If you Google "Series K investment" basically all the hits are about this. Same applies for J and I - you have to get back to H before you start seeing anyone else.
What’s the obvious rationale for going through the whole alphabet of funding rounds, instead of going public / IPO after «the usual» number of raising money.
Wouldn’t the current strategy result in some serious stock dilution for the early investors?
Both have benefits. Staying private means a lot less distractions, less investor scrutiny (good and bad), and the general ability to do whatever you want (good and bad).
It's a lot easier to stay long-term focused without investors breathing down your neck. As a private company you're not dealing with shortsellers, retail memers, institutional capital that wants good earnings now, etc..
Of course, the bad side is that if the company gets mismanaged, there's far less accountability and thus it could continue until it's too late. In the public markets it's far easier to oust the C-suite if things go south.
It's a shame that the trend of staying private longer means retail gets shut out from companies like this.
I always struggled to understand how do you make a company adopt a platform like databricks to « manage data » isnt managing data a minefield with plenty of open source pieces of software that serve different purposes ? who is the typical databricks customer?
you kill off all open source pieces, in turn compliance is happy, and a CTO is happy because he has a maintenance contract and can blame other people if stuff goes wrong.
It's a way to get those pesky Python people to shut up
Oh, and a CTO is always valued more if he manages a 5 million Databricks budget, where he can prove his worth by showing a 5% discount het negotiated very well, than a 1 million whatever-else budget that would be best in class. Everybody wins.
My company is doing the dbx thing, and the best I can tell my manager is that I'm neutral on it.
My working theory is that the UI, a low-grade web-based SQL editor and catalog browser, is more integrated that the hodgepodge of tools that we were using before, and people may gain something from that. I've seen similar with in-house tools that collect ad-hoc/reporting/ETL into one app, and one should never underestimate the value that people give to the UI.
But we give up price-performance; the only way it can work is if we shrink the workload. So it's a cleanup of stale pipelines combined with a migration. Chaos in other words.
I think the governance stuff might push it over the top for a lot of organisations; it's pretty well integrated with IAM providers not only for structured/modelled data but also workspaces for the data sciencey stuff. Pretty much everything has permissions associated with it. When you have a big data engineering/science push off the back of the AI hype I think it appeals to the cheque writers to have something centralised and controlled.
Aside from that I do get the feeling that most small and medium sized companies have been oversold on it - they don't really have enough data to leverage a lot of the features and they don't really have the skill a lot of the time to avoid shooting themselves in the foot. It's possible for a reporting analyst upskilling to learn the programming skill to not create a tangled web of christmas lights but not probable in most situations. There seems to be a whole cottage industry of consultancies now that purport to get you up and running with limited actual success.
At least it's an incentive for companies to get their data in order and standardise on one place and a set of processes.
In terms of actual development the notebook IDE feels like big old turd to use tho and it feels slow in general if you're at all used to local dev. People do kinda like these web based tools tho. Can't trust people all the time! There's VS code and PyCharm extensions but my team work mainly with notebooks at the moment for good or ill and the experience there is absolute flaky dogshit.
I think it's possible to make some good stuff with it and it's paying my bills at the moment, but I think a lot of the adoption may be doomed to failure lol
we have databricks at my company 50m ARR, 150 employee thats still growing at 15% YoY.. With 0 full time Data Engineer (1 data scientist + 1 db admin manages everything on there as part time jobs). We are able to have data from like 100 transactional database tables, Zendesk, all our logs of every API call, every single event from every user in our mobile and web applications, banking data, calendar data, goole play store data, apple store data, all in 1 place. We are a 2-sided marketplace, we can easily get 360 degree data on our B2B customers, B2C customers, measure employee productivity across all departments.
My team of 3 data scientists are able to support a culture of experimentation, data-informed decision making accross the entire org.
And we do all that 30k annual spend on databricks. That's less than 1/5 the cost of 1 software engineer. Excellent value for money if you ask me.
I really struggle to imagine being able to that any cheaper. How else we can engineer a hub for all of our data and manage appropriate access, run complex calculations in seconds, join data from so many disparate sources, at a total cost (tool + labor) <80k/yr. I double dare you to suggest or find me a cheaper option for our use case.
Why Databricks would do this (rather than IPO) is obvious. When you can raise privately, it’s way easier than IPO. The real question to me is why the investors (new and previous) are going along with it?
Regardless of the product and idea they had, a company that is 15 years old and raised 10+ billion dollars still needing to raise money after all this time is ridiculous.
Not being sustainable after all this time and billions of dollars is a sign company is just burning money, and a lot of it. wework vibes.
This. To me if you are still unprofitable after 15 years you are not really a business.
However genuinely curious about the thesis applied by the VC’s/Funds that invest in such a late stage round? Is it simply they are taking a chance that they won’t be the last person holding the potato? Like they will get out in series L or M rounds or the company may IPO by then. Either ways they will make a small return? Or is the calculus diff?
Same story was with Spunk. Yet it was acquired by Cisco for $28 billion.
Valuation and ability to burn cash for 10+ years of the Silicon Valley companies never cease to amaze me.
Databricks on azure is huge. I've heard that in some Azure regions, over 70% of the compute usage is just Databricks. So there is definitely an incentive for MS to acquire them.
Depends on how you define cheaper - you could set up Apache Iceberg, Spark, MLFlow, AirFlow, JupyterLab, etc and create an abomination that sort of looks like Databricks if you squint, but then you have to deal with set up, maintenance, support, etc.
Computationally speaking - again depends on what your company does - Collect a lot of data? You need a lot of storage.
Train ML Models, you will need GPUs - and you need to think about how to utilise those GPUs.
Or...you could pay databricks, log in and start working.
I worked at a company who tried to roll their own, and they wasted about a year to do it, and it was flaky as hell and fell apart. Self hosting makes sense if you have the people to manage it, but the vast majority of medium sized companies will have engineers who think they can manage this, try it, fail and move on to another company.
Exasol costs us a fraction of what we used to pay for Databricks, and that is even with us serving far more users than we used to do (from a data size perspective we are not at the petabytes scale yet, but getting there).
I don't think there is anything out there that really bundles everything exactly like databricks does.
There are better storage solutions, better compute and better AI/ML platforms, but once you start with databricks, you dig yourself a hole because the replacing it is hard because it has such a specific subset of features across multiple domains.
In our multinational environment, we have a few companies that are on different tech stacks (result of M&A). I can say Snowflake can do a lot of the things Databricks does, but not everything. Teradata is also great and somehow not gaining a lot of traction. But they are near impossible to get into as a startup, which does not attract new talent to give it a go.
On the ML side, Dataiku and Datarobot are great.
Tools like Talend, snaplogic, fivetran are also really good at replacing parts of databricks.
So you see, there are better alternatives for sure, cheaper at the same time too, but there is no drop-in replacement I can think of
It's been mentioned but I want to add that the original idea of the post (mid size VPS hosting apache spark) might be missing that spark is ideal for distributed and resilient work (if a node fails the framework is able to avoid losing that work).
If you don't need this features, specially the distributed one, going tall (single instance with high capacity, replicate when necessary) or going simpler (multiple servers but without spark coordinating the work) could be good options depending on your/the team's knowledge
This curvature of spacetime is caused by the mass of the AI bubble.
While many comments were focused on the "K" letter, I wanted to remind us all that OpenAI stretched their Series E from Jan 23, 2023 to Nov 22, 2024
-- 23 months, squeezing in 6 rounds
Their product looks like basic wrappers for managing postgres instances and dashboards. Why would anyone with even minimal technical expertise pay for a generic service like that?
It doesn't look like a typical round for raising capital for investments. Instead:
1. Liquidity: Early investors could sell to late-stage investors, since they are not IPO. Their previous round looked like that.
2. Markup: The previous investors can increase their valuation by doing a round again. It also provides a paper valuation for acquiring new companies. That combined with preferred stock (always get 1x back) might be appealing and make some investors more generous on valuation.
I had no idea how preferred shares actually worked, so I went down a rabbit hole looking it up. That "always get 1x back" thing you mentioned is called a liquidation preference, which means preferred shareholders get their money back first before anyone else sees a dime.
Turns out there are different flavors too. "Non-participating" means preferred gets their original investment back, then common stock splits whatever's left. "Participating" means preferred gets their money back AND also gets to participate in splitting the leftovers with common shareholders. No wonder investors are willing to pay up for these late-stage rounds when they've got that safety net.
Looks like someone is thinking “hey let’s wave our hands in the air and talk about AI and someone will write us a cheque!” as a way to kick the can down the road that this far into it they’re still not selling a product that’s making money. Looks a bit desperate TBH.
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[ 6.9 ms ] story [ 67.9 ms ] threadWouldn’t the current strategy result in some serious stock dilution for the early investors?
It's a lot easier to stay long-term focused without investors breathing down your neck. As a private company you're not dealing with shortsellers, retail memers, institutional capital that wants good earnings now, etc..
Of course, the bad side is that if the company gets mismanaged, there's far less accountability and thus it could continue until it's too late. In the public markets it's far easier to oust the C-suite if things go south.
It's a shame that the trend of staying private longer means retail gets shut out from companies like this.
Ai is not far away from dropping to the “trough of disillusionment” and I can’t see why databricks even needs Postgres.
Hopefully I’m wrong as I’m a big fan of databricks.
I never seen such invertment round. aren't you supposed to stop at C or D? .. or at least at some point?
It's a way to get those pesky Python people to shut up
Oh, and a CTO is always valued more if he manages a 5 million Databricks budget, where he can prove his worth by showing a 5% discount het negotiated very well, than a 1 million whatever-else budget that would be best in class. Everybody wins.
The CTO of a "traditional" company who is responsible for "implementing digital transition".
My working theory is that the UI, a low-grade web-based SQL editor and catalog browser, is more integrated that the hodgepodge of tools that we were using before, and people may gain something from that. I've seen similar with in-house tools that collect ad-hoc/reporting/ETL into one app, and one should never underestimate the value that people give to the UI.
But we give up price-performance; the only way it can work is if we shrink the workload. So it's a cleanup of stale pipelines combined with a migration. Chaos in other words.
Aside from that I do get the feeling that most small and medium sized companies have been oversold on it - they don't really have enough data to leverage a lot of the features and they don't really have the skill a lot of the time to avoid shooting themselves in the foot. It's possible for a reporting analyst upskilling to learn the programming skill to not create a tangled web of christmas lights but not probable in most situations. There seems to be a whole cottage industry of consultancies now that purport to get you up and running with limited actual success.
At least it's an incentive for companies to get their data in order and standardise on one place and a set of processes.
In terms of actual development the notebook IDE feels like big old turd to use tho and it feels slow in general if you're at all used to local dev. People do kinda like these web based tools tho. Can't trust people all the time! There's VS code and PyCharm extensions but my team work mainly with notebooks at the moment for good or ill and the experience there is absolute flaky dogshit.
I think it's possible to make some good stuff with it and it's paying my bills at the moment, but I think a lot of the adoption may be doomed to failure lol
My team of 3 data scientists are able to support a culture of experimentation, data-informed decision making accross the entire org.
And we do all that 30k annual spend on databricks. That's less than 1/5 the cost of 1 software engineer. Excellent value for money if you ask me.
I really struggle to imagine being able to that any cheaper. How else we can engineer a hub for all of our data and manage appropriate access, run complex calculations in seconds, join data from so many disparate sources, at a total cost (tool + labor) <80k/yr. I double dare you to suggest or find me a cheaper option for our use case.
Not being sustainable after all this time and billions of dollars is a sign company is just burning money, and a lot of it. wework vibes.
That costs a fair bit of dosh.
However genuinely curious about the thesis applied by the VC’s/Funds that invest in such a late stage round? Is it simply they are taking a chance that they won’t be the last person holding the potato? Like they will get out in series L or M rounds or the company may IPO by then. Either ways they will make a small return? Or is the calculus diff?
I can’t know if it’s completely true ofc, but that’s what employees are told.
What's a good roll your own solution? DB storage doesn't need to be dynamic like with DynamoDB. At max 1TB - maybe double in the future.
Could this be done on a mid size VPS (32GB RAM) hosting Apache Spark etc - or better to have a couple?
P.S. total beginner in this space, hence the (naive) question.
Computationally speaking - again depends on what your company does - Collect a lot of data? You need a lot of storage.
Train ML Models, you will need GPUs - and you need to think about how to utilise those GPUs.
Or...you could pay databricks, log in and start working.
I worked at a company who tried to roll their own, and they wasted about a year to do it, and it was flaky as hell and fell apart. Self hosting makes sense if you have the people to manage it, but the vast majority of medium sized companies will have engineers who think they can manage this, try it, fail and move on to another company.
There are better storage solutions, better compute and better AI/ML platforms, but once you start with databricks, you dig yourself a hole because the replacing it is hard because it has such a specific subset of features across multiple domains.
In our multinational environment, we have a few companies that are on different tech stacks (result of M&A). I can say Snowflake can do a lot of the things Databricks does, but not everything. Teradata is also great and somehow not gaining a lot of traction. But they are near impossible to get into as a startup, which does not attract new talent to give it a go.
On the ML side, Dataiku and Datarobot are great.
Tools like Talend, snaplogic, fivetran are also really good at replacing parts of databricks.
So you see, there are better alternatives for sure, cheaper at the same time too, but there is no drop-in replacement I can think of
If you don't need this features, specially the distributed one, going tall (single instance with high capacity, replicate when necessary) or going simpler (multiple servers but without spark coordinating the work) could be good options depending on your/the team's knowledge
Mega lmao. They already owe $20B.
Their revenue is good, though, further adding to the mistery.
Rust + Cloud Object Store/serverless/S3 + Postgres. Slap "AI agents" on top: keyword peak reached. So they will easily raise the 100bn.
Meanwhile, this is Lakebase/Neon: https://blog.opensecret.cloud/why-we-migrated-from-neon-to-p...
Due diligence? Taboo.
While many comments were focused on the "K" letter, I wanted to remind us all that OpenAI stretched their Series E from Jan 23, 2023 to Nov 22, 2024 -- 23 months, squeezing in 6 rounds
source: https://tracxn.com/d/companies/openai/__kElhSG7uVGeFk1i71Co9...
Their product looks like basic wrappers for managing postgres instances and dashboards. Why would anyone with even minimal technical expertise pay for a generic service like that?
1. Liquidity: Early investors could sell to late-stage investors, since they are not IPO. Their previous round looked like that.
2. Markup: The previous investors can increase their valuation by doing a round again. It also provides a paper valuation for acquiring new companies. That combined with preferred stock (always get 1x back) might be appealing and make some investors more generous on valuation.
Turns out there are different flavors too. "Non-participating" means preferred gets their original investment back, then common stock splits whatever's left. "Participating" means preferred gets their money back AND also gets to participate in splitting the leftovers with common shareholders. No wonder investors are willing to pay up for these late-stage rounds when they've got that safety net.
Also announcing the signed term sheet but not the close so this is a PR push to find more investors?