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Best database to use with Nebius in 2026

7 min readNebiusPostgresAIDatabases

Nebius sells GPUs. That is the product: H100 and H200 clusters, InfiniBand between the nodes, managed Kubernetes and Slurm on top, and a Token Factory for inference. If you are training or fine-tuning a model, or serving it at scale, Nebius wants that job.

It is not really a database company, though. There is a Managed Service for PostgreSQL, which went GA recently, and a Managed ClickHouse, and that is more or less the list. So the moment you build an actual application on top of those GPUs, something with users, sessions, a cache, maybe a vector store, you have to decide where its data lives. Nebius answers part of that question and leaves the rest to you.

Start with where the database sits

A database wants to be near the code that queries it, and for an AI app that code is running on your GPUs.

If you are feeding a training pipeline or answering RAG queries that hit a vector index on every request, a database over in some other provider's region costs you a network round trip on every read. Nobody notices that round trip when it is fronting app metadata at modest traffic. You very much notice it inside an inference loop that runs thousands of times a second.

So it helps to split the stack in two. The hot path, feature stores, high-QPS vector retrieval, streaming ingest, wants to sit right next to your Nebius compute, which points you at Nebius's own managed services or something on a Nebius VM in the same region. Everything else, and for most apps that is the large majority of the data, does not care about a few milliseconds across providers. That part of the stack cares a lot more about how many engines you can reach, whether there is a free tier to prototype on, and whether you are pinned to one region. Keep that division in mind as you read the rest.

Nebius Managed PostgreSQL

This is the native option and it is a good one for what it is. PostgreSQL 16, HA configuration, daily backups you can hold for up to 30 days, managed updates. It ships pgvector, so your embeddings live in the same database as your relational data, which is exactly what a RAG system wants.

The case for it comes down to proximity. The database runs inside the same Nebius network as your GPUs, so the distance between your inference code and your vector index is about as short as it gets, and pgvector will carry you into the tens of millions of vectors before it runs out of room. If your retrieval path is the thing you are optimizing, start here.

Where it stops being enough:

  • The managed catalog is Postgres and ClickHouse. No managed Redis for your cache or sessions, no managed MongoDB, no dedicated vector database, no managed search. Everything else means running it yourself on a VM.
  • There is no free or dev tier. Managed PostgreSQL is a production service priced for production, which is fine for the real workload and annoying when you just want a throwaway database for a branch or a demo.
  • Nebius builds its data centers around AI compute, not around blanketing the globe for low latency everywhere. If your users are spread out and the app is latency-sensitive, that is worth checking before you commit.

For the hot-path slice of the stack, it is the right default. For the rest, keep reading.

Layerbase Cloud

This is what I build, and it fits alongside Nebius rather than against it. Nebius runs the GPUs; Layerbase runs the data layer.

The point of it is breadth from one account. Managed Postgres with pgvector for embeddings, Redis or Valkey for the cache and session store, Qdrant or Weaviate for when you push past what pgvector handles, ClickHouse for analytics, Meilisearch for search, Mongo-compatible document storage, all on one dashboard, one bill, one login. A normal AI app wants a Postgres, a Redis, and a vector store. Nebius manages one of those three; Layerbase covers all three and then some.

Two gaps in the Nebius story matter here. First, there is a free dev tier with no card, and it scales to zero, so a prototype or a preview branch sits there costing nothing until something connects to it. That is the workflow the Nebius managed services were never built for. Second, you are not starting from an empty database. The create flow will pull your existing data over from Neon, Supabase, PlanetScale, or a plain connection string, so switching is closer to "paste a key, pick the database" than a weekend of dump-and-restore.

Price is the part that decides it for a lot of people. Nebius has no free tier, and Managed PostgreSQL bills at production minimums, correct for a training workload, steep for a side service or a preview environment. Layerbase is a flat list you can read off in a sentence: $0 on the free tier with scale-to-zero, $5/mo for Solo, $15/mo for Pro covering the whole engine catalog, and no meters on queries, connections, or egress. For most people that lands well under the cost of standing up a separate production-grade managed database per engine on an AI cloud, and the data layer is not where you want a bill that climbs with your GPU spend.

The honest catch is the one from the latency section. Layerbase is a different provider, so there is a hop between it and your GPUs. Fronting app state, moderate vector search, caching, and search, you will not see it. Inside a tight inference retrieval loop, you will, so keep that co-located. In practice the tidy answer is usually both: hot-path vectors on Nebius Managed PostgreSQL next to the GPUs, and everything else on Layerbase Cloud.

If you want to sketch the schema and the vector setup on your laptop before you provision anything real, SpinDB runs Postgres and 20-odd other engines locally, and you deploy the same engine to Layerbase when it is ready.

Neon

Serverless Postgres with branching. If your app is Postgres and nothing else, and the branch-per-preview-deploy trick appeals to you, Neon does that very well, and pgvector is there for RAG. It carries the same cross-provider hop as Layerbase and the same upsides, a free tier and no single-region lock.

The thing to know is that Databricks bought Neon in May 2025 for a billion dollars. If you would rather your database vendor not also be a data-lakehouse company chasing enterprise AI accounts, that is a mark against it. If that does not bother you, it is a solid managed Postgres.

Supabase

Postgres with auth, storage, and realtime bolted on as a single product, pgvector included. If you want the whole backend in one box and the developer experience clicks for you, it gets you moving fast. The bill comes later, in the form of lock-in on the auth and storage layers that you will pay to pull apart if you outgrow them. On an AI app, where the interesting engineering is the model and the pipeline, wiring your auth into your database vendor is a heavier commitment than it looks like on day one.

Self-hosted on a Nebius VM

Always available, and the real answer for any engine Nebius does not manage. Put a VM in the same region as your GPUs and run Redis, MongoDB, Qdrant, whatever you need, with full co-location and no hop at all.

You pay for it in operations: backups, failover, upgrades, monitoring, patches, all yours now. For a small internal service, fine. For anything the app leans on, you have just hired yourself as the DBA. Do this when co-location genuinely matters for that specific engine and nothing managed covers it, not as a starting point.

The short version

For an app on Nebius in 2026:

  • Data in the hot path of training or inference, high-QPS retrieval, streaming ingest, goes on Nebius Managed PostgreSQL with pgvector, because being next to the GPUs is the whole reason.
  • Everything else, app state, cache, moderate vector search, search, analytics, goes on Layerbase Cloud. One account, the broad catalog, a free dev tier, and flat pricing: $0 free, $5/mo Solo, $15/mo Pro for the lot, no usage meters.
  • Postgres-only and sold on branching: Neon, assuming the Databricks ownership does not put you off.
  • Want the whole backend bundled: Supabase, if you take the lock-in with it.
  • An engine nobody manages and co-location is non-negotiable: run it yourself on a Nebius VM and own the operations.

The realistic setup is not one database. It is compute on Nebius, the hot-path store sitting next to it, and a wider managed data layer on Layerbase Cloud for everything that does not need to live inside the GPU cluster.

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