Databricks at $134B: Why the Data Layer Is the Real Bet
Databricks is a top 2026 IPO candidate at roughly $134B after a late-2025 Series L, and as a backend engineer I think the data-and-AI platform layer is the durable one.
Amid all the model-company headlines, one name on the 2026 IPO watchlist sits at a different layer of the stack. Databricks is considered a top IPO candidate for the year, with a valuation of about $134 billion following a Series L round in late 2025. It isn't a model company. It represents the data-and-AI platform layer — the place where data gets stored, governed, and fed into whatever models you choose to run.
That distinction is the whole reason I find this one more interesting than the model races.
Models rent; data compounds
The layer everyone funds loudest is the model layer, but I've always thought the more durable position is one below it. Here's the way I reason about it:
- Models are swappable. You can change which model you call this quarter. Migrating your entire data estate is a multi-year project nobody undertakes casually.
- Data has gravity. The more of your operational and analytical data lives in one platform, the higher the cost of leaving — which is exactly why a platform at that layer can command a $134 billion valuation.
- Governance is sticky. Lineage, access control, and audit trails are precisely the things you don't want to rebuild. Once they're wired into a platform, they stay.
That gravity cuts both ways, and as a builder I hold both sides at once. It's why the data layer is a strong business. It's also why I'm careful about how deep I let any one platform's proprietary formats reach into my architecture.
The portability question I'd ask
If I were standing up a data-and-AI platform on something at this layer, the question wouldn't be "is it powerful" — at $134 billion, clearly the market thinks so. The question would be "how much of this is portable if I ever need to leave":
- Are my tables in an open format I could read with other engines, or in something only this vendor reads?
- Is my transformation logic expressed in portable SQL and code, or in platform-specific abstractions I'd have to rewrite?
- Can I get my data out at reasonable cost and speed, or is egress the silent lock-in?
The healthiest version of this layer is one that leans on open formats, because that's what keeps the relationship a choice rather than a trap. Where a platform genuinely supports open table formats, the lock-in is operational convenience rather than a cage — and I'm comfortable paying for convenience as long as the exit stays real.
What the $134 billion valuation tells me is that the market has figured out something backend engineers have known for a long time: the data layer outlasts the application layer above it. Frameworks come and go, models get swapped quarterly, but the system of record tends to stay put for years. A company sitting at that layer is selling permanence, and permanence is valuable.
My take, then, is less about whether Databricks IPOs well and more about the structural point it illustrates. When the dust settles on the model gold rush, the businesses still standing will often be the ones holding the data, the governance, and the pipelines. That's the layer I'd want to own a position in if I were investing — and the layer I'd be most deliberate about keeping portable if I were building.
Sources: Built In, AlphaSense.