I'm curious about the practical applications of large language models (LLMs) that are actually generating revenue. If you've successfully monetized an LLM-based product or service:
What does your product/service do?
How did you develop and launch it?
Which LLM(s) are you using (e.g. GPT-4, Claude, open source models)?
What's your revenue model?
How much are you making (if you want to share, ballpark figures are fine)?
What challenges have you faced?
Any advice for others looking to enter this space?
Thanks for sharing your experiences!
lbhdc4 months ago
What does your product/service do?
Ad tech
How did you develop and launch it?
New features for an existing product. Most of them were switched on globally when we launched them.
Which LLM(s) are you using (e.g. GPT-4, Claude, open source models)?
We started using language models before ChatGPT and friends came out. We built our models from scratch, and still use and update them.
What's your revenue model?
Saas
How much are you making (if you want to share, ballpark figures are fine)?
We don't charge extra for features that use ML, it's an implementation detail that our users don't care about. So, I am not sure what % of the whole they contribute to. Its not the majority.
What challenges have you faced?
"mlops" was a real challenge in the early days, and we built so many bespoke distributed systems to figure it out. Eventually we did, but finding the right balance between building a performant system and a system that doesn't explode in cost was tough. There is off the shelf things that do this these days, but they can be expensive.
Any advice for others looking to enter this space?
Solve problems customers are willing to pay for. Some of the features we have dreamed up have been total flops. Many of the features we have developed (ml or otherwise), while the add value for the customer and add to the moat, they aren't valuable enough that our users are willing to pay extra for it as a standalone product.
kordlessagainop4 months ago
Thank you for providing a detailed response. It's helpful!
gtirloni4 months ago
> There is off the shelf things that do this these days, but they can be expensive.
Could you share some names for someone inexperienced in MLOps to do some research?
lbhdc4 months ago
Kubeflow is the one I see used the most (self hosted). The public clouds also have products targeting these workloads.
lWaterboardCats4 months ago
Would love to read an MLOps lessons learned or approach you had or if you recommend any particular books that really hit the nail on the head
lbhdc4 months ago
I don't have any books specific to MLOps, just because they weren't out when I was building that system. All of the good practices from building resilient distributed systems apply. Designing Data-Intensive Applications is always a great read.
Some things that have were notable:
Model pipelines tend to be flakier than other pipelines you have. They are much more complicated, and it can be easy to hit a resource limit if you aren't careful, or have a unhandled exception accidentally kill a pipeline 10hrs into it.
Avoiding those outright is obviously the best path, but that can be easier said than done.
One thing that we found really helpful was creating an error record in a database for every piece of data that failed to get processed, where it failed in the pipeline, etc. Retries, and alters were easy to tack on after that.
mac3n4 months ago
presumably someone is making money by spamming HN with links to substack articles that are LLM-generated ads
mergisi4 months ago
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enceladus064 months ago
Wow, this is a great looking product and website. Do you have ideas of another niche problem simular to SQL query generation that could be solved with GPT?
rene77054 months ago
JS generation. CSS too. and HTML.
i was unable to get the menu as is shown now on my https://nicer.app up and running with ChatGPT 4. part of me is glad that human programmers are still needed, but there's a lot of code that i wanna write during this lifetime. so i'd appreciate upgrades to AI.
mergisi4 months ago
I totally get that feeling! AI tools like ChatGPT can be great for basic code generation, but they still have limitations when it comes to complex or highly customized outputs, like your menu. That’s why tools fine-tuned for specific tasks, like AI2SQL for database queries, can be more reliable in those areas. As AI continues to evolve, I'm hopeful we’ll see improvements that bridge these gaps for more complex coding tasks like JS, CSS, and HTML.
mergisi4 months ago
Thanks for the feedback! Aside from SQL query generation, we're exploring ideas like automating data transformation tasks or simplifying API integrations using GPT. There’s a lot of potential for solving niche problems like these. I'd love to hear any ideas you have!
mraza0074 months ago
Hey love your site and it looks amazing.
I’m genuinely curious to ask this question.
Why would someone use your site compared to just using claude or chat gpt
mergisi4 months ago
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mraza0074 months ago
Got it thanks for answering
Gooblebrai4 months ago
How did you promote the product to reach your target audience?
mergisi4 months ago
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Gooblebrai4 months ago
Thanks for tips. Have you also used AI to format this answer?
kordlessagainop4 months ago
Your site and offering looks great. Thank you for the feedback!
mergisi4 months ago
Thank you for the kind words! I'm glad you liked the site and offering. Let me know if you have any questions or need more information—happy to help!
altdataseller4 months ago
What % of your reveneue goes towards LLM costs?
mergisi4 months ago
LLM costs are indeed a significant part of our operating expenses. While I can't share exact percentages, I can say that they constitute a meaningful portion of our revenue. We continually optimize our usage and explore cost-effective strategies to manage these expenses while maintaining the quality of our service.
meyerluanna4 months ago
I’ve been using an AI chatbot powered by GPT-4 to manage customer service tasks for my small business. The chatbot handles basic queries, FAQ responses, and routing, and it’s been a great time-saver. The service operates on a freemium model, where the base features are free, and you can pay for more advanced capabilities like analytics and integrations.
One challenge I've noticed is ensuring it works seamlessly across different business needs while staying easy to use. For those exploring LLMs, starting with a targeted niche and iterating based on real user feedback has been essential in maximizing the tool’s efficiency.
readyplayernull4 months ago
Thank you AI.
meyerluanna4 months ago
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Demonologist4 months ago
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