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maybe clickhouse can finally make sense of the langfuse documentation
how does it benefit for clickhouse?
Clickhouse needs observability models to be more useful to agent run infra
Congratulations to everyone involved, quite remarkable considering Langfuse was only founded as part of YC 23.
the "Prompt Management" part of these products always seemed odd. Does anyone use it? Why?
(congrats team! such a joy to see you succeed)

every single day there is an acquisition on here. what's going on in the macro?

Does this mean Langfuse will now have better ClickHouse integration?
I predict it will be Pydantic next to get picked up by someone for logfire and agent framework.... fine as long as all these open source projects stay open source then good for them
> Our goal continues to be building the best LLM engineering platform

Interesting headline for a checks notes time series database company.

Just did a funding round. In a sign of the times clickhouse used to be an interesting DB product, but is now a "database software that companies can use as they develop AI agents "

<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...

The fundraising market is very interesting right now. You have to have some AI and agenr narrative without which you do not look very forward looking. You might be a database company with million in revenue but if you do not have a AI narrative you would not be perceived as forward looking as compared to a startup thats burning through millions in token with no path to profitability. It has become table stakes and the new reality for startups.
Langfuse has been my favorite LLM observability solution so far. Hopefully this acquisition makes it better, not worse.
As a big Clickhouse fan, agent evals are where their product really shines. They're buying into market segment where their product is succeeding so they can vertically integrate and tighten up the feedback loop.
For those building applications with Langfuse and Clickhouse - do you like these products? I get the odd request to do an AI thing, and my previous experience with LLM wrappers convinced me to stay away from them (Langchain, Llamaindex, Autogen, others). In some cases they were poorly written, and in other ways the march of progress rendered their tooling irrelevant fairly quickly. Are these better?
We like Langfuse for observability via OpenTelemetry. Prompt management is too basic for our needs.
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SaaS company pivots to AI. Gets funding rebranded as AI company. Buys a company that actually knows it.

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.

Since clickhouse is headquartered in the US that means the langfuse cloud is no longer GDPR compliant.
Isn't ClickHouse owned by Nebius in Amsterdam?
This is part of a bigger consolidation trend, AI hype or not: which general-purpose data vendor gets to store and query all of your observability and business data?

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.

Very sad, for all their marketing around EU, GDPR, privacy and so on. I feel dumb for having fell for it a little.

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".

Clickhouse's full announcement is here https://clickhouse.com/blog/clickhouse-raises-400-million-se... and I think another big piece is directly integrating postgres into their ecosystem.

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.