Great post, Pararth. We tried to run the PLG + SLG playbook in parallel at an earlier company and learned a painful (but useful) lesson: it splits the org’s attention before you’re ready, and nobody ends up truly happy.
Two different cadences. PLG pushes you toward friction-free onboarding, rapid iteration, and a broad roadmap driven by in-app data. SLG demands white-glove hand-holding, custom features, and long enterprise security reviews. Each cadence cannibalizes the other’s velocity.
Resource whiplash. Early teams are tiny. Every hour your engineers spend on an enterprise “one-off” is an hour they’re not fixing the self-serve funnel. Sales wants roadmap guarantees; growth wants experiments. Impossible trade-offs pile up fast.
Diluted product signal. When you’re simultaneously chasing 5-figure ARR pilots and $79/mo swipe-ups, the definition of “who we’re building for” gets fuzzy. We ended up with a product that was almost good for two segments and perfect for none.
Our eventual fix was to nail PLG first—drive usage to the point we had undeniable pull from larger customers—then layer on a dedicated enterprise motion once we could fund a solutions team without starving the core roadmap. Focus is an under-appreciated moat.
Totally agree SLG can surface deep insights, but in my experience those insights are even more valuable when you’ve already established a strong, opinionated product foundation via PLG. Just one data point from the trenches, but thought I’d share!
Appreciate the note, Michael! 100% agree with “focus is a moat”. The question in my mind is how does GenAI change the equation — both from reduced team sizes needed to ship products, and from the crowding in every enterprise category requiring deeper insights to build the winning product.
Totally. Frank Slootman puts it best: when you can “whittle your priorities down to one thing, you become unstoppable.” Easier said than done, but ruthless focus is still the cheat code—especially when Gen AI is lowering build costs and raising the noise level. Link for anyone who hasn’t seen the clip: https://www.youtube.com/watch?v=2CrA8aeeuPw&t=1s
Great post, Pararth. We tried to run the PLG + SLG playbook in parallel at an earlier company and learned a painful (but useful) lesson: it splits the org’s attention before you’re ready, and nobody ends up truly happy.
Two different cadences. PLG pushes you toward friction-free onboarding, rapid iteration, and a broad roadmap driven by in-app data. SLG demands white-glove hand-holding, custom features, and long enterprise security reviews. Each cadence cannibalizes the other’s velocity.
Resource whiplash. Early teams are tiny. Every hour your engineers spend on an enterprise “one-off” is an hour they’re not fixing the self-serve funnel. Sales wants roadmap guarantees; growth wants experiments. Impossible trade-offs pile up fast.
Diluted product signal. When you’re simultaneously chasing 5-figure ARR pilots and $79/mo swipe-ups, the definition of “who we’re building for” gets fuzzy. We ended up with a product that was almost good for two segments and perfect for none.
Our eventual fix was to nail PLG first—drive usage to the point we had undeniable pull from larger customers—then layer on a dedicated enterprise motion once we could fund a solutions team without starving the core roadmap. Focus is an under-appreciated moat.
Totally agree SLG can surface deep insights, but in my experience those insights are even more valuable when you’ve already established a strong, opinionated product foundation via PLG. Just one data point from the trenches, but thought I’d share!
Appreciate the note, Michael! 100% agree with “focus is a moat”. The question in my mind is how does GenAI change the equation — both from reduced team sizes needed to ship products, and from the crowding in every enterprise category requiring deeper insights to build the winning product.
Totally. Frank Slootman puts it best: when you can “whittle your priorities down to one thing, you become unstoppable.” Easier said than done, but ruthless focus is still the cheat code—especially when Gen AI is lowering build costs and raising the noise level. Link for anyone who hasn’t seen the clip: https://www.youtube.com/watch?v=2CrA8aeeuPw&t=1s