This pricing issue makes it easy for Segment's fast-growing customers to justify an in house replacement. This puts huge down-pressure on their "revenue retention" rate and makes it hard to raise more VC or go public.
[0] - Segment technically prices on user volume but in reality it's a mix of users + objects depending on what your sources are
- Scaled deployments have Segment connecting many systems run by many teams, so the cost of coordinating a replacement can go up just as fast as Segment's.
- High growth companies have fires everywhere, including other infrastructure, so it's not obvious that Segment makes it to the top of the list.
- I expect the bulk of Segment revenue is not from companies with "high growth" by HN standards but scaled enterprises with high complexity and lower growth. These can dominate net rev retention even if there are smaller companies growing faster.
Again, no data to support the above, just a different narrative. Anyone have facts?
Are you eventually running your own cluster? Or, are you paying per Gb per second how GCP bills with decoupled compute / memory / storage? Or maybe with a little bit more overhead with the orchestration cost on top of consumption with how azure bills for ADF.
Just from reading blog posts, Spotify’s move was interesting, regarding their annual “songs you liked” at the holidays, where they sort of ran a hybrid.
Sorry to digress but what is the end state. Is there just a number where it makes sense to hire people and build and/or shift vendors? And if yes, what vendor is “after” stich? Astronomer maybe. Sorry if that’s basic.
SaaS companies pricing is often geared towards startups that "can't afford" to control costs, i.e. developer time spent on minimizing cost has a higher monetary and opportunity cost than ignoring those costs in the short term, even if they're piling up at $50/mo for every auxiliary service.
But that strategy does not account for those same startups after they grow enough to take a breath and start optimizing costs.
The “how” question has changed every 3 years in the last 10 that I’ve done data work. That trend is driven primarily by data scale and partially that analytics and ML access patterns have evolved a lot.
This post [0], and the open job requisitions suggest you’re using a bit of cash to do this. What is your business model?
0: https://mobile.twitter.com/grouparoo/status/1292844163617239...
after a certain sales price, there are very few potential buyers, and unclear if the usual top acquirers would want to pay the premium (google, ms, salesforce, ...). likewise, always risk of things going sideways wrt ipo. $1-3B exit in this situation makes a lot of sense.
more interesting to me is twilio seems to be such an operationally awesome company, going through each phase of growth so well