How would you approach GTM? Initial ICPs are AI startups that have already reached PMF (or are close) [I will not promote]
We’re trying to figure out the right GTM for a dev tool, and I’d love to hear from founders who’ve sold infrastructure or built AI products at scale. The idea came from noticing that many AI startups solve the same problem twice. They first build a working…
We’re trying to figure out the right GTM for a dev tool, and I’d love to hear from founders who’ve sold infrastructure or built AI products at scale. The idea came from noticing that many AI startups solve the same problem twice. They first build a working product with prompt chains and frontier models. Then, once customer usage starts growing, engineering shifts toward making those same workflows cheaper, faster and more reliable using caching, routing, regexes, parsers, classic ML, deterministic pipelines, smaller models, and lots of bespoke engineering. Our platform analyzes production traces and identifies repeated LLM workflows that could instead become deterministic, testable software. The goal isn’t to replace every LLM call. It’s to identify the 20% of workflows that account for 80% of production traffic and compile those into cheaper, more reliable implementations while leaving the genuinely open-ended reasoning to frontier models. Our current hypothesis is that the ICP isn’t “any AI startup.” It’s companies that are: post-MVP with real production traffic rapidly acquiring customers accumulating prompt technical debt starting to care about inference spend, latency and reliability instead of just shipping features Our planned GTM is founder-led. Offer a free production trace audit, replay historical traffic, quantify the potential savings and reliability improvements, then deliver a productionization sprint if the ROI is compelling. The team has technical and B2B sales experience, but we are also completely new to dev tools. We’re still early, and I’d rather find out our assumptions are wrong now than six months from now. Wanted to pick everybody's brains on how to better approach this GTM. Thanks in advance!
已收录讨论
I'd start with the boring wedge: infra spend, not “AI startup”. Find teams with usage-based model bills painful enough that a trace audit has an obvious dollar value. If the audit can say “this workflow costs $X/mo and we can make it $Y”, that’s much easier to buy than “we reduce prompt technical debt.” Also, don’t make the free audit too broad. Pick one workflow, one metric, one before/after. Otherwise you’ve invented consulting with nicer fonts.
> Otherwise you’ve invented consulting with nicer fonts. Lol....love the way you phrased it.
It's the gravity well of B2B. If the product doesn't eat the messy bit, the messy bit becomes a person with a nicer deck.
This is a good point. My experience as a techie across companies and big and small for the past 12+ years tells me that few people ever finds time for non-critical tech debt. The "fixes" are also not easy (i.e involves infra and data annotations, deployment), which is why so many so startups/teams procrastinate. I don't really have a super precise persona yet, but I'm thinking a Director of Engineering or CTO would be the right champion?
On second thought, I agree. I think it's probably like some team that is spending >100k/annum on LLM or something. Not sure how'd identify them though.
Non- critical equates to not with the cost to fix. So if you can quantify and make it easier to fix it may help - but yead director/cto is likely the buyer ICP.
the icp that matters probably isn't "ai startup with pmf," it's whoever already has a slack thread where someone screenshots the openai bill every month. that's the pain your free audit needs to find, not the funding stage.
Great response. The only tactical suggestion I would add is to find one or two teams to start with and make them beta users. Create proof that it works and use their improvements as marketing. Also helps to ensure the platform works/integrates as expected.
Since PMF or near-PMF startups are focused on scaling, how you position your service as scaling via cost reductions and efficiency is important - not as infra around tech debt (also as below, you may have a entry point for more established companies with usage-based costs) A concern with such a detailed review is that they get your output, but decide to fix in-house once you've shown them the focal points - You might show low hanging fruite and hide broader reports behind a summary and potential $ savings to entice them to either pay fo the audit and fix themselves, or hire you to get ti all done.
Start by qualifying trace access. A free audit can attract teams that cannot let an outside vendor touch production data. Ask for a redacted sample first and identify who owns security approval. Confirm that the project has budget this quarter before spending engineering time on it. Sell the diagnostic as a paid benchmark credited toward implementation. That filters curiosity from urgency and reduces the chance of unpaid consulting.