Apache Superset vs Power BI for Australian Enterprises (2026)
TL;DR — At a Glance
| Factor | Apache Superset | Microsoft Power BI |
|---|---|---|
| Licensing cost | Free (open source) | AU$15.70/user/mo (Pro), AU$31.50/user/mo (Premium per-user), Premium capacity from ~AU$7,800/mo |
| Year-1 TCO, 100 users | ~AU$200k (infra + 1 FTE) | ~AU$235k (Premium per-user + services) or ~AU$140k (Pro only) |
| Data residency in AU | Full control — any AU AWS / Azure / on-prem region | Limited — Microsoft 365 tenant geography rules; not all Power BI features are AU-region-resident |
| Microsoft ecosystem fit | Indirect (works with Excel, Snowflake, etc.) | Native (Teams, SharePoint, Excel, Fabric, Synapse) |
| Semantic layer | External (dbt / Cube) | Built-in (DAX + Tabular models) |
| Vendor lock-in | None — portable SQL + YAML | High — DAX, .pbix proprietary format, Fabric tie-in |
| Best for | Cost-sensitive, AU-data-residency, multi-cloud, embedded analytics | All-Microsoft shops, Excel-heavy analyst teams, regulated industries already on M365 |
The short answer: if your business already runs everything on Microsoft 365 and your analysts live in Excel, Power BI is the path of least resistance — but you’re locking into Microsoft’s data stack (and increasingly Microsoft Fabric) for the long run. If you have data-engineering capacity and care about cost predictability, sovereignty, or vendor portability, Apache Superset wins comfortably above ~50 analytics users.
Australian context: for APRA-regulated, AUSTRAC-reporting, or sovereign-data-sensitive workloads, Microsoft’s tenant geography rules mean parts of Power BI workloads can still touch non-AU regions. Self-hosted Superset on an AU AWS or Azure region puts the data residency question entirely under your control. PADISO’s $50K D23.io / Apache Superset consulting engagement takes a typical AU mid-market rollout from zero to production in 6 weeks.
Table of Contents
- Executive Summary
- The Core Difference: Hosted vs Self-Hosted
- Cost Comparison for Australian Enterprises
- Data Sovereignty and Compliance
- Feature Comparison: What You Actually Need
- Implementation Timeline and Complexity
- Security, Audit-Readiness, and Vanta Integration
- Real-World Australian Use Cases
- When to Choose Superset, When to Choose Power BI
- Migration and Integration Considerations
- Next Steps: Making Your Decision
Executive Summary
If you’re running a mid-market or enterprise operation in Australia, you’ve likely encountered the Business Intelligence (BI) decision: Microsoft Power BI or Apache Superset. This isn’t a simple feature comparison. It’s a strategic choice about cost, data residency, compliance, and how much control you want over your analytics stack.
Power BI wins on ease of use and Microsoft ecosystem integration. Superset wins on cost, data sovereignty, and control. For Australian enterprises bound by local data regulations, Superset deployed via D23.io or similar Australian-hosted infrastructure can be the difference between a $500k annual bill and a $50k bill—plus the ability to keep customer data on Australian soil.
This guide cuts through the marketing and gives you the numbers, trade-offs, and decision framework you need.
The Core Difference: Hosted vs Self-Hosted
Understanding the fundamental architecture difference is essential before diving into features and costs.
Power BI: Microsoft-Hosted SaaS
Power BI is a fully managed cloud service. You connect your data sources, build dashboards in Power BI Desktop or the web interface, and Microsoft handles infrastructure, updates, security patches, and scaling. Your data flows through Microsoft’s cloud (typically US or EU regions, with limited Australia-specific options).
This model is fast to deploy. A team can go from zero to dashboards in weeks. But you’re paying for convenience, and your data residency options are constrained.
Apache Superset: Open-Source, Self-Hosted
Superset is an open-source BI platform you deploy on your own infrastructure—whether that’s an Australian AWS region, a private data centre, or a managed provider like D23.io. You own the deployment, manage updates, and control where data sits.
This requires more upfront engineering work. But once running, your cost scales linearly with infrastructure, not with user count or query volume. And your data never leaves Australia.
For Australian enterprises, this distinction matters. Data sovereignty is not theoretical—it’s regulatory. If you’re handling customer data under Australian Consumer Law or Privacy Act obligations, keeping data on Australian servers isn’t optional.
Cost Comparison for Australian Enterprises
Let’s talk real numbers. Cost is often the deciding factor, and the gap is substantial.
Power BI Pricing Model
Microsoft charges per user, per month:
- Power BI Pro: AUD $15–20/user/month (requires individual licenses)
- Power BI Premium: AUD $5,000–10,000/month (capacity-based, supports unlimited Pro users)
- Data Gateway: Included, but premium features cost extra
- Embedded analytics: Additional licensing tiers
For a 100-person organisation with 50 active BI users and Premium capacity, you’re looking at AUD $7,000–12,000/month, or AUD $84,000–144,000 annually.
Add data integration tools (Power Automate, Azure Data Factory), and you’re easily at AUD $150,000+/year.
Apache Superset Pricing Model
Superset itself is free (open-source). You pay for infrastructure:
- Managed hosting (e.g., D23.io, Preset): AUD $500–2,000/month depending on query volume and users
- Self-hosted on AWS/Azure (AU region): AUD $300–1,000/month for typical workloads
- Engineering time to set up, integrate data sources, and manage: 4–8 weeks initial, then 10–20 hours/month maintenance
A fully deployed Superset instance with managed hosting costs AUD $8,000–15,000 annually. Self-hosted is often cheaper.
The Cost Gap
For a mid-market Australian enterprise, switching from Power BI to Superset can cut BI costs by 70–85%. A company paying AUD $120,000/year on Power BI might spend AUD $12,000/year on Superset infrastructure plus AUD $20,000/year in engineering support.
That’s a AUD $88,000 annual saving—enough to hire a dedicated data engineer.
However, this assumes you have or can hire engineering capacity. If you don’t, the engineering cost can offset savings.
Data Sovereignty and Compliance
This is where Australian enterprises often find their real reason to choose Superset.
Power BI and Australian Data Residency
Microsoft Power BI operates primarily from US and EU data centres. While Microsoft offers “Australia East” region storage, data can still transit through US infrastructure for processing and caching. Microsoft’s data residency commitments are complex and depend on your subscription tier.
For enterprises handling:
- Customer personal data (Privacy Act)
- Health information (Privacy Act + state legislation)
- Financial records (AML/CFT Act)
- Government contracts (Protective Security Policy Framework)
…keeping data in Power BI’s US-based systems creates compliance friction. You may need Data Processing Agreements (DPAs), Privacy Impact Assessments (PIAs), and vendor audits. Some government contracts explicitly prohibit it.
Apache Superset and Australian Data Residency
When deployed via Australian infrastructure—AWS ap-southeast-2 (Sydney), Azure Australia East, or an Australian-hosted provider like D23.io—Superset keeps data entirely on Australian soil. Your data never leaves the country.
This eliminates:
- Cross-border data transfer compliance questions
- US CLOUD Act exposure
- Vendor lock-in to Microsoft’s data centre choices
- DPA complexity with US entities
For regulated industries (financial services, healthcare, government), this is often the decisive factor. An Australian bank or health provider using Superset on Australian infrastructure has a much cleaner compliance story.
Audit-Readiness: Superset + Vanta
If you’re pursuing SOC 2 Type II or ISO 27001 compliance, data residency is just one piece. You also need audit-ready infrastructure.
Superset deployments can integrate with Vanta (a compliance automation platform) to continuously monitor security controls, generate audit evidence, and track compliance status. This is particularly valuable for Australian enterprises building toward SOC 2 or ISO 27001 certification.
Power BI also supports Vanta integration, but the compliance burden is shared with Microsoft. Superset gives you full control of the compliance stack.
Feature Comparison: What You Actually Need
Both tools do the same core job: connect to data, build dashboards, share insights. But the details matter.
Power BI Strengths
Ease of use: Power BI Desktop is intuitive. Non-technical users can build dashboards. The learning curve is shallow.
Microsoft ecosystem integration: If you’re already on Office 365, Azure, Dynamics 365, or Teams, Power BI is native. Data flows seamlessly. Sharing and collaboration happen within familiar tools.
Advanced analytics: Power BI includes R/Python integration, forecasting, and machine learning capabilities built in. For data scientists, this is valuable.
Mobile experience: Power BI mobile apps are polished and responsive.
Vendor support: Microsoft provides 24/7 support, documentation, and a large community.
Apache Superset Strengths
Cost and scalability: Superset scales with infrastructure, not users. 10 users or 1,000 users cost the same to run.
Flexibility: Superset is open-source. You can fork it, modify it, and integrate it into your tech stack. No vendor lock-in.
Data sovereignty: Deploy on Australian infrastructure. Data stays local.
SQL-first approach: Superset is built for analysts and engineers who write SQL. If your team speaks SQL, Superset feels natural.
Embedded analytics: Superset integrates into custom applications more easily than Power BI. If you’re building a product with embedded dashboards, Superset is simpler.
Feature Parity
For standard BI work—dashboards, drill-downs, filters, drill-through reports—both tools are feature-complete. The gap widens only in advanced analytics (Power BI) or customisation (Superset).
For most Australian enterprises, the feature difference is negligible. The decision is cost, compliance, and team skills.
Implementation Timeline and Complexity
Power BI Implementation
Timeline: 4–8 weeks for a basic deployment.
Effort: Low to moderate. You need:
- Power BI Premium capacity (or Pro licenses)
- Data source connections (SQL Server, Azure Data Lake, etc.)
- Desktop-to-cloud publishing workflow
- User training
Complexity: Mostly configuration. Limited custom development.
Risks: Licensing sprawl, user adoption, data refresh latency at scale.
Apache Superset Implementation
Timeline: 6–12 weeks for a production deployment, depending on infrastructure choices.
Effort: Moderate to high. You need:
- Infrastructure provisioning (AWS, Azure, or managed host)
- Superset deployment and configuration
- Database connection setup
- Custom dashboard development
- User authentication (LDAP, OAuth, SAML)
- Monitoring and alerting
Complexity: More technical. Requires DevOps and data engineering skills.
Risks: Infrastructure management, ongoing maintenance, team skill gaps.
Which Is Faster?
Power BI is faster to initial dashboards. Superset is faster to stable, scalable operations. If you need dashboards in 4 weeks, Power BI wins. If you need a BI platform that won’t cost AUD $150k/year in 2 years, Superset wins.
For Australian enterprises, consulting with an AI agency experienced in platform engineering can compress both timelines significantly. PADISO, for example, has deployed Superset instances for Australian enterprises in 6–8 weeks, including infrastructure, security hardening, and Vanta integration.
Security, Audit-Readiness, and Vanta Integration
Security is non-negotiable for Australian enterprises. Let’s compare how each platform handles it.
Power BI Security Model
Power BI inherits Microsoft’s security infrastructure:
- Encryption: Data at rest and in transit (TLS 1.2+)
- Access control: Role-based access control (RBAC) and row-level security (RLS)
- Audit logging: Azure audit logs track access and changes
- Compliance certifications: SOC 2, ISO 27001, HIPAA, etc. (Microsoft-level)
Limitation: You’re auditing Microsoft’s controls, not your own. For SOC 2 Type II, you inherit Microsoft’s attestation but can’t audit the underlying infrastructure.
Apache Superset Security Model
Superset security depends on your deployment:
- Encryption: You configure TLS, database encryption, and secret management
- Access control: RBAC and RLS built in; LDAP/OAuth/SAML integration
- Audit logging: You own the logs (stored in your infrastructure)
- Compliance: You control the audit trail
Advantage: You can build SOC 2 Type II compliance because you own and control every layer. Vanta can monitor your Superset deployment directly, generating audit evidence continuously.
Vanta Integration for Compliance
Both Power BI and Superset can integrate with Vanta, but the benefit differs:
Power BI + Vanta: Vanta monitors Microsoft’s controls (via APIs) and your Power BI configuration. Useful, but Microsoft’s infrastructure is a black box.
Superset + Vanta: Vanta monitors your entire stack—infrastructure, application, database, access logs. You get complete visibility and can generate SOC 2 evidence directly from your deployment. This is particularly valuable for Australian enterprises building toward compliance.
If audit-readiness and compliance automation are priorities, Superset + Vanta is the stronger choice.
Real-World Australian Use Cases
Case 1: Financial Services (Mid-Market Bank)
Requirement: Real-time fraud detection dashboards, customer analytics, regulatory reporting. Data must stay in Australia.
Power BI: Requires DPA with Microsoft, cross-border data transfer risk, and limited audit control. Cost: AUD $120k/year.
Superset: Deployed on AWS ap-southeast-2 (Sydney), fully integrated with Vanta for SOC 2 readiness. Cost: AUD $15k/year infrastructure + AUD $30k/year engineering support = AUD $45k/year.
Winner: Superset. Cost, compliance, and data residency align perfectly.
Case 2: SaaS Startup (Series A)
Requirement: Embedded analytics for customers, internal dashboards, quick iteration on metrics.
Power BI: Requires Premium capacity for embedded analytics, expensive licensing, not designed for product embedding. Cost: AUD $80k/year.
Superset: Embedded dashboards via APIs, open-source customisation, scales with customer base. Cost: AUD $10k/year infrastructure + AUD $20k/year engineering = AUD $30k/year.
Winner: Superset. Better for embedded use cases, lower cost, faster iteration.
Case 3: Government Agency (Large Enterprise)
Requirement: Compliance with Protective Security Policy Framework (PSPF), data sovereignty, audit trails.
Power BI: US data centre exposure, limited audit control, DPA complexity. Cost: AUD $200k/year.
Superset: Deployed on Australian government-approved infrastructure (e.g., AWS GovCloud AU), full audit control, Vanta integration for compliance. Cost: AUD $40k/year infrastructure + AUD $50k/year managed services = AUD $90k/year.
Winner: Superset. Government contracts often mandate local data residency and full audit control.
Case 4: Healthcare Provider (Multi-Site)
Requirement: Patient analytics, privacy compliance (Privacy Act), data sensitivity.
Power BI: Data crosses US borders, Privacy Impact Assessment required, ongoing vendor audit burden. Cost: AUD $150k/year.
Superset: Australian deployment, Privacy Act-compliant data handling, integrated with Vanta for healthcare-grade compliance. Cost: AUD $20k/year infrastructure + AUD $40k/year managed services = AUD $60k/year.
Winner: Superset. Privacy and data sensitivity drive the decision.
For detailed case studies on how Australian enterprises are transforming with modern data platforms, see PADISO’s case studies which document real implementations and ROI outcomes.
When to Choose Superset, When to Choose Power BI
Choose Power BI If:
- You’re already deep in Microsoft: Office 365, Azure, Dynamics 365. Power BI integrates seamlessly.
- Speed to dashboards is critical: You need BI live in 4 weeks, not 8.
- Your team isn’t technical: Non-technical business users will build dashboards.
- You have unlimited budget: Cost isn’t a constraint.
- Data residency isn’t a concern: Your data is non-sensitive or you’re comfortable with US-based processing.
- You need advanced analytics: R/Python integration, forecasting, machine learning built in.
- Vendor support matters: You want Microsoft’s 24/7 support and SLA guarantees.
Choose Apache Superset If:
- Cost is a priority: You need to cut BI spend by 70%+ and reinvest in data talent.
- Data sovereignty is mandatory: Australian data residency is a hard requirement.
- Compliance is central: You’re building toward SOC 2 or ISO 27001 and need full audit control.
- You have engineering capacity: Your team includes data engineers or you can hire them.
- Embedded analytics matter: You’re building a product with dashboards inside it.
- You want no vendor lock-in: Open-source flexibility and portability matter.
- You’re scaling rapidly: Cost per user is zero, so 10 or 10,000 users costs the same.
- Customisation is important: You need to fork, extend, or integrate Superset into a custom stack.
The Hybrid Approach
Some Australian enterprises use both: Power BI for business users and quick dashboards, Superset for regulated data, embedded analytics, and compliance-critical workloads. This isn’t wasteful if it aligns with your architecture.
Migration and Integration Considerations
If you’re moving from Power BI to Superset (or vice versa), plan carefully.
Power BI to Superset Migration
What carries over: Your data sources, SQL queries, and dashboard logic. Most of the work is rebuilding the UI.
What doesn’t: Power BI’s advanced analytics (R/Python), some formatting options, and embedded licensing models.
Timeline: 8–12 weeks for a full migration of a mature Power BI deployment.
Cost: AUD $30k–60k in consulting and engineering time, plus infrastructure setup.
When to do it: When cost savings exceed migration cost (usually within 6–12 months for mid-market enterprises).
Superset to Power BI Migration
What carries over: SQL queries, data sources, and dashboard structure.
What doesn’t: Custom Superset extensions, embedded integrations, and deployment-specific configurations.
Timeline: 6–10 weeks.
Cost: AUD $20k–40k in consulting.
When to do it: Rarely. Most migrations are Power BI → Superset, not the reverse.
Integration with Existing Tools
Both Superset and Power BI integrate with:
- Data warehouses: Snowflake, BigQuery, Redshift, Azure Synapse
- ETL tools: dbt, Fivetran, Stitch, Apache Airflow
- Collaboration: Slack, Teams, email
- Authentication: LDAP, OAuth, SAML
Superset integrates more easily with open-source stacks (Airflow, dbt, Kafka). Power BI integrates more easily with Microsoft stacks (Azure, Dynamics, Teams).
For Australian enterprises modernising their data stack, partnering with a platform engineering specialist can ensure integration decisions align with your long-term architecture.
Making the Decision: A Framework
Here’s a simple decision tree:
Question 1: Is data sovereignty mandatory?
- Yes → Superset
- No → Continue
Question 2: Is cost a primary driver?
- Yes → Superset
- No → Continue
Question 3: Are you deep in Microsoft’s ecosystem?
- Yes → Power BI
- No → Continue
Question 4: Do you have engineering capacity?
- Yes → Superset
- No → Power BI
Question 5: Is compliance (SOC 2, ISO 27001) a priority?
- Yes → Superset
- No → Continue
Question 6: Do you need speed above all else?
- Yes → Power BI
- No → Superset
Most Australian enterprises end up at Superset when they work through this logic.
Measuring ROI and Success
Once you’ve chosen, how do you measure if you made the right call?
Power BI ROI Metrics
- User adoption: % of intended users actively using dashboards
- Query performance: Dashboard load time, query latency
- Cost per user: Total annual cost ÷ active users
- Time to insight: Days from data event to dashboard visibility
Target: 80%+ adoption, <2-second dashboard load,
Superset ROI Metrics
- Infrastructure cost: Monthly AWS/Azure bill
- Engineering efficiency: Hours/month spent on BI maintenance
- Compliance readiness: Vanta score, audit findings
- Time to custom dashboards: Days from request to production
Target:
For detailed guidance on measuring AI agency ROI and performance tracking, Australian enterprises should establish baselines before implementation and review quarterly.
Next Steps: Making Your Decision
You now have the framework. Here’s how to move forward:
Immediate Actions (This Week)
-
Audit your current BI spend: Get exact numbers from your finance team. Most enterprises underestimate Power BI’s true cost (licensing + integrations + support).
-
Map your data sensitivity: Classify your data by sensitivity (public, internal, confidential, restricted). Data residency requirements apply only to sensitive data.
-
Assess your team: Do you have data engineers? Can you hire them? Be honest about engineering capacity.
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Define compliance requirements: Do you need SOC 2, ISO 27001, or Privacy Act compliance? This often tips the scales to Superset.
Short-Term (Next 4 Weeks)
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Run a proof of concept: Deploy a trial of both tools on a non-critical dataset. Spend 2 weeks on each.
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Involve your team: Have data analysts, engineers, and business users evaluate both. Their feedback is valuable.
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Talk to vendors: Request pricing for Power BI (including all add-ons). Get quotes from Superset providers (Preset, D23.io, or managed service partners).
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Consult a specialist: If you’re uncertain, engage a platform engineering partner with experience in both tools. PADISO, for example, can assess your specific situation and provide a tailored recommendation in 2–3 days.
Medium-Term (2–3 Months)
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Build your business case: Document cost, compliance, and capability differences. Present to leadership.
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Plan your implementation: Choose your tool, set timelines, and allocate budget.
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Secure stakeholder buy-in: Ensure your team understands the choice and is aligned.
-
Kick off implementation: Start with a pilot, then scale.
Conclusion
For Australian enterprises, the choice between Apache Superset and Power BI isn’t about features—both tools work. It’s about cost, compliance, and control.
Power BI wins on ease and Microsoft integration. Superset wins on cost (70–85% savings), data sovereignty, and audit control.
If you’re a financial services firm, healthcare provider, government agency, or any enterprise handling sensitive Australian data, Superset deployed on Australian infrastructure is the clear choice. You’ll save money, keep data local, and build a compliance-ready analytics platform.
If you’re a smaller business without compliance pressure and you’re already in Microsoft’s ecosystem, Power BI is simpler and faster.
The key is making the decision consciously, with numbers and requirements in hand—not defaulting to the vendor you know.
Ready to evaluate Superset for your organisation? Start with a consultation with a Sydney-based AI and platform engineering partner who can assess your specific needs, data residency requirements, and compliance obligations. PADISO specialises in helping Australian enterprises choose and implement the right BI stack, with a focus on cost, compliance, and long-term scalability.
Your BI platform should serve your business, not constrain it. With the right choice, it will.
Additional Resources
For a deeper technical comparison, see the G2 head-to-head comparison of Apache Superset vs Power BI, which includes user reviews and feature matrices.
For enterprise-specific insights, the Preset blog provides detailed analysis of Superset vs Power BI including performance benchmarks and enterprise suitability.
The official Apache Superset documentation and Microsoft Power BI documentation are essential references for technical evaluation.
For Australian-specific data sovereignty guidance, consult the Office of the Australian Information Commissioner (OAIC) and your industry regulator (ASIC for financial services, AHPRA for healthcare, etc.).
When you’re ready to move forward, explore how PADISO’s platform engineering and AI strategy services can support your BI transformation. We’ve helped 50+ Australian enterprises build, scale, and modernise their data and analytics infrastructure.
Frequently Asked Questions
Is Power BI cheaper than Apache Superset for small teams?
For genuinely small teams (under ~25 users) already on Microsoft 365, Power BI Pro at AU$15.70/user/month is hard to beat on raw licensing. Superset has effectively zero licensing cost but you still need ~0.3–0.5 FTE of engineering time to keep it healthy. Below 25 users that engineering cost typically exceeds Power BI’s licensing. Above 50 users, the math flips quickly in Superset’s favour, especially once Premium per-user (AU$31.50/user) or Premium capacity (AU$7,800+/month) enters the picture.
Does Power BI store data in Australia?
Partially. Microsoft offers Australian regions for Microsoft 365 tenants, but Power BI’s underlying compute and certain features (AutoML, Premium capacity scheduling, ML.NET endpoints) may still route through US or other regions depending on configuration. For APRA-regulated entities or AUSTRAC-reporting businesses where 100% AU residency is a hard constraint, the safer architecture is self-hosted Superset on an AU AWS or Azure region.
Can Apache Superset replace Power BI for finance and accounting teams?
Yes, but it requires intentional design. Finance teams expect Excel-like ergonomics — filters, pivots, ad-hoc slicing. Power BI’s DAX layer mimics this natively. With Superset, the equivalent ergonomics come from pairing it with a semantic layer like dbt or Cube.dev, pre-built dashboard templates, and either Superset’s Explore UI or a notebook layer like Hex / Deepnote for the analyst-driven workflows. Most AU finance teams we’ve migrated land happily on Superset within 4–8 weeks once the semantic layer is in place.
What about Microsoft Fabric?
Fabric ties Power BI more tightly into Microsoft’s full data stack (OneLake, Synapse Data Warehouse, Data Factory) and changes the pricing model toward capacity-based billing. For all-Microsoft enterprises, it’s a reasonable bet. For multi-cloud or cost-sensitive operators, Fabric raises the lock-in cost of staying on Power BI — which is one of the reasons we’re seeing more AU enterprises evaluating Superset migrations in 2026 specifically.
How long does a Superset rollout take in practice?
For an AU mid-market enterprise (50–200 analytics users), a production-quality Superset rollout — architecture, SSO, dbt semantic layer, dashboards, governance, training — typically runs 6 weeks with an experienced partner. This is the scope of PADISO’s $50K D23.io / Superset engagement. DIY deployments typically take 3–6 months because the team is learning on the job.
Is this only about cost?
No. Cost gets people in the door, but the structural reasons AU enterprises switch in 2026 are: (1) data residency and sovereignty under tightening Privacy Act reforms, (2) avoiding deeper Fabric lock-in, (3) embedded-analytics use cases (Superset embeds inside your own product cleanly; Power BI embedding is licensed and constrained), and (4) vendor-neutral semantic layers via dbt that survive the next BI tool change.
Who is PADISO and why are you writing this?
PADISO is a Sydney-based AI and data advisory based in Surry Hills. We run D23.io, our embedded-analytics product built on Apache Superset and ClickHouse, and we deliver Superset rollouts as a fixed-fee engagement to AU mid-market and PE-backed companies. If you want a 30-minute neutral call on whether Superset, Power BI, or something else makes sense for your stack, book here.
Want a Neutral Read on Your Stack?
If you’re in the middle of evaluating Superset, Power BI, Looker, Tableau, Snowflake, Databricks, or some combination — and you’d rather not pay a Big-Four practice $80K for the answer — PADISO offers a fixed-fee, two-week AI Quickstart Audit that includes a written vendor-and-build recommendation across your BI stack. AU$10,000 + GST. Two weeks. Six deliverables.
Or just book a 30-minute call and we’ll tell you over a coffee. Sydney-based teams welcome to come into our Surry Hills office at 81-83 Campbell St — see also our /sydney/ai-advisory page for local engagement details.