Ask HN: Are you making money from LLMs? How? | Hacker News Reader

Ask HN: Are you making money from LLMs? How?

14 pointskordlessagain21 comments

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?

I'm particularly interested in hearing about niche or unexpected applications, not just the obvious chatbots and writing assistants. Also curious about solo founders or small teams, not just well-funded startups.

Thanks for sharing your experiences!

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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.

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

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.

> 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?

https://www.kubeflow.org/

Kubeflow is the one I see used the most (self hosted). The public clouds also have products targeting these workloads.

Thank you for providing a detailed response. It's helpful!

presumably someone is making money by spamming HN with links to substack articles that are LLM-generated ads

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.

Thank you AI.

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