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.
So if I understand well, investors are not really investing for the company results, but more on the hope that people will continue to invest in the company?
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.
Definitely seem like bad investments from my perspective on databricks.
Databricks is great at offering a "distributed spark/kubernetes in a box" platform. But its AI integration is one of the least helpful I've experienced. It's very interuptive to a workflow, and very rarely offers genuinely useful help. Most users I've seen turn it off, something databricks must be aware of because they require admins permission for users to opt out of AI.
I don't mean to rant, there's lots that is useful in databricks, but it doesn't seem like this funding round is targeting any of that.
i don't think that it is possible to raise a 100 billion without name dropping ai in every sentence in every meeting you have with a potential investor....
My company is heavily invested in Databricks and let me tell you it sucks. 5 min to spin up a job that needs to run for 10 seconds is a terrible way to spend ones time and money.
Unfortunately what I see is companies, especially smaller companies who originally got into Databricks because they hired people with Databricks/Spark experience, are trying to get away from the platform because it is too expensive -- and with that kind of money it is just easier to use Snowflake.
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?
Investors put 10 billion in in a previous round; that's a lot. Somehow, more is needed now. 100M is just 1% of that. So it's not going to massively move the needle. But it does raise the question where all that cash is going.
My guess is that they might be about to embark on a shopping spree and acquire some more VC backed companies. They've actually bought quite a few companies already in the past few years. And they would need cash to buy more. The company itself seems healthy and generating revenue. So, it shouldn't strictly need a lot of extra capital. Acquisitions would be the exception. You can either do that via share swaps or cash. And of course cash would mostly go to the VCs backing the acquired companies. Which is an interesting way to liquidate investments. I would not be surprised to learn that there's a large overlap with the groups of VCs of those companies and those backing databricks. 100M$ on top of 10B sounds like somebody wants in on that action.
As a financial construction it's a bit shady of course. VCs are using money from big institutional investors to artificially inflate one of their companies so that it can create exits for some of their other investments via acquisitions financed with more investment. It creates a steady stream of "successes". But it sounds a bit like a pyramid game. At some point the big company will have to deliver some value. I assume the hope is some gigantic IPO here to offload the whole construction to the stock market.
Stock dilution doesn't work like that. If a seed investor invests for 5% at a $10mil valuation, and the company goes 10x (ie. a valuation of $100mil), if the company now raises a $100mil Series K, that means the Series K investor owns 50% of the company, and the seed investor got diluted down to 2.5%. However, the new valuation of the company is now $200mil with the cash that the new investor brought in, effectively making the seed investor's investment worth the same.
It's a smaller piece of a bigger pie.
To answer your question, the right question to ask is why go public when you can remain private? Public means more paperwork, more legalese, more scrutiny, and less control for the founder, and all of that only to get a bit more liquidity for your stock. If you can remain private, there really isn't much of a reason to not do that.
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.
An order of magnitude less scrutiny, but also an order of magnitude in size of investor base. The private markets trade at Palantir levels so why go public. Also the private markets are now routinely doing secondary transactions so even less reason to go public.
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?
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.
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.
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
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).
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
My little Databricks story: we setup hosted model inference for an in-house model. Worked great for several months!
But then they did maintenance and broke the entire feature. Reconfiguring everything from scratch didn't work. A key part where a Docker image is selected was replaced with a hard-coded value including a long system path (and employee name -- verified via LinkedIn).
Because of constant turn-over in account reps we couldn't get any help there. General support was of no use. We finally got acknowledgement of the issue when we got yet another new account rep, but all they did was push us towards paid support.
We exhaustively investigated the issue and it was clearly the case that nothing could be done on our end to resolve it. The entire underlying compute layer was busted.
Eventually they released a newer version of the feature which did work again, but at this point it has become impossible to justify the cost of the platform and we're 100% off.
Good luck to them, but from my experience the business fundamentals are misaligned and it's not a company I hope to ever work with again.
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?
Since this year the employees are vesting RSUs (not options, and also no expiry date) quorterly now, they sell a portion of them (automatically) and pay taxes to the government at each vesting event, as the expiry date no longer exists. For liquidity there are tenders where employees sell their stock privately, so the employees no longer need IPO to cash out.
Just to clarify - for many years employees were getting the RSUs not options, just with the expiratation date attached - which is gone since this year.
Just finished ripping out Databricks at one of my clients, and have several more queued up. Folks can't wait to get as far away as they can, and as fast as they can from any of their offerings. Poor performance, bad product, bad UX: hard to get even decent logs out of the damn thing, and it's incredibly overpriced.
They told a good story and had a good sales team, but the writing is on the wall for them.
[+] [-] jakozaur|6 months ago|reply
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.
[+] [-] ygouzerh|6 months ago|reply
In a kind of a ... ponzi pyramid?
[+] [-] devoutsalsa|6 months ago|reply
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.
[+] [-] ed_elliott_asc|6 months ago|reply
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.
[+] [-] benrutter|6 months ago|reply
Databricks is great at offering a "distributed spark/kubernetes in a box" platform. But its AI integration is one of the least helpful I've experienced. It's very interuptive to a workflow, and very rarely offers genuinely useful help. Most users I've seen turn it off, something databricks must be aware of because they require admins permission for users to opt out of AI.
I don't mean to rant, there's lots that is useful in databricks, but it doesn't seem like this funding round is targeting any of that.
[+] [-] whalesalad|6 months ago|reply
[+] [-] alwahi|6 months ago|reply
[+] [-] burnerzzzzz|6 months ago|reply
[+] [-] ferguess_k|6 months ago|reply
[+] [-] TrackerFF|6 months ago|reply
Wouldn’t the current strategy result in some serious stock dilution for the early investors?
[+] [-] jillesvangurp|6 months ago|reply
My guess is that they might be about to embark on a shopping spree and acquire some more VC backed companies. They've actually bought quite a few companies already in the past few years. And they would need cash to buy more. The company itself seems healthy and generating revenue. So, it shouldn't strictly need a lot of extra capital. Acquisitions would be the exception. You can either do that via share swaps or cash. And of course cash would mostly go to the VCs backing the acquired companies. Which is an interesting way to liquidate investments. I would not be surprised to learn that there's a large overlap with the groups of VCs of those companies and those backing databricks. 100M$ on top of 10B sounds like somebody wants in on that action.
As a financial construction it's a bit shady of course. VCs are using money from big institutional investors to artificially inflate one of their companies so that it can create exits for some of their other investments via acquisitions financed with more investment. It creates a steady stream of "successes". But it sounds a bit like a pyramid game. At some point the big company will have to deliver some value. I assume the hope is some gigantic IPO here to offload the whole construction to the stock market.
[+] [-] impulser_|6 months ago|reply
[+] [-] n2d4|6 months ago|reply
It's a smaller piece of a bigger pie.
To answer your question, the right question to ask is why go public when you can remain private? Public means more paperwork, more legalese, more scrutiny, and less control for the founder, and all of that only to get a bit more liquidity for your stock. If you can remain private, there really isn't much of a reason to not do that.
[+] [-] mgfist|6 months ago|reply
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.
[+] [-] jgalt212|6 months ago|reply
[+] [-] simonebrunozzi|6 months ago|reply
[+] [-] Lionga|6 months ago|reply
[+] [-] echelon|6 months ago|reply
Plus the markets are in a weird state right now.
[+] [-] thinkindie|6 months ago|reply
[+] [-] namenotrequired|6 months ago|reply
[+] [-] TuringNYC|6 months ago|reply
[+] [-] groaninvasion|6 months ago|reply
[+] [-] JCM9|6 months ago|reply
[+] [-] apwell23|6 months ago|reply
whats so hard about this. i don't get it.
[+] [-] uxcolumbo|6 months ago|reply
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.
[+] [-] AJRF|6 months ago|reply
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.
[+] [-] dahcryn|6 months ago|reply
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
[+] [-] hiyer|6 months ago|reply
[+] [-] unknown|6 months ago|reply
[deleted]
[+] [-] jinjin2|6 months ago|reply
[+] [-] anktor|6 months ago|reply
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
[+] [-] mjaques|6 months ago|reply
[+] [-] nikolayasdf123|6 months ago|reply
I never seen such invertment round. aren't you supposed to stop at C or D? .. or at least at some point?
[+] [-] geodel|6 months ago|reply
[+] [-] rmonvfer|6 months ago|reply
[+] [-] mr-wendel|6 months ago|reply
But then they did maintenance and broke the entire feature. Reconfiguring everything from scratch didn't work. A key part where a Docker image is selected was replaced with a hard-coded value including a long system path (and employee name -- verified via LinkedIn).
Because of constant turn-over in account reps we couldn't get any help there. General support was of no use. We finally got acknowledgement of the issue when we got yet another new account rep, but all they did was push us towards paid support.
We exhaustively investigated the issue and it was clearly the case that nothing could be done on our end to resolve it. The entire underlying compute layer was busted.
Eventually they released a newer version of the feature which did work again, but at this point it has become impossible to justify the cost of the platform and we're 100% off.
Good luck to them, but from my experience the business fundamentals are misaligned and it's not a company I hope to ever work with again.
[+] [-] bix6|6 months ago|reply
Also announcing the signed term sheet but not the close so this is a PR push to find more investors?
[+] [-] rgiar|6 months ago|reply
[+] [-] lolive|6 months ago|reply
[+] [-] xendo|6 months ago|reply
[+] [-] i7l|6 months ago|reply
[+] [-] dude250711|6 months ago|reply
[+] [-] retinaros|6 months ago|reply
[+] [-] nikolayasdf123|6 months ago|reply
[+] [-] throwawaydbb|6 months ago|reply
Just to clarify - for many years employees were getting the RSUs not options, just with the expiratation date attached - which is gone since this year.
[+] [-] hiyer|6 months ago|reply
[+] [-] tormeh|6 months ago|reply
[+] [-] iamleppert|6 months ago|reply
They told a good story and had a good sales team, but the writing is on the wall for them.
[+] [-] georgemcbay|6 months ago|reply