We’re exploring a pretty specific product shape:
A lower-cost, OpenAI-compatible API layer for indie developers and early AI products.
The idea is not “more features.”
The idea is:
lower cost
minimal migration
familiar workflow
In other words: keep the OpenAI-style integration pattern, reduce friction, and improve economics for smaller teams.
The biggest open question isn’t whether cost matters.
It clearly does.
The bigger question is trust.
If you were building:
a side project
an MVP
a content-heavy AI app
a low-sensitivity workflow
Would you use a third-party OpenAI-compatible layer to reduce API cost?
If not, what would stop you first?
My guess is the top concerns are something like:
reliability
latency
data handling
long-term stability
“why not just stay direct?”
Would love honest answers, especially from people who’ve actually shipped AI products.
I would trust a lower-cost OpenAI-compatible layer for the right kind of workload, but only after checking the boring trust pieces first:
For a side project or MVP, that is usually enough to test. For anything sensitive, I would start with low-risk workflows and compare latency, error rate, and billing logs against direct provider calls.
I keep a broader checklist here: https://github.com/alicekellings/awesome-ai-api-proxy
Disclosure: I maintain Wappkit, an OpenAI-compatible gateway, so I am biased toward quotas and request logs. The trust question is real though; lower price alone is not enough.
This is exactly what I've been working on to solve this exact dilemma for indie hackers!
To bridge the trust gap, I made my implementation fully transparent and premium-routed, handling OpenAI, Claude, and DeepSeek via a single custom endpoint.
Since it's in beta and trust needs to be earned, I'm opening it up for testing with free credits. Anyone interested can check the info in my profile / bio. Would love to get your developer feedback! 🚀