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NuoData‘s leadership team sat down to answer six questions about the platform’s origins, its position alongside Snowflake, Databricks, Microsoft Fabric and Google Cloud, and where they believe enterprise data and AI architecture is headed next. The answers have been lightly edited for length.
NuoData describes itself as an end-to-end enterprise Data, AI and Agentic orchestration platform. What was the gap in the market that convinced you enterprises didn’t need another data platform, but a fundamentally different way of connecting data, AI and automation?
Every major shift in enterprise data infrastructure — warehouse, lake, lakehouse — has repeated the same premise: centralize the data first, and value follows. That premise held up reasonably well when the main consumers of data were dashboards and scheduled reports. It breaks down the moment AI and autonomous agents enter the picture, because an agent doesn’t wait for a migration roadmap to finish. It needs to know what data exists right now, whether it’s allowed to touch it, and how to act on it safely — across a mainframe, a SaaS app, a warehouse and a lakehouse, often within the same task.
The gap we saw wasn’t “enterprises need a better place to store data.” It was that data engineering, analytics, and now AI and agent orchestration had each evolved as separate stacks — separate catalogs, separate access controls, separate ways of moving data — and nobody had built the connective layer underneath that let all three share one governed view of the enterprise. Every AI initiative was re-solving governance and lineage from scratch, on whatever subset of data happened to already be migrated somewhere convenient.
(Credits:NuoData)
None of that makes centralization wrong. Sometimes proximity between data and compute genuinely is what a workload needs, and we built NuoData with a world-class managed Iceberg lakehouse, alongside governed Postgres and MongoDB, for exactly those moments.
What changes is the sequencing: centralization becomes a decision made for a specific reason, on your own timeline, not a prerequisite you have to clear before AI and governance are available at all. NuoData isn’t first and foremost a new place to put data — though it’s a very good one when you choose it. First and foremost, it’s the layer that lets data, AI and automation operate on one policy model and one catalog, wherever the data already lives.
Many enterprises already have investments in platforms like Snowflake, Databricks, Microsoft Fabric and Google Cloud. Where does NuoData fit within that ecosystem, and what are the biggest misconceptions you encounter when explaining your platform to prospective customers?
We don’t ask enterprises to leave Snowflake, Databricks, Fabric or their Google Cloud investment behind — those are exactly the kind of well-run compute and storage engines NuoData is built to sit across, not replace. Halo Connections, our governance and connectivity layer, treats a Databricks lakehouse, a Snowflake warehouse and a legacy Oracle instance as peers: it applies one policy model to all of them, federates queries across them, and lets Cosmo or Nova reason across all of them without anyone moving a byte.
We also don’t stop at federation. For workloads where proximity between data and compute genuinely pays off — high-frequency joins, large model training runs, cost-sensitive batch processing — NuoData includes its own managed Iceberg lakehouse alongside governed Postgres and MongoDB. The point isn’t that we refuse to centralize data; it’s that centralization becomes a workload-by-workload decision made at your own pace, not a blanket migration mandate.
Figures reflect typical customer environments compared to comparable peer platforms; see Q3, Q5 and Q6 for detail (Source: NuoData)
An enterprise gets day-one AI readiness across everything it already has, and full convergence for the specific slice of data where convergence earns its cost — without ever being forced to choose one over the other.
The most common misconception we run into is that offering our own managed lakehouse means we’re competing to become an enterprise’s next Databricks or Snowflake. We’re not. Unity Catalog governs what’s inside Databricks. Snowflake Horizon governs what’s inside Snowflake. Neither reaches the legacy Oracle instance, the five SaaS tools, or, for that matter, the other vendor’s platform sitting right next to it. Our lakehouse exists for convergence when an enterprise chooses it — it’s not a mandatory destination for the governance we deliver everywhere, including inside Databricks and Snowflake themselves.
The second misconception is that adopting NuoData means another migration. It’s the opposite of our philosophy — zero mandatory modernization is a design commitment, not a slogan. We migrate data selectively, into open formats, only when there’s a real technical or economic reason to. Most of an enterprise’s estate never needs to move to become governed and AI-ready.
One of the themes throughout your platform is eliminating complexity — from unified data access to built-in governance and AI orchestration. Which capability do customers typically underestimate until they see it in production?
The one people consistently underestimate is how governance is enforced in Halo. The intuitive assumption is that strong governance — row-level security, column masking, PCI- or HIPAA-grade controls — comes with a query-time performance tax, because that’s how most platforms do it: check the policy on every single query. We made a different architectural choice. Masking and row-level policy are design-time decisions, compiled and enforced once, at the source, when a governed dataset is defined. Runtime is reduced to a single, fast authorization check — is this user or agent entitled to this dataset — with zero masking logic re-evaluated on every query. Customers usually don’t believe that until they see the query plans.
The second is Code Lineage — extending lineage past the data layer into the actual repositories that move it. Most lineage tools stop at “this dashboard reads this table.” Ours can tell you which function, in which file, in which repository, wrote to that table last Tuesday, with what confidence, and whether a pending pull request is about to break it. Enterprises don’t ask for that in a demo, because they don’t know to ask for it — until an impact-analysis query answers a question their existing catalog physically cannot.
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The third is how much of the platform runs without code at all, and how deliberately we resist reaching for AI to do what a deterministic rule already handles well. Maestro’s pipelines and workflows are built visually; Halo’s policies are configured, not coded; ingestion and transformation in Quantum are declarative by default. Generative AI enters only where genuine reasoning is required — inferring a dynamic SQL target, disambiguating an entity, drafting a first-pass mapping — and even then it’s scoped narrowly rather than left to run the whole pipeline.
Customers building their first project usually assume they’ll need a bench of expensive specialists and a meaningful AI token budget just to stand up governed pipelines. In practice, teams typically run with 40 to 50 percent lower operating cost than comparable peer stacks, because the people required to build and maintain the platform don’t need to be as specialized, and AI spend stays a small, predictable line item rather than the backbone of every pipeline.
Your platform includes products like Nova for AI and LLMOps and Nora for agentic AI. There’s no shortage of companies adding AI features today. What do you believe enterprises need beyond simply connecting an LLM to their data?
Connecting a large language model to enterprise data answers one question: can it retrieve something plausible? It says nothing about whether the model only sees what a specific user is entitled to see, whether the answer is grounded in current, resolved data rather than a stale export, whether you can show an auditor exactly what an agent looked at and did six months ago, or what happens to your architecture the day you want to switch models.
That’s the gap between Nova and Nora and “plug an LLM into a vector store.” Nova handles the model side — training, fine-tuning, serving and retrieval pipelines built on governed data, so what the model retrieves is what the catalog says is true, not whatever a disconnected embedding job happened to index. Nora handles the agent side: agents and multi-step workflows that operate through the same governance Halo already enforces, an MCP registry so agents reach tools and systems in a controlled way rather than an ad hoc integration per agent, and human-in-the-loop checkpoints for anything consequential enough to need one.
There’s a second, more architectural answer we don’t always lead with, but probably should: we don’t believe AI belongs in the middle of the pipeline by default. In Nora, and across the platform generally, generative AI sits at the edge — invoked only where a deterministic rule genuinely can’t do the job: resolving an ambiguous reference, reasoning through an edge case, drafting something a human will review.
(Ashish Baghel is the Founder and CEO of NuoData)
Everything that can be handled by a parser, an API call or a declarative rule is handled that way, every time, before an agent is ever asked. That discipline is what keeps token spend a small fraction of total platform cost rather than a runaway line item, and it’s a meaningful part of why compute on NuoData typically runs 20 to 40 percent cheaper than comparable stacks — an enterprise isn’t paying AI-inference prices for work that never needed AI in the first place. And because we don’t marry the platform to one model vendor, that discipline holds no matter which LLM is running behind it; the architecture doesn’t change when the model does.
What enterprises actually need beyond a connected LLM is governance and orchestration wrapped around it, and a design that only spends AI where AI earns its keep — the difference between a demo and a system someone is willing to run, and pay for, in production.
One of your core messages is that organizations shouldn’t have to migrate all of their data before becoming AI-ready. Why has the industry become so focused on data consolidation, and what does NuoData’s approach enable that traditional architectures don’t?
Consolidation became doctrine for a legitimate historical reason: joining data across systems was slow and expensive, so for two decades, “move everything to one warehouse or lake” was the only pragmatic way to get consistent analytics. Vendors then built businesses around that constraint, and over time the constraint hardened into an assumption — that governance and AI-readiness are things you earn only after migration finishes.
For a modern enterprise, migration never finishes. By the time seventy percent of an estate has moved, the business has onboarded three new SaaS platforms and a new streaming source, and the finish line has moved with it. We’ve watched partially-migrated estates become their own category of technical debt — the worst of both worlds, paying for two architectures at once with neither fully governed.
Our approach inverts the dependency without discarding the destination. Halo Connections federates across 100+ modern and legacy ecosystems under one policy model, so day-one AI readiness doesn’t wait on a migration.
And when convergence genuinely pays off for a specific workload, NuoData’s own managed Iceberg lakehouse, alongside governed Postgres and MongoDB, is right there as a world-class place to centralize into — on your schedule, for your reasons, not a vendor’s roadmap. That’s the actual difference: not “never centralize,” but “centralize only when it earns its cost, and only as fast as you choose to.” An enterprise keeps the option to converge data later without having architected around a single proprietary format from day one, and never has to justify a migration budget just to get basic governance.
Looking ahead, what do you think enterprise data platforms will look like three to five years from now? Will organizations continue assembling best-of-breed tools, or are we moving toward unified platforms that manage data, analytics and AI together?
We expect convergence at the governance and orchestration layer, and continued plurality at the execution layer — and we don’t think those are in tension; we think that’s the only architecture that survives contact with agentic AI at scale.
(Shutterstock/NicoElNino)
The best-of-breed era made sense when humans were the ones stitching tools together and could absorb the integration tax themselves. That doesn’t hold once an enterprise has dozens or hundreds of agents operating semi-autonomously across the business — the coordination and governance overhead of a dozen disconnected point tools becomes a compliance and cost risk in its own right, not just an inconvenience. At the same time, we don’t think enterprises are heading back to a single monolithic vendor owning storage, compute and AI end to end. The pace of change in compute engines and language models specifically is too fast for anyone to want that dependency locked in for the next five years.
The resolution is an open control plane — one catalog, one policy model, one orchestration layer — that lets the execution underneath keep changing: today’s LLM, tomorrow’s cheaper or better one; today’s query engine, tomorrow’s faster one. We’d also expect real pressure on both halves of the cost equation. On the compute side, consumption-based pricing tied to a single proprietary engine is going to look expensive next to architectures that push work down to open, commoditized compute — a meaningful part of why NuoData environments typically run 20 to 40 percent cheaper than comparable stacks.
On the AI side, the platforms that treat generative AI as the default way to do everything will find token costs scaling with agent activity in ways no CFO signed up for; the ones that treat AI as a capability invoked deliberately, at the edges, will not. That combination — open control plane, deterministic-first execution, AI used only where it earns its keep — is the platform pattern we built NuoData to be, well before “agentic” became the word everyone reached for.
NuoData is the Unified Open Data & AI Platform™ — governed data and AI across 100+ modern and legacy ecosystems, with a world-class managed Iceberg lakehouse, Postgres and MongoDB available whenever convergence is the right call. Any source. Any LLM. Any agent. Any infrastructure.
Editor’s note: The responses above reflect NuoData’s own views and descriptions of its platform and capabilities.
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