inference Hosting
Users struggle to deploy, scale, and manage local or self-hosted GPU inference reliably and affordably.
In their words
it feels important to know, especially for agentic work, planning, coding, and long-context tasks where subtle degradation matters.
checkpoints and LoRAs have to sit next to the GPU or the boot is even slower, and that storage gets billed every month whether you generate or not
We need it to run large open-source models like GLM and others... No rate limits or cutting off... 24/7 uptime.
There just wasn't enough VRAM available on the single 5090 for that.
le passage en prod c'est l'enfer absolu sur toute la partie industrialisation et architecture cloud
I'm in this for serious works for teams shipping real apps to paying customers, and I don't see a real solution today.
It tends to borrow characteristics (especially lips for some reason) so characters drift slightly towards each other
the software stack is a mess. There is no framework that has all the inference optimizations that are available on CUDA/NVIDIA
every few months someone drops a new organ or tissue type and retraining the whole embedding from scratch gets absurd fast
it's going to be atleast 10-15 different kinds of applications ... replicated at several locations maybe 30-40-50 locations.
Weights sit decrypted in shared memory, and root can dump them.
I had to categorise 12,000 emails so just ran it on the local model and Claude supervised and spot checked
my data is too sensitive to be uploaded to a cloud of which I have no gaurantees of privacy/security
I deploy browser extensions to millions of devices, and small local models that I can run in that space are incredibly appealing. But the "per user" model is just especially difficult to justify here
ML engineers and AI developers
Where they talk
- r/LocalLLaMA
- r/mlops
- r/LocalLLM
- r/comfyui
- r/LLMDevs
- r/StableDiffusion
- r/computervision
- r/unsloth
Tools they use today
- vLLM
- Docker
- ComfyUI
- llama.cpp
- RunPod
- Qwen
- Ollama
- Claude
Why those fall short
- Character traits bleed; caps at 4 characters; fails on some base models
- No way to keep or quickly restore large models without huge storage and slow downloads
- pieces scattered across frameworks; each implements only part of the stack
- pathologies only appear around step 20M, too late for hyperparameter search
- retraining from scratch is computationally expensive and impractical
Map of ideas
Drawing the map…