You don’t know how often we use LLM calls in our workflow automation, what models we are using, what our margins are or what a high cost is to my organization.
That aside, business processes solve for problems like this, and the business does a cost benefit analysis.
We monitor costs via LiteLLM, Langfuse and have budgets on our providers.
Also, your last note is hilarious to me. “I don’t want all the free stuff because the company might charge me more for it in the future.”
Our design is decoupled, we do comparisons across models, and the costs are currently laughable anyway. The most expensive process is data loading, but good data lifecycles help with containing costs.
Ok, so you’re completely delusional.
The current business model is unsustainable. For LLMs to be profitable, they will have to become many times more expensive.
What are you even trying to say? You have no idea what these products are, but you think they are going to fail?
Our company does market research and test pilots with customers, we aren’t just devs operating in a bubble pushing AI.
We are listening and responding to customer needs and investing in areas that drive revenue using this technology sparingly.
I don’t know what your products are. I’m speaking specifically about LLMs and LLMs only.
Seriously research the cost of LLM services and how companies like Anthropic and OpenAI are burning VC cash at an insane clip.
That’s a straw man.
You don’t know how often we use LLM calls in our workflow automation, what models we are using, what our margins are or what a high cost is to my organization.
That aside, business processes solve for problems like this, and the business does a cost benefit analysis.
We monitor costs via LiteLLM, Langfuse and have budgets on our providers.
Similar architecture to the Open Source LLMOps Stack https://oss-llmops-stack.com/
Also, your last note is hilarious to me. “I don’t want all the free stuff because the company might charge me more for it in the future.”
Our design is decoupled, we do comparisons across models, and the costs are currently laughable anyway. The most expensive process is data loading, but good data lifecycles help with containing costs.
Inference is cheap and LiteLLM supports caching.
Also for many tasks you can run local models.