ClickHouse acquires Langfuse(langfuse.com) |
ClickHouse acquires Langfuse(langfuse.com) |
<i>Database technology startup ClickHouse Inc. has raised $400 million in a new funding round that values the company at $15 billion — more than double its valuation less than a year ago. </i>
https://www.bloomberg.com/news/articles/2026-01-16/clickhous...
Acquired hyperdx[1] for their clickstack[2] observability platform and adding langfuse to a bunch of other llm related acquisitions and products
They're really building out a snowflake / databricks alternative
[0] https://clickhouse.com/cloud/postgres
I wish there was less of it, we'd have better software then, but :/
What do you do? “We power your agents” okay… but what do you do? How do you do that?
Every DB, storage system, and analytics tool website is like this lately.
Interesting headline for a checks notes time series database company.
1: https://news.ycombinator.com/item?id=44194082 2: https://clickhouse.com/use-cases/observability
The tools you mentioned are indeed to be avoided. I trialed them early on and quickly realized in 99.9% they do nothing but bog you down. Pretty sure they'll be dead sooner rather than later.
These frameworks provide structure for established patterns,but they also actually do a lot that you don't have to do anymore. If you are for example building an agentic application then these kind of frameworks make it very simple to create the workflows, do the chat with the model providers, provide structure for agentic skills, decision making and the human in the loop, etc. etc.
All stuff that I would consider "low level". All things you don't have to build.
If you have an aversion to frameworks then sure - by all means. But if you like to move faster and using good building blocks then these frameworks really help.
One thing to keep in mind - many of these AI frameworks are open source and work really well without needing backend services. Or you can self host them where needed. But for many that is also the premium model, please use and pay for our backend services. But that is also a choice of course.
Two years in the LLM race will have definitely depleted their seed raise of $4m from 2023, and with no news of additional funds raised it's more than likely this was a fire sale.
They also say in the announcement that they had a term sheet for a good series a.
I think the team just took the chance to exit early before the llm hype crashes down. There is also a question of how big this market really is they mostly do observability for chatbots but there are only so many of those and with other players like openais tracing, pydantic logfire, posthog etc they become more a feature than a product of its own. Without a great distribution system they would eventually fall behind I think.
2 years to a decent exit (probably 100m cash out or so with a good chunk being Clickhouse shares) seems like a good idea rather than betting on that story to continue forever.
Snowflake acquired Observe last week, AWS made it easy in December to put logs from Cloudwatch in their managed iceberg catalog, and Azure is doing a bunch of interesting stuff with Fabric.
The line between your data lake/analytics vendor and observability vendor is getting blurry.
every single day there is an acquisition on here. what's going on in the macro?
It seems like an expansion play from their team and their end vision as both a platform (clickhouse + postgres) and product (observability) seems to be pretty good combo that fits hand in hand.
For good or bad, I think we're pretty "SaaS/vc/etc." already.
It’s still early but I question how much of these SaaS companies will continue. I’d rather connect Claude or whatever to do my task than have to learn a new platform let alone login to it.
Or well, technically incorrect, as someone will surely point out. US companies can be legally compliant with GDPR, it's just that the likes of the CLOUD Act and FISA make it completely meaningless.
Before anyone comes in talking about how it's farfetched that those matter, it's 100x as far-fetched that self-hosted Chinese LLM models would exfiltrate your data (you can even airgap them) yet 90% of corporate America is avoiding them based solely on the country they were trained in. Compared to that insanity, above US acts are a very real threat.
And that's of course on top of that now an adversarial state's company has the power to immediately dissolve Langfuse.
Having said that, I struggled a lot with actually implementing langfuse due to numerous bugs/confusing AI driven documentation. So I’m amazed that it’s being bought to be really frank. I was just on the free version in order to look at it and make a broader recommendation, I wasn’t particularly impressed. Mileage may vary though, perhaps it’s a me issue.
You want to see the best results you can get from a prompt, so you use features like prompt management an A/B testing to see what version of your prompt performs better (i.e. is fit to the model you are using) on production.
This is a big reason why there are so few EU tech startups, they get bought out if they're doing well, more and more consolidation in tech, more and more "exits".
https://clickhouse.com/blog/clickhouse-raises-400-million-se...
[0] https://clickhouse.com/blog/clickhouse-acquires-langfuse-ope...
Disclosure: I run Altinity, a vendor in this space.
(Update: Disclaimer -> Disclosure. Sigh.)
(they've never been a time series database company either lol)
It’s great when you get this insight as a student of NLP, because suddenly your toolset grows quite a bit.
They do have GitHub discussions where you can raise things, but I also encountered some issues with installation that just made me want to roll the dice on another provider.
They do have a new release coming in a few weeks so I’ll try it again then for sure.
Edit: I think I’m coming across as negative and do want to recommend that it is worth trying out langfuse for sure if you’re looking at observability!
But those are also very trivial to build, and you end up having to customize them for your need, and if the framework don't have those levers, better be prepared to either fork the framework, or spend time contributing upstream.
Or, start simple yourself with what you need, use libraries for the hairy parts you don't want to be responsible for the implementation of, then pipe these things together. You'll get a less compromised experience, and you'll understand 100% how everything works, which is the part people generally try to avoid and that's why they're reaching for frameworks.
> But if you like to move faster and using good building blocks then these frameworks really help.
I find that they help a lot with the "move faster" part in the beginning, but after that period, they slow you down instead. But I'm also a person that favors "slow software design and development" where you take your time to nail down a good design/architecture before you run. Slow is fast, and avoiding hairballs is the most important part if you're aiming for "move fast for longer" rather than "a sprint of fast".
always nice to see a database ceo be "one of us" and/or "write like a real human being".
E.g, fitting a model to house prices, you don’t care if feature 1 is square meters and feature 2 is time on market, or vice versa, but in a time series, your model changes if you reverse the order of features.
With text, the meaning of word 2 is dependent on the meaning of word 1. With stock prices, you expect the price at time 2 to be dependent on time 1.
Text can be modeled as a time series.
A language model tells you the next character/token/word depending on the previous input.
Language models are time series.
It’s not an audacious claim.
Any student of nlp should have met a paper modeling text as time series before writing their thesis. How could you not meet that?
RAG and vector Approximate Nearest Neighbour (ANN) is the the go to use case.
[2] https://arxiv.org/abs/2506.02389
[3] https://arxiv.org/html/2402.10835v3
Some links from the top of Google search.
Take a look here, also, it's an important law: https://en.wikipedia.org/wiki/Benford%27s_law
It is possible for LLMs to learn Bernford's law, implicitly. So they will be non-null predictors of time series data, because time series data is also Bernford-law-distributed [4].
[4] https://ui.adsabs.harvard.edu/abs/2017EGUGA..19.2950T/abstra...
My prediction, not going to be a good investment.
I clearly didn't build pydantic "to take VC money" - I maintained Pydantic for 5 years before deciding to take VC money.
I wanted to build logfire and I didn't want to compete with or restrict our open source, we so built logfire.
Let's see on the investment; seems to be going pretty well so far.
Yeah, like FOSS which is drastically underfunded since birth, yet continues to put out software that the entire world ends up relying on, instead of relying on whatever VC-pumped companies are putting out.
I'm not talking "better software" as in "made a lot of money", I meant "better" as in "had a better impact on the world".
FOSS software that many rely on that has been around for a while were non-VC: VCS, Linux / GNU / BSD, web browsers, various programming languages, various databases...
I don’t think everything VCs touch is gold, but it’s also not the case that they are pure evil either. It’s almost as if you can’t claim they are all good or all bad.
They tend to have more grounded financials (read: paths to profitability) and while the pay packages aren't quite aligned with the top end of the market, they also tend to manage headcount more responsibly than FAANG. I work with a fairly niche stack and I'm constantly finding new companies that I've never heard of and don't raise VC rounds.
Long way of saying that just because they're not easy to find doesn't mean they don't exist.
I don't know why people are so upset here.
Back then it was very normal to get VC funding and then hire the core committers of your most important open source software and pay them to keep working on it. I worked at Sendmail in the 90s and we had Sendmail committers (obviously) but also BSD core devs and linux core devs on staff. We also had IETF members on staff.
And we weren't unique, this happened a lot.
Was it in a different nature to current VC funded FOSS though? It sounds like their contributions to FOSS was tangential and not the sold product?
Maybe a bit more like Google and Chrome?
It's honestly hard to pick a pattern out for older open source project contributions. PostgreSQL started at UC Berkeley but people contributed to it from all over. Key engineers like Tom Lane worked a number of companies in the database field, some dependent on VC funding, some not. He's currently at Snowflake. [0] A lot of recent innovation around PostgreSQL today (Neon, Supabase, etc.) is VC funded.
That pattern changed with projects like Hadoop, which was about the time that VC funds recognized a standard playbook around monetizing open source. [1]
[0] https://en.wikipedia.org/wiki/Tom_Lane_(computer_scientist)
I think it’s easy to forget how long ago it was when FOSS truly was the outsider and wouldn’t be touched by most companies.
Mozilla/Firefox started in 1998 and then started taking ad revenue from Google in 2005, which pays for a large chunk of its development. It’s been part of the Silicon Valley money machine for 20 years, most of its existence.