Generally agree on your broad point, but not on the architecture. I'm biased, but I would suggest that if you follow GitPrime's lead, you architect with OSS components that you can deploy on-prem as it was crucial to their success: https://blog.replicated.com/gitprime-enterprise-saas/
Trying to build a vertical analytics offering on top of OSS increases the level of difficulty by 100x. I’m not saying that it’s never the right answer, but I am saying there’s a large category of companies waiting to be built where the primary value is the expertise around how to analyze the data, not the infrastructure. If you’re analyzing data that comes from cloud data sources anyway, there’s really no sense in “deploying”. CIOs ask for it, you say no, you do the security compliances and eventually they come around.
"If you’re analyzing data that comes from cloud data sources anyway, there’s really no sense in “deploying”."
- This is only true if the security controls that your team, application, infrastructure has in place is matches the major cloud providers (i.e. Salesforce, Google, AWS, Microsoft). Even then, spreading your data around to 1,000 different SaaS vendors increases the surface area for attack/loss by 1000x.
"Trying to build a vertical analytics offering on top of OSS increases the level of difficulty by 100x"
- 100x is hyperbole, it significantly harder before OSS was focused on operations, but now there is an HA Helm chart, or even an K8s operator for most of the popular OSS components. It might still be slightly harder today, but organizations that want to pull insights from THEIR data often value the proprietary nature of that data.
Keep in mind you're replying to the CEO of a YC W13 company that wasn't acquired two months ago for $150M by Google (that was his competitor, Alooma [1]) and wasn't acquired in November for $60M by Talend (that was Stitch Data, another competitor [2]). Your points are absolutely valid, you're just barking up the wrong tree. Both of those were SaaS-based ETL connector companies that were acquired while executing a similar enough strategy, and they both saw nice exits.
If you look at his comment history, you'll notice he mentions Snowflake at every single opportunity. Snowflake raised $450M last October (~$1B valuation), so they have a nice warchest for strategic acquisitions.
I stand by 100x in this context. OSS is way behind the commercial analytical databases. And deploying a horizontally scaleable analytical database is a completely different ballgame than, say, Postgres.
I don't have much experience with the exact topic, but anyone throwing phrases like "100x more difficult" very quickly loses credence with people.
How do you measure 100x? How come it isn't 90x or 112x?
Does that mean what one engineer can do with a commercial database, you would need 100 people do the the same thing with OSS in the same time frame?
If you used a phrase like "difficulty on another level" or something descriptive like that instead, I'm sure people would be more interested to hear what you have to say.