The right question isn't which platform queries fastest. It's how much of the AI problem it actually solves.

Nexaris doesn't replace your lakehouse investment — it federates intelligence across it. Keep running your existing Databricks jobs while extending AI and governance to the data that can't be moved into the lakehouse: on-premises systems, regulated workloads, SAP, Oracle and air-gapped environments.

Snowflake is excellent for cloud data warehousing, and it is a destination — data has to live inside it. Nexaris lets Snowflake remain part of a broader ecosystem, connecting it in real time to SAP, Oracle, Epic and Salesforce without replication, and governing all of those sources under one policy engine.

Starburst is the best-known federated SQL engine there is and we do not pretend otherwise. The difference is scope. Starburst ends at query consumption — its own on-premises product page describes a “data consumption layer” and makes no mention of model training, model serving, guardrails or unstructured data. With Nexaris, federation is one capability inside five products on one control plane: AI Factory covers experiments, registry, deployment, guardrails and a compliance ledger; Wa-Sul AI Studio covers low-code AI apps and annotation; Data Catalog covers ontology and observability; Guardiuum governs all of them under a single policy engine. A Starburst buyer assembles those from other vendors.

Dell’s storage engineering is excellent and PowerScale and ObjectScale feed GPUs about as well as anything on the market — if you are buying a rack, it is a serious option. Two structural differences are worth knowing. First, AIDP is assembled from four vendors: Dell for storage, compute and lifecycle, Starburst for analytics, Elastic for search and vectors, NVIDIA for acceleration — so governance is per-engine rather than one plane. Second, it assumes Dell hardware. Nexaris is software that runs on the estate you already have, Dell hardware included, and Dell’s own technical overview describes model use as bring-your-own-model with no deployment infrastructure — which is precisely what AI Factory provides.

Cloudera is the incumbent in the regulated data centre and its private-AI push with NVIDIA NIM is real. Two things change. Operationally, Cloudera Runtime packages roughly fifty open-source projects for your team to run; Nexaris is five products with one console. Architecturally, Cloudera is a destination — data lands in the cluster — whereas Nexaris queries sources where they already are, including the Cloudera cluster itself. On timing: Cloudera’s on-premises GenAI inference reached technical preview with Data Services 1.5.5 in August 2025. You do not have to decommission Cloudera to start; most customers connect it as a source.

Yes. Spark engineers still write Spark, dbt developers still use dbt, Tableau analysts still open Tableau. Nexaris sits above your tools and connects what your team builds into a governed, AI-ready layer — your team keeps every skill it already has.

Partner-led platform licensing rather than consumption billing. You are not charged extra for querying data or for egress, so your enterprise data budget is predictable year over year rather than scaling with data volume.

Air-gapped deployment is a first-class mode with full feature parity — no call-home, no cloud control plane and no reduced capability set. See Security & compliance for the architecture detail.

No. Nexaris takes the position that the customer, not the vendor, should be the centre of the architecture. Those systems stay exactly where they are and Nexaris adds the intelligence and governance layer above them. See Switching platforms.