Enterprise Pricing

Pricing built around tenants, data scale, and AI consumption.

ZHIMINDS pricing is structured for enterprise rollout. Start with a scoped pilot, then scale by data sources, tenants, workloads, and production service levels.

Pilot

4-8 weeks

Focused scope for source connection, dashboard, and one AI workflow.

Scale

Usage-based

Data volume, tenant count, AI calls, and integration complexity drive the quote.

Enterprise

SLA + Private

Dedicated deployment options for regulated or mission-critical environments.

Packages

The packages below are commercial starting points. Final pricing depends on tenant model, ingestion scope, AI workload, and deployment requirements.

Starter

Data Intelligence Pilot

For teams validating a business case with limited data sources, governed dashboards, and one decision intelligence workflow.

  • Connect 1-3 priority data sources or application systems.
  • Build an initial metric model and management dashboard.
  • Deliver one AI workflow such as report generation, anomaly explanation, or NL2SQL.

Growth

Multi-domain Analytics Rollout

For companies expanding from a pilot to multiple departments, governed metrics, forecasting, and reusable data services.

  • Connect operational databases, files, and streaming data sources.
  • Publish reusable curated datasets and metric services.
  • Add forecasting, scheduled reports, and role-based analytics experiences.

Enterprise

AI Data Platform Production

For mission-critical workloads requiring multi-tenant governance, private deployment options, security controls, and production support.

  • Dedicated architecture for tenant isolation, SLA, monitoring, and audit requirements.
  • Full data lakehouse, AI orchestration, BI portal, and reporting workflows.
  • Security controls across IAM, KMS, WAF, CloudTrail, Secrets Manager, and sensitive-data scanning.

Infrastructure

Blockchain Full-Node SaaS Plan

For customers needing managed full-node runtime, tenant access, indexed chain data, telemetry, and quota management.

  • Pricing varies by chain, node count, storage retention, traffic, and availability targets.
  • Includes monitoring, alerting, access control, and tenant-level quota policies.
  • Can be combined with analytics and AI interpretation for chain-data products.

Operating Model

How quotes are built

We avoid one-size-fits-all pricing because enterprise data platforms vary heavily by integration scope and operating requirements.

01

Scope

Confirm business domains and data sources

We identify systems, data freshness, source ownership, metric scope, and users.

02

Workload

Estimate compute, AI, and storage patterns

We size ingestion, storage retention, BI concurrency, AI calls, and model-routing needs.

03

Delivery

Define rollout, support, and SLA

We align on milestones, deployment model, monitoring, security controls, and support expectations.

Pricing Dimensions

What usually affects the quote

These dimensions make pricing transparent before a formal proposal is created.

Data source and pipeline complexity

Source type, CDC requirements, streaming latency, transformation logic, and data-quality controls.

Tenant and permission model

Number of tenants, role hierarchy, data isolation, audit requirements, and user access patterns.

AI workload volume

NL2SQL usage, report generation frequency, long-context analysis, RAG retrieval, and model routing.

Included Services

What customers typically receive

A production rollout includes more than software access. It includes architecture, governance, integration, and operational readiness.

Solution architecture

Target architecture, AWS service mapping, data flow design, and security control planning.

Implementation support

Pipeline setup, metric modeling, dashboard/report configuration, AI workflow setup, and launch support.

Get a scoped commercial proposal

Share your data sources, priority scenarios, expected users, and deployment constraints. We will respond with a practical rollout and pricing structure.