I'm a CS student from Shantou. I'm building a low-cost LLM API for SEA devs (70% cheaper than OpenAI)
Hi everyone,
I'm a CS student from Shantou, China. I'm excited to share what I'm working on.
The background:
Our city (Shantou) recently launched a state-approved cross-border data service – a "data bonded zone" with a direct subsea cable to Singapore (32.7ms latency). It's powered by offshore wind energy, which keeps costs super low.
What I'm building:
I'm creating an API that gives developers access to this infrastructure – essentially a low-cost, compliant LLM API for Southeast Asian developers.
My target: 70% cheaper than OpenAI, with lower latency to SEA.
Current status:
Still very early stage. I'm currently working on the technical integration and the API gateway.
Why I'm posting:
I'd love to hear from this community:
Would you use something like this?
What would make you actually switch from OpenAI or other providers?
Any red flags or pitfalls I should watch out for?
I'm not selling anything yet – just trying to validate the idea and learn from experienced builders like you.
Thank you for reading! Honest feedback (good or bad) is very welcome.
Interesting idea. But developers will only switch if you prove reliability, model quality, and real benchmarks not just cheaper pricing. Start with a small MVP, show latency,cost comparisons, and get early pilot users to build trust.
The cost barrier gets most of the attention but the workflow barrier is bigger for research use cases. Researchers (PhD students, postdocs) who start using LLMs for literature review, methodology checks, or academic writing hit a ceiling fast - not because the API is expensive, but because they don't have tested prompt structures for their specific tasks.
A literature review prompt that works for chemistry is broken for qualitative social science. A methodology critique prompt needs to know what 'threat to validity' means in your field. The researchers who get the most out of low-cost APIs are the ones who've spent time developing field-specific prompt libraries, not the ones who just switched providers.
Good timing on the price angle though - the unlock for SEA academic use is probably cost + workflow, not cost alone.
The cost angle is useful, but I wouldn’t lead only with “70% cheaper than OpenAI.” Cheap LLM APIs are easy to dismiss as unstable, risky, or temporary. The stronger wedge is probably regional infrastructure: lower-latency SEA access, compliance-aware routing, and a cost base tied to real physical infrastructure instead of just reseller pricing.
For developers to switch, they’ll need trust before price. Clear uptime numbers, model availability, data handling, API compatibility, latency benchmarks, and fallback behavior will matter more than the discount itself.
One thing I’d think about early is the brand frame. If this becomes serious LLM infrastructure for SEA developers, it needs to sound more like durable API infrastructure than a student project or location-based experiment. Exirra .com would fit that direction well because it feels sharper, technical, and more infrastructure-grade.
This is incredibly valuable feedback. You're right. 'Cheaper' alone can sound like a red flag.
The real moat is exactly what you said: purpose-built infrastructure for SEA.
Lower latency (the direct cable to SG ~32ms)
Compliant routing (the 'data bonded zone')
Cost based on physical infra, not just reselling.
In the long run, uptime, reliability, and a clear data policy are what make developers switch.
I'll work on framing the message around 'reliable, regional infrastructure that happens to be 70% cheaper' , rather than the other way around.
A quick question for you: As a developer in this space, which data point would you trust more on a landing page?
'70% cheaper than OpenAI'
'32.7ms avg latency to Singapore' + 'Powered by state-approved cross-border data infrastructure'
Thanks again for pushing me to think bigger.
I’d trust the latency/infrastructure proof more.
“70% cheaper than OpenAI” gets attention, but it also creates doubt. Developers may wonder what tradeoff they are accepting.
“32.7ms avg latency to Singapore” plus “cross-border data infrastructure” feels more credible because it says why the cost advantage exists.
So I’d frame it like:
“Regional LLM infrastructure for SEA developers, with Singapore latency and compliance-aware routing.”
Then use the 70% cheaper point as supporting proof, not the headline.
The bigger question is trust. If a developer is moving API traffic, they need to believe the infra will stay reliable, compliant, and stable.
That is also why I’d be careful with the brand early. If this becomes serious LLM infrastructure, the name has to carry trust before people even read the benchmarks.
Exirra.com still feels closer to that infrastructure-grade direction than a name that sounds local, experimental, or cost-first.
For those curious about the technical side: the API will be OpenAI-compatible, so switching over is just changing the base URL. Main challenge right now is making the gateway rock-solid for production use.
What’s the #1 feature you’d need to see before trusting a new API provider like this?
Personally, I'm betting on stability and latency being the top priority for SEA devs. The subsea cable gives us a real advantage there. But I'd love to hear if I'm wrong — does a dirt-cheap price matter more than rock-solid uptime for your side projects?
Good luck! and don't forget to make your progress live at indie hackers :) for feedback
Also Check out my tool [Pterocos] at Products DB its called Pterocos : ) upvote to support me
Thanks for the suggestion! Just checked out Pterocos — interesting tool. Good luck with it!
And yes, planning to post regular updates here as I build. Appreciate the support!