On-Premise vs. Private Cloud vs. SaaS AI Data Analysts

This is primarily a boundary and operating-model decision. The model is only one component. Documents, database connections, identity, telemetry, updates, backups, audit records, and support access all cross—or stay inside—the chosen boundary.

What is an on-premise AI data analyst?

An on-premise AI data analyst is a natural-language analytics system installed within an organization’s own infrastructure. It can connect to approved documents and databases while the organization controls the network, identity integration, model-serving environment, retention, and upgrade windows defined for that deployment.

“On-premise” should not be treated as shorthand for “secure.” Security depends on architecture and operation: least-privilege data access, user scope, secrets handling, patching, logging, model endpoints, backups, and administrative controls still matter.

DataFact publicly offers SaaS, private-cloud, on-premise, and scoped isolated-network routes. Its pricing page says the enterprise route can run in a customer’s data centre or private-cloud tenancy, integrate with SSO or LDAP, and use model serving on the customer’s own hardware. Exact environment, integration, retention, and support terms are scoped per project.

Four deployment models

Model	Operational boundary	Typical reason to choose it	Main trade-off to examine
SaaS	Vendor-operated service	Fast pilot, no platform infrastructure to run	Data-processing, residency, and integration requirements
Private cloud	Dedicated customer cloud tenancy or boundary	Cloud controls with stronger isolation	Shared responsibility and integration complexity
On-premise	Customer-operated data centre or infrastructure	Data or model serving must stay inside the network	Upgrade, capacity, backup, and support ownership
Isolated network	Restricted or no outbound connectivity	Highly constrained environments	Offline dependencies, updates, support, and model lifecycle

These are category-level descriptions. A vendor contract and architecture review must define the actual boundary.

When SaaS is the practical default

SaaS is usually the shortest path to a pilot when policy allows the relevant data and service providers. The vendor operates the platform, releases updates, and provides the service boundary.

DataFact describes its SaaS route as subscription-based with no customer infrastructure to install. Its Starter tier is positioned for one workspace and one schema, while Business adds multiple workspaces, governance, identity-related capabilities, an embeddable widget, and an open API. Check current plan details at purchase time because packaging can change.

Choose SaaS when:

• the data classification permits the service;
• residency and subprocessors meet policy;
• identity and network integration are sufficient;
• a managed update cadence is acceptable;
• the team wants to validate real questions before owning infrastructure.

When private cloud fits better

Private cloud can be appropriate when the organization needs the platform within its own cloud tenancy or network boundary but does not require physical on-premise infrastructure.

Questions to resolve include:

• Which party provisions and operates compute, storage, and networking?
• Where do model endpoints run?
• How are database routes and document stores exposed?
• Which logs or support artifacts can leave the tenancy?
• Who owns patching, backups, recovery, and monitoring?
• How does identity connect to workspace, team, and data roles?

Do not infer the answers from the label “private cloud.” Put them in the architecture and responsibility matrix.

When on-premise is required

On-premise becomes the leading option when policy, regulation, latency, existing integration, or risk review requires the product and model-serving path to remain in customer-operated infrastructure.

It is a good fit only if the organization can operate the stack. Capacity planning, GPU or model infrastructure, observability, upgrades, high availability, incident response, and support access become part of the product experience.

DataFact states that on-premise deployments can integrate with an existing identity provider and can run model serving on customer hardware. It also states that licence terms and upgrade windows are scoped for that route. Verify supported identity patterns, model providers, sizing, and service levels in writing.

Isolated networks are a separate design problem

A network with limited or no outbound connectivity affects more than model calls. Consider package and model delivery, licence checks, time synchronization, certificate rotation, vulnerability updates, telemetry, remote support, and artifact export.

DataFact’s customers page says environments with restricted or no outbound connectivity are scoped per project. That is not a universal promise of air-gapped operation for every configuration. Bring the exact constraints to the vendor early.

The deployment checklist

Data boundary

• Where are uploaded documents stored and backed up?
• Does the system copy warehouse data or query the source in place?
• Which metadata, logs, and generated files are retained?
• Can administrators set retention and deletion rules?
• Which regions and disaster-recovery locations apply?

DataFact states that it connects to existing documents and databases without replacing the warehouse. Confirm whether any query results, extracted chunks, indexes, caches, or generated artifacts are persisted in the chosen deployment.

Model boundary

• Which models are supported?
• Where does inference run?
• Can prompts, retrieved context, or outputs leave the environment?
• Are model logs retained?
• How are model versions approved and changed?

Database boundary

• Which credentials does the platform use?
• Is execution read-only?
• How are schemas, tables, rows, and result sizes scoped?
• What network path reaches the database?
• How are secrets rotated and audited?

DataFact’s database reference notes that connection types and source modes are deployment-dependent. A listed database type still must pass a connection test and return schemas; network access, credentials, and database permissions remain external prerequisites.

Identity and authorization

• Does the deployment integrate with the required identity provider?
• How do organization, workspace, team, and user scopes interact?
• How are table and row rules mapped to business roles?
• Are administrator and end-user actions logged?
• What happens when access is revoked?

Operations

• Who monitors queues, parsing, indexing, and model services?
• Who owns upgrades and compatibility testing?
• What is the backup and restore plan?
• How are security fixes delivered to restricted environments?
• What diagnostics can support staff access?
• What are the recovery objectives and escalation path?

How to compare proposals fairly

Run the same workload and role matrix in each viable environment. Include one document collection, one database schema, representative hard questions, different user scopes, and an update or failure scenario.

Compare:

1. time to a working, governed pilot;
2. answer and evidence quality;
3. control over data and model boundaries;
4. integration and identity effort;
5. ongoing platform and infrastructure work;
6. upgrade and change-control process;
7. total cost over the expected usage period.

Do not compare only licence price. The customer engineering time required to operate a private deployment is part of its cost.

DataFact deployment options

DataFact says the core platform, governance model, and API are available across its SaaS and private deployment routes, allowing a team to evaluate on subscription and later move to a private environment without rebuilding its workspace. Treat migration mechanics, supported versions, downtime, and commercial terms as items to confirm for your project.

The public site does not publish a universal certification list, reference architecture, or fixed price for private deployments. This guide therefore makes no certification or compliance claim. Request the current security package and deployment design during evaluation.

Frequently asked questions

Is private cloud the same as on-premise?

No. Private cloud usually runs in a dedicated cloud tenancy or network boundary; on-premise runs in customer-operated infrastructure such as a data centre. The responsibility split may also differ.

Does on-premise mean no external model calls?

Not automatically. The model endpoint and network path must be defined explicitly. DataFact says model serving can run on customer hardware for private deployments; confirm the selected model and all outbound dependencies.

Can we start with SaaS and move on-premise later?

DataFact says teams can evaluate by subscription and move to private deployment while keeping the same product and governance model. Confirm workspace migration, integrations, data transfer, versions, and contractual scope before treating that as a committed plan.

Which deployment is most secure?

No label determines security. The right choice is the environment where the organization can enforce and operate the necessary data, identity, network, model, logging, and update controls.

Does DataFact support air-gapped deployments?

The public site says restricted or no-outbound environments are scoped per project. Ask DataFact to validate your exact definition of air-gapped and document every dependency; do not assume standard support.

Make the boundary explicit

The deployment decision becomes easier when every team can point to the same diagram and say where documents, queries, models, logs, identities, and support access live.

Review DataFact deployment and pricing options, then use How to Evaluate an Enterprise AI Data Analyst to design a workload-based pilot.