A self hosted BI tool runs the entire analytics stack inside your own infrastructure, your VPC, your private cloud, or your on-premises servers, so the data never leaves your security perimeter. Analytify is an open-source self hosted BI tool built for product, data, and engineering teams that need full control over their data, deployment, and roadmap, without giving up modern dashboards, multi-tenant security, or generative-AI features.
Open-source. Deployed on your VPC, private cloud, or on-prem. SOC 2-aligned, GDPR-ready, GenBI built in.
Drag-and-drop dashboard builder with 30+ chart types, drilldowns, cross-filters, exports, and scheduled email or Slack delivery. The interface looks and behaves like a 2026 SaaS product, not a 2014 enterprise BI tool. Non-technical users can build and share dashboards in their first sitting.
Visual query builder for analysts who do not want to write SQL, plus a full-featured SQL editor with autocomplete, schema browser, query history, and version control for analysts who do. Both modes share the same semantic layer, so metric definitions stay consistent.
Powered by GenieAIQ, our generative-AI engine. End users type natural-language questions and Analytify produces the chart. Bring your own LLM (Claude, OpenAI, open-source Llama), point it at your governed semantic layer, and keep both the prompts and the data inside your network. This is GenBI without the data-leaving-your-VPC tradeoff most cloud BI vendors force you into.
Analytify enforces row-level security at query time, driven by signed JWT or session tokens passed from your application. Customer A cannot see Customer B’s data even if they tamper with the URL. Object-level permissions, role-based access control, and per-team workspaces are first-class, not paid add-ons.
Native connectors for Snowflake, BigQuery, Amazon Redshift, Databricks, Apache Iceberg, ClickHouse, Apache Pinot, DuckDB, PostgreSQL, MySQL, Microsoft SQL Server, MongoDB, plus custom REST and webhook ingestion. A semantic layer sits on top so every chart agrees on what “monthly recurring revenue” or “active user” means.
Need to ship analytics inside your own product, not just internal dashboards? See our customer-facing analytics platform page for embedding via iframe, JavaScript SDK, and React or Vue components, all with the same multi-tenant security model.
See Analytify on your stack. Book a 30-minute demo.
Three deployment patterns, ranked by time to first dashboard:
The runtime needs Postgres for metadata, Redis for caching and queues, and your warehouse for the actual data. That is the entire stack. No proprietary licence server, no hidden cloud dependency, no telemetry beacon you have to firewall off.
| Self hosted BI | Managed cloud BI | |
|---|---|---|
| Data location | Your VPC, private cloud, or on-prem | Vendor’s cloud |
| Compliance papering | Your existing controls cover it | Re-papered with each vendor renewal |
| Pricing model | Open-source licence + infra cost | Per-seat or per-application licence fees |
| Customisation | Full source access, fork-friendly | Limited to vendor-exposed config |
| Time to first chart | 1 hour (Docker) to 2 weeks (production K8s) | 10 minutes (sign up) to 2 days (data wired) |
| Maintenance burden | You own upgrades, monitoring, scaling | Vendor handles it |
| AI / GenBI privacy | Bring your own LLM, prompts stay on your network | Prompts and data leave your perimeter |
| Best for | Regulated industries, large enterprises, security-first teams | Small teams, rapid POCs, no DevOps capacity |
Most mid-market and enterprise teams end up running both: managed cloud for fast experiments, self hosted for production analytics on regulated data.
The most common alternatives teams evaluate alongside Analytify are Metabase, Apache Superset, and Redash.
Analytify sits between the open-source basics and the commercial heavyweights: production-ready embedded analytics, GenBI included by default, and an open-source core that lets you self host without a per-user licence.
Honest answer: self hosted BI is not free. The licence is, the engineering hours are not. Teams typically spend 0.25 to 1.0 engineer-equivalents on the analytics stack in steady state, depending on scale and data volume. That covers upgrades (release cadence is roughly monthly), database backups, capacity tuning, monitoring, and integrating new data sources.
That is still the cheaper path than commercial per-seat BI for most teams above 50 end users, and significantly cheaper than building analytics in-house. But it is real engineering work and worth being honest about. If you cannot fund 0.25 of an engineer on infrastructure, the managed cloud version is a better starting point.
Three paths, depending on how much of the operations work you want to own:
Book a demo and we will walk through pricing scoped to your team size, data sources, and deployment model.
What is a self hosted BI tool?
A self hosted BI tool is a business intelligence platform you deploy and operate inside your own infrastructure rather than consuming as a third-party SaaS service. Your data never leaves your network, your security perimeter, or your compliance scope.
Why pick a self hosted analytics platform over managed cloud BI?
Three reasons drive most decisions: data sovereignty (regulated industries and EU customers), predictable cost (open-source licence + infrastructure beats per-seat fees at scale), and customisation (open source means you can fork and modify the platform).
How long does it take to deploy Analytify self hosted?
Docker Compose for proof-of-concept: under an hour. Production Kubernetes deployment with backups, monitoring, and SSO: 1 to 2 weeks. Air-gapped on-prem deployment: 3 to 6 weeks depending on existing infrastructure.
Is Analytify HIPAA, SOC 2, and GDPR compliant?
The platform is architected to be SOC 2-aligned and GDPR-ready out of the box. HIPAA compliance is achievable when Analytify is deployed inside a HIPAA-covered environment with a signed Business Associate Agreement. Compliance ultimately rests on the deployment, not just the software.
Can I run GenBI features on a self hosted deployment?
Yes. Bring your own LLM (Claude, OpenAI, Anthropic, open-source Llama or Mistral), point GenieAIQ at your governed semantic layer, and keep both prompts and data inside your network. This is the differentiator most cloud BI vendors cannot match.
Which databases and warehouses does Analytify connect to?
Snowflake, BigQuery, Redshift, Databricks, Apache Iceberg, ClickHouse, Apache Pinot, DuckDB, PostgreSQL, MySQL, Microsoft SQL Server, MongoDB, plus custom REST and webhook ingestion.
How does Analytify compare to Metabase or Apache Superset?
Metabase is lighter and easier for small teams; row-level security and embedding require Metabase Pro. Apache Superset is the most powerful open-source BI platform for very large data teams; it is also the most operationally heavy. Analytify sits in between: production-ready embedded analytics and GenBI in the open-source core, without the operational overhead of Superset or the paywalled features of Metabase.
What does it actually cost to self host?
Open-source licence is free. Year-1 infrastructure plus engineering for a typical mid-market deployment runs $5,000 to $40,000. For comparison, a 50-seat managed cloud BI deployment is $30,000 to $150,000+ per year.
Do I need a DevOps team to run Analytify?
For Docker Compose proof-of-concept, no. For production Kubernetes deployment, you need someone comfortable with Helm, Postgres administration, and basic monitoring. We provide deployment-assistance services if you would rather have us run it.
Most BI demos are scripted on someone else’s data inside someone else’s cloud. Book a 30-minute call and we will walk through Analytify with the deployment model, data sources, and security requirements your team needs.