Supernova vs xPrivo: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Supernova and xPrivo — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Supernova

Paid

An encrypted Iceberg data lake with a built-in engine and MCP endpoint, so Claude and Codex can query every tool your company uses.

MCP Endpoint for Claude and Codex: Point any MCP-speaking assistant at mcp.supernova.ai/mcp and every synced table becomes queryable in natural language.
Encrypted Iceberg Lake: Open Apache Iceberg tables in object storage with table-level encryption, so the data stays in a portable open format you control.
Zero-Copy Connections: Any engine that speaks Iceberg can read the lake directly, avoiding a second copy of your warehouse.
Time Travel: Every table retains version history, so you can query the state of your data as of any earlier point.
Built-In Frontier Models: Ask a question or describe a dashboard in plain language and Supernova generates the models and visualisations without a data team.
TypeSQL: Schema-aware SQL that autocompletes across joins and type-checks before execution, catching errors the way a typed language would.
Single-Binary CLI: One command-line tool connects sources, runs queries, tails live table changes and registers the MCP endpoint with Claude Desktop, from a laptop or CI.
Git-Backed Dashboards: Models and dashboards are readable and writable through Git, putting analytics artefacts under normal version control.

Conversational Revenue Analysis: Ask Claude which customers churned last quarter and why, with the answer computed over live Stripe and HubSpot tables.
Warehouse Cost Reduction: Replace a multi-vendor pipeline-plus-warehouse stack with one usage-billed platform, which the vendor illustrates as $5,640/mo dropping to $540/mo for a hardware company.
Dashboards Without a Data Team: Describe the dashboard you want in a sentence and have the models and charts generated for you.
AI-Native Data Access Layer: Give internal agents a governed, encrypted single endpoint for company data instead of per-tool API integrations.
Auditing Historical State: Use table version history to reconstruct what the numbers looked like before a pricing or schema change.
CI-Driven Data Workflows: Drive connections, queries and change tailing from pipelines using the single CLI binary.

Freemium

Privacy-first, open-source anonymous AI chat assistant that can be used hosted or run locally with no tracking.

Anonymous Usage: Enables conversations without account creation so users can interact with the assistant without providing identity-linked information.
No Tracking & Data Protection: Designed to avoid telemetry and tracking, with a focus on keeping user inputs private and not logged by default.
Open-Source Codebase: Publicly available source code for inspection, modification, and self-hosting, enabling transparency and auditability.
Local / Self-Hosted Deployment: Ready-to-run locally so organizations or individuals can host their own instance and retain full control over data and infrastructure.
Hosted Web Option: Provides a hosted website instance for users who prefer not to self-host while maintaining core privacy promises.
Freemium Model with PRO Tier: Core functionality is free and open-source while a paid PRO subscription is available for users seeking premium or hosted conveniences.
Anonymous usage without account creation
Open-source codebase (can be inspected and self-hosted)
Option to run locally for enhanced privacy
Hosted web interface available for convenience
Privacy-first design with no tracking and data protection
Free core offering with optional paid PRO tier
No public API or integration details disclosed in provided content

Private Personal Assistant: Individuals who want conversational AI for personal research or drafting without creating accounts or exposing queries to third parties.
Self-Hosted Enterprise Chatbot: Teams that require an internal assistant but must keep all data on-premises or within a private cloud for compliance.
Journalist & Researcher Workflows: Professionals researching sensitive topics who need anonymity and assurance that queries are not tracked or logged.
Educational Deployments: Schools or instructors deploying chat assistants locally for classroom use where student data must remain private.
Open-Source Development & Customization: Developers who want to fork or extend a transparent chat assistant to integrate custom LLM backends or business logic.
Privacy-Focused Public Access: Operators offering a public chat endpoint that respects user anonymity and avoids collecting personal data.
Private one-on-one conversational assistant without account or tracking
Local/self-hosted deployments for sensitive or regulated data
Users seeking an open-source alternative to commercial chatbots
Developers or researchers wanting to run or inspect assistant code locally
Individuals or teams requiring a simple hosted chat option with privacy guarantees