API Bartender for LLM APIs.

We are building an API that combines different LLMs for quality and cost optimization. Our machine-learning model sorts tasks based on complexity and specialization, directing them to the best-performing LLM in those categories. This approach allows companies to save costs, as cheaper models can perform equally well in certain areas. Currently, we are 35% cheaper than GPT-4 without sacrificing quality. Startups and SMEs use our service to save money and get better results.

Chicago, US / San Francisco, US

We met through a mutual friend in college and have been best friends since then. We have been living and working together.

We launched our public beta yesterday (10/12). We already have paying customers as well as free trial users. We’re working on integrating Claude, PaLM, and some fine-tuned models.

---- Update 10/27 ----
Over the past two weeks we have been focusing on four things:
1) adding user requested features, such as organization management, usage tracking, and LangChain integration.
2) addressing user concerns, such as GDPR compliance, terms of use, and privacy policy.
3) sales & user engagement - we booked 23 demos up to today and helped 3 companies with custom integration.
4) QA & testing.

We started our company in January and have been working full-time. We pivoted to the current product last month after two previous YC submissions and two failed ideas.

We have 17 users running our free trial API key since yesterday's launch (10/12). We also have seven B2B customers who are paying and working on integrating our API. Although we don't yet know precisely how much they will pay, we expect ~$500-1500 /user per month based on their current AI usage costs.
The three customers we expect the most revenue from are Customer 1 (B2B knowledge base chatbot, 100+ customers), Customer 2 (SQL copilot, 10k+ users), and Customer 3 (AI for codebase, $10k+ monthly AI spending).

---- Update 10/27 ----
Current revenue = $1968 + ~$100 unbilled usage + (+$399 in transit, payment ran into issues with Stripe). Customer 4 is paying us close to $1800.

We plan to update our pricing model from subscription-based to solely pay-by-usage, as many users have indicated a preference for this model.

---- Update 10/27 ----
We have updated to usage-based pricing.

We pivoted from an AI job search copilot and learned three key lessons: 1) B2C faces high churn in recruiting. 2) User feedback is vital; building the wrong thing is costly — "make something people want." 3) Quick launches are crucial for finding product-market fit; now we ship in weeks, not months. We believe our current idea is more scalable and can grow very quickly.

We chose this idea after encountering limitations with GPT-3.5 and high costs with GPT-4 in our previous project. The concept actually began as a cost-optimization solution for our own use before we realized its broader application.

With conversations with over a hundred founders in the generative AI space, we have a strong grasp of the domain and customer needs.

We knew people wanted our product when we had more than 20 demos booked on the day we launched. We received many “please consider us for a demo” and “this is such a great product” messages. One customer we talked to today was especially thrilled that we could save them $3,500 monthly on their calls without sacrificing quality.

Our competitors are companies that offer single or fine-tuned LLMs. We will incorporate these individual LLMs into our product, as well as fine-tune some LLMs for specialized use-cases.

We outperform our competitors in both cost and output quality. As the cost, speed, and quality of individual LLMs like GPT-4 improve, so does our product, ensuring we always maintain a competitive edge. There are no companies working on a combined LLM as of now, and we firmly believe this is the future for LLM usage.

We'll make money with a pay-by-usage API model. Right now our profits are more than half of our revenue. We'll initially target startups and SMBs using generative AI.

With a huge market size of ~$200 billion for the AI market, our current serviceable available market is $2.7 billion.

Andy, CEO - 51%
Raymond, CTO - 49%
We also plan to create a 20% employee pool for early hires.

We both write code. Andy does design and frontend; Raymond does full-stack with a focus on ML.

We currently do not have other ideas; however, as a startup that has pivoted twice, we're adaptive and understand the importance of making decisive changes for higher potential projects.

We firmly believe YC can boost our outcome by at least 7%. We plan to use the 3 months at YC to continue focusing on building our product and talking to users — every generative AI YC startup as a potential customer.

Andy worked at Yummy Future (YC S19) in 2021 and learned a lot of good things about YC.