Table of Contents
- The Mid-Market Visualisation Problem
- Why Self-Hosting Superset Fails at Scale
- The D23.io Difference: Managed Hosting for Modern Teams
- Cost Economics: Where Managed Hosting Wins
- Speed to Insight and Time-to-Value
- Security, Compliance, and Audit Readiness
- Real-World Deployment Scenarios
- Comparing Hosting Options: D23.io vs. Alternatives
- The Platform Engineering Advantage
- Getting Started: Migration and Implementation
The Mid-Market Visualisation Problem
You’ve got data. Lots of it. Your finance team needs dashboards. Your operations team needs real-time metrics. Your product team needs user behaviour analysis. Your board wants KPI tracking.
But every team is asking the same question: where do we put this?
Mid-market companies—those with $10M to $500M revenue, 50 to 500 employees, and growing technical complexity—face a specific bind. You’re past the startup phase where a single analyst runs everything from their laptop. You’re not yet enterprise-scale where you can justify a dedicated platform engineering team and unlimited infrastructure budgets.
You need visualisation infrastructure that:
- Works immediately. No six-month platform build-out before your first dashboard goes live.
- Scales without drama. Adding a new data source or user shouldn’t require engineering escalation.
- Costs predictably. Not $50K per seat for Tableau, not $200K for a failed self-hosted Superset project.
- Passes audits. If you’re pursuing SOC 2 or ISO 27001 compliance—or your customers demand it—you need hosting that’s audit-ready from day one.
- Stays current. Security patches, dependency updates, and feature releases should happen without your team babysitting infrastructure.
Most mid-market teams default to one of three paths, and all three fail:
Path 1: Enterprise BI (Tableau, Looker, Power BI). Costs $100+ per user per month. Licensing sprawl. Vendor lock-in. Your analysts spend time on tool administration instead of analysis.
Path 2: Self-hosted open-source (Superset, Metabase). Cheap upfront. Becomes a tax on your engineering team within months. You own the infrastructure, the security posture, the scaling headaches, and the compliance burden. A breach isn’t the tool vendor’s problem—it’s yours.
Path 3: Homegrown dashboards. React + D3 + your database. Flexible. Expensive. Fragile. Every dashboard is a bespoke project. Your team becomes a dashboard factory instead of building product.
D23.io exists because this problem is real, and the stakes are high.
Why Self-Hosting Superset Fails at Scale
Apache Superset is genuinely good software. It’s open-source, flexible, and powerful. Many teams start here thinking they’ve solved the problem.
They haven’t. They’ve just deferred it.
The Hidden Cost of “Free” Infrastructure
When you self-host Superset, you’re not paying a subscription fee. You’re paying in engineer time, opportunity cost, and risk.
Here’s what happens in practice:
Month 1-2: Enthusiasm. Your data engineer or backend engineer sets up Superset on EC2 or Kubernetes. First dashboards go live. Success.
Month 3-4: Complexity creeps in. Someone needs to add a new database connection. Someone else wants to embed dashboards in the product. A third person asks about scheduled reports. Your engineer is now a part-time Superset admin.
Month 5-6: Scaling problems emerge. Dashboards are slow. Queries are timing out. Your engineer spends a week tuning database indexes and Superset configuration. They’re not building product anymore.
Month 7-8: Security questions arrive. Your security team asks: who has access to what? Where are the credentials stored? How are we backing up the metadata database? Your engineer realizes they’ve built something that’s not audit-ready. Remediation takes weeks.
Month 9-10: A dependency breaks. A Superset upgrade introduces a bug. A database driver is no longer maintained. Your engineer spends days troubleshooting. In the meantime, dashboards are offline.
Month 11-12: You do the math. Your engineer has spent 20% of their time on Superset operations. At $150K+ salary, that’s $30K+ per year in labour cost. You’ve also had two incidents where dashboards were unavailable. You’ve deferred three feature projects because of infrastructure work. And you still don’t have audit-ready infrastructure.
You realise you should have outsourced this.
The Compliance Tax
Self-hosting gets worse if you need compliance.
If you’re pursuing SOC 2 Type II certification, your auditors will ask:
- How are you managing access controls?
- Where are encryption keys stored?
- How do you handle data retention and deletion?
- What’s your incident response plan?
- How do you monitor for unauthorised access?
- What’s your change management process?
For a self-hosted system, every answer is your responsibility. You own the security posture. You own the audit trail. You own the compliance evidence.
Building this infrastructure correctly takes weeks of engineering work and months of process documentation. Most teams skip it, then panic when a customer asks for SOC 2 evidence.
The Scaling Treadmill
As your data grows, self-hosted Superset becomes a resource hog.
Superset runs in-memory query caching. It needs enough CPU and RAM to handle concurrent dashboards. It needs a robust metadata database (PostgreSQL). It needs monitoring, logging, and alerting. It needs backups. It needs disaster recovery.
You’re now managing a production system with the same operational rigour as your main product. Except it’s not your core business. It’s a tax.
Mid-market teams often reach this point and realise: we’re spending engineering effort on infrastructure that a specialised vendor could run better, cheaper, and more securely.
That’s when they look at managed hosting.
The D23.io Difference: Managed Hosting for Modern Teams
D23.io is a managed Superset hosting platform built for mid-market teams that need custom visualisation infrastructure without the operational burden.
Here’s what that means in practice:
You Get Superset Without the Operations Tax
D23.io handles the infrastructure, security, scaling, and compliance. You focus on dashboards and insights.
When you sign up:
- Your Superset instance is live in hours, not weeks.
- Database connections are encrypted and managed.
- Access controls are built in. Role-based permissions work out of the box.
- Backups are automated. Disaster recovery is handled.
- Security patches and Superset updates happen automatically, without your team lifting a finger.
- Scaling is invisible. As your data grows and your dashboard load increases, infrastructure scales automatically.
Your engineering team gets their time back. Your data team gets a tool that just works.
Compliance-Ready From Day One
D23.io is built with audit readiness in mind.
If you’re pursuing SOC 2 or ISO 27001 certification via Vanta, D23.io instances integrate with your compliance framework. Access logs are preserved. Data flows are documented. Security controls are auditable.
You don’t have to build compliance infrastructure separately. It’s part of the platform.
Cost Predictability
With enterprise BI tools, cost scales with users. With self-hosted Superset, cost scales with infrastructure and engineer time. With D23.io, cost scales with usage.
You pay for:
- The Superset instance (fixed monthly fee).
- Data processing (based on query volume and data size).
- Storage (based on your database size).
- Optional add-ons (alerting, scheduling, advanced permissions).
No surprises. No hidden licensing. No seat-based pricing. No engineer time burned on infrastructure.
Integration With Your Existing Stack
D23.io connects to any data source Superset supports: PostgreSQL, MySQL, Snowflake, BigQuery, Redshift, ClickHouse, MongoDB, and dozens more.
If you’re running platform engineering projects with PADISO or another partner, D23.io integrates seamlessly into your data architecture. It’s not a siloed tool—it’s part of your data stack.
Multi-Tenancy and Embedded Analytics
Some mid-market teams need to embed dashboards in their product. D23.io supports this natively.
You can:
- Embed dashboards in your app with a few lines of code.
- Control permissions per customer.
- Brand dashboards with your logo and colour scheme.
- Track usage and analytics.
This is where D23.io shines for product-led companies. You’re not building custom React dashboards. You’re using a managed, scalable platform.
Cost Economics: Where Managed Hosting Wins
Let’s talk numbers, because cost is often the deciding factor for mid-market buyers.
The Per-User Trap
Tableau and Looker charge per user. A typical enterprise BI deployment costs $100–$150 per user per month.
For a 50-person company with 20 dashboard users, that’s $24K–$36K per year. For a 200-person company with 50 users, that’s $60K–$90K per year.
Licensing grows as your company grows. More people = more users = higher cost.
The Self-Hosted Hidden Tax
Self-hosting Superset looks free. It’s not.
Here’s the real cost:
| Cost Category | Year 1 | Year 2+ |
|---|---|---|
| Infrastructure (EC2, RDS, etc.) | $5K–$15K | $10K–$30K |
| Engineer time (20% of one senior engineer) | $30K | $30K |
| Compliance and security work | $10K–$20K | $5K–$10K |
| Incident response and downtime | $5K–$10K | $5K–$10K |
| Total | $50K–$55K | $50K–$80K |
And that assumes nothing goes seriously wrong. A major incident—a breach, a data loss, a compliance failure—can cost 10x this amount in remediation and reputational damage.
The D23.io Cost Model
D23.io pricing is transparent and usage-based:
- Base instance: $500–$2,000 per month depending on features and data size.
- Data processing: $0.10–$0.50 per GB queried (varies by source).
- Storage: $0.05–$0.10 per GB stored.
- Add-ons: Alerting, scheduling, advanced permissions ($100–$500/month each).
For a typical mid-market deployment:
- 50 GB of data across 5 databases.
- 100 queries per day.
- 20 active dashboard users.
- Basic alerting and scheduling.
Monthly cost: ~$1,500–$2,500. Annual cost: $18K–$30K.
That’s 30–50% cheaper than enterprise BI. It’s also cheaper than the true cost of self-hosting when you factor in engineer time and risk.
Break-Even Analysis
For most mid-market teams, D23.io breaks even against self-hosting within 6 months:
- Self-hosted cost (Year 1): $50K–$55K (infrastructure + 20% engineer time).
- D23.io cost (Year 1): $18K–$30K.
- Savings (Year 1): $20K–$37K.
- Plus: Your engineer gets 20% of their time back (worth $30K+).
The longer you run D23.io, the more you save. After three years, you’ve saved $100K+ versus self-hosting, and your engineers have built three more product features instead of babysitting dashboards.
Scaling Economics
As your company grows, the cost advantage compounds.
If you add 10 new dashboard users per year:
- Enterprise BI: +$12K–$18K per year in licensing.
- Self-hosted: +$10K–$15K per year in infrastructure (and more engineer time).
- D23.io: +$2K–$5K per year in data processing.
D23.io’s cost grows with usage, not headcount. That’s the key difference.
Speed to Insight and Time-to-Value
Cost matters, but speed matters more for mid-market teams moving fast.
D23.io is built for speed.
From Signup to First Dashboard: Hours, Not Weeks
With D23.io:
- Sign up and provide database credentials (10 minutes).
- D23.io connects to your data sources and auto-discovers tables (5 minutes).
- You create your first dashboard using the visual editor (30 minutes).
- You share it with your team (2 minutes).
Total time to first dashboard: 45 minutes.
Compare this to self-hosted Superset:
- Provision infrastructure (EC2, RDS, VPC, security groups) – 2 hours.
- Install Superset and dependencies – 1 hour.
- Configure the metadata database – 1 hour.
- Set up database connections and encryption – 2 hours.
- Configure access controls and user management – 2 hours.
- Set up monitoring and alerting – 2 hours.
- Document the setup and create runbooks – 2 hours.
- Create your first dashboard – 1 hour.
Total time to first dashboard: 13 hours.
That’s 15x faster with D23.io.
For a team that needs dashboards live this quarter, not next quarter, that speed is critical.
Iterating on Dashboards
Speed also matters in iteration.
With D23.io, adding a new data source takes minutes:
- Provide credentials.
- D23.io discovers tables.
- You build new dashboards.
With self-hosted Superset, it’s an engineering task:
- Request the connection from your data engineer.
- They provision infrastructure access.
- They configure the connection in Superset.
- They test it.
- You build dashboards.
D23.io removes the middleman. Your data team can self-serve.
Time-to-Insight for Stakeholders
Mid-market teams often have non-technical stakeholders who need dashboards: finance, operations, customer success.
With D23.io’s visual editor, these teams can build dashboards without SQL:
- Drag and drop fields.
- Choose visualisation types.
- Apply filters and grouping.
- Share dashboards.
No SQL required. No data engineer bottleneck.
This is where managed hosting creates real business value. Your team spends less time on infrastructure and more time on insights.
Security, Compliance, and Audit Readiness
For mid-market teams pursuing compliance or serving regulated customers, security isn’t optional. It’s table stakes.
D23.io is built with security as a first-class concern.
Encryption in Transit and at Rest
All data flowing into and out of D23.io is encrypted with TLS 1.2+. Data at rest is encrypted with AES-256.
Database credentials are encrypted and never logged or exposed.
This is standard practice, but it matters: self-hosted Superset requires you to configure this correctly. Most teams don’t.
Access Control and Role-Based Permissions
D23.io supports role-based access control (RBAC) out of the box:
- Admin: Full access to all dashboards and data sources.
- Editor: Can create and edit dashboards, but can’t modify data sources.
- Viewer: Can view dashboards but can’t edit.
- Custom roles: Define fine-grained permissions per dashboard or data source.
Access changes are logged. You can audit who accessed what, when.
For compliance frameworks like SOC 2, this audit trail is essential.
Compliance Frameworks and Integrations
D23.io is designed to integrate with compliance tools like Vanta.
When you’re pursuing SOC 2 Type II certification, Vanta connects to D23.io and automatically gathers:
- Access logs.
- Configuration settings.
- Backup and disaster recovery evidence.
- Security control documentation.
This cuts weeks of manual evidence collection. Your compliance team can focus on policy and process, not data gathering.
Data Retention and Deletion
For teams handling sensitive data (PII, health records, financial data), data retention policies matter.
D23.io lets you define:
- How long data is retained.
- Automatic deletion after retention period expires.
- Manual deletion on request.
- Audit trails for all deletions.
This is critical for GDPR, HIPAA, and other privacy regulations.
Incident Response and Disaster Recovery
D23.io maintains:
- Automated daily backups.
- Geographically redundant storage.
- 99.9% uptime SLA.
- Incident response procedures.
- Regular disaster recovery testing.
If something goes wrong, D23.io has a playbook. Your team doesn’t have to improvise.
Comparing Security Postures
Here’s how D23.io stacks up against alternatives:
| Security Feature | D23.io | Self-Hosted Superset | Enterprise BI |
|---|---|---|---|
| Encryption in transit | ✓ | ✓ (if configured) | ✓ |
| Encryption at rest | ✓ | ✗ (usually) | ✓ |
| RBAC | ✓ | ✓ (basic) | ✓ |
| Audit logging | ✓ | ✗ (usually) | ✓ |
| Compliance integrations | ✓ | ✗ | ✓ |
| Data retention policies | ✓ | ✗ | ✓ |
| Automated backups | ✓ | ✗ (usually) | ✓ |
| Disaster recovery | ✓ | ✗ (usually) | ✓ |
| Security incident response | ✓ | ✗ | ✓ |
D23.io matches enterprise BI on security. It’s vastly better than self-hosted Superset, where most teams skip security hardening.
Real-World Deployment Scenarios
Let’s walk through how different mid-market teams use D23.io.
Scenario 1: Financial Services Company (50 people)
Situation: A fintech startup offering lending products. They need dashboards for:
- Loan portfolio analytics (underwriting team).
- Risk metrics (compliance team).
- Customer acquisition cost and lifetime value (growth team).
- Operational KPIs (CEO).
Challenge: They’re pursuing SOC 2 Type II certification. They need audit-ready infrastructure. They can’t afford Tableau ($100+/user/month). They don’t have the engineering bandwidth to self-host Superset.
D23.io solution:
- Connected to their PostgreSQL database (loan data) and Stripe API (payment data).
- Built 8 dashboards in two weeks using the visual editor.
- Configured RBAC: underwriting team sees loan data, compliance team sees risk metrics, growth team sees acquisition data.
- Integrated with Vanta for SOC 2 audit evidence.
- Cost: $1,800/month.
Outcome: Dashboards live in two weeks. Audit-ready infrastructure. No engineering overhead. $21.6K/year for complete visualisation infrastructure.
Scenario 2: E-Commerce Retailer (150 people)
Situation: A DTC e-commerce brand with 10+ data sources: Shopify, Google Analytics, Klaviyo, Stripe, custom data warehouse. They need dashboards for:
- Daily sales and conversion metrics.
- Inventory and fulfillment tracking.
- Customer cohort analysis.
- Email campaign performance.
Challenge: They tried self-hosting Superset 18 months ago. It works, but their backend engineer spends 15–20% of their time maintaining it. They want to free up that engineer to build product.
D23.io solution:
- Migrated from self-hosted Superset to D23.io (data import took one day).
- Connected all 10 data sources.
- Recreated 15 existing dashboards in the new platform (two days of work).
- Added new dashboards for inventory and cohort analysis (one week).
- Cost: $2,500/month.
Outcome: Engineer freed up from infrastructure work. Dashboards faster and more reliable. Cost slightly higher than self-hosted, but 20% of engineer time is worth $30K+/year. Net savings: $25K+/year.
Scenario 3: SaaS Startup (80 people, $5M ARR)
Situation: A B2B SaaS company. They need:
- Internal dashboards for operations, finance, product.
- Embedded dashboards for customers (as a product feature).
Challenge: They’re building embedded dashboards with React + D3. It’s expensive and slow. Each new dashboard is a 2-week engineering project. They want to move faster.
D23.io solution:
- Built internal dashboards for operations (KPIs, churn, MRR).
- Built customer-facing embedded dashboards for product analytics.
- Customers can drill down into their own data without leaving the product.
- Multi-tenancy configured: each customer sees only their data.
- Cost: $3,000/month.
Outcome: Embedded dashboards live in 4 weeks instead of 4 months. Engineers freed up for core product. Customers get better analytics. Differentiated product feature.
These aren’t hypothetical. These are real patterns we see across mid-market teams.
Comparing Hosting Options: D23.io vs. Alternatives
Let’s do a direct comparison of hosting choices for custom visualisation infrastructure.
D23.io vs. Self-Hosted Superset
| Dimension | D23.io | Self-Hosted |
|---|---|---|
| Setup time | 1 hour | 13+ hours |
| Monthly cost | $1,500–$3,000 | $2,000–$3,500 (infra + engineer time) |
| Scaling | Automatic | Manual (requires engineering) |
| Security | Audit-ready | Requires configuration |
| Compliance | Built-in (Vanta integration) | Manual evidence collection |
| Backups | Automated | Your responsibility |
| Updates | Automatic | Manual |
| Monitoring | Included | Your responsibility |
| Support | D23.io team | Stack Overflow |
| Vendor lock-in | Moderate (easy to export) | Low (open-source) |
Winner for mid-market: D23.io. Better time-to-value, lower total cost of ownership, better compliance posture.
D23.io vs. Tableau
| Dimension | D23.io | Tableau |
|---|---|---|
| Setup time | 1 hour | 2–4 weeks (with professional services) |
| Monthly cost (20 users) | $2,000–$3,000 | $2,400–$3,600 |
| Per-user licensing | No | Yes ($100–$150/user/month) |
| Scaling cost | Linear with data | Linear with users |
| Ease of use | High (visual editor) | High (visual editor) |
| Customisation | Good (Superset flexibility) | Limited (Tableau constraints) |
| Compliance | SOC 2-ready | SOC 2-ready |
| Embedded analytics | Native | Requires Tableau Server |
| Vendor lock-in | Moderate | High |
Winner for mid-market: D23.io for cost and flexibility. Tableau for enterprises with unlimited budgets.
D23.io vs. Looker
| Dimension | D23.io | Looker |
|---|---|---|
| Setup time | 1 hour | 4–8 weeks |
| Monthly cost (20 users) | $2,000–$3,000 | $3,000–$5,000 |
| Per-user licensing | No | Yes |
| LookML requirement | No (SQL or visual) | Yes (LookML learning curve) |
| Ease of use | High | Medium (LookML required for advanced) |
| Customisation | Good | Excellent (if you learn LookML) |
| Compliance | SOC 2-ready | SOC 2-ready |
| Vendor lock-in | Moderate | Very high (LookML lock-in) |
Winner for mid-market: D23.io for speed and cost. Looker for teams that want to invest in LookML expertise.
D23.io vs. Metabase
| Dimension | D23.io | Metabase |
|---|---|---|
| Setup time | 1 hour | 2–4 hours (self-hosted) |
| Monthly cost | $2,000–$3,000 | $500–$2,000 (self-hosted) or $1,000–$3,000 (managed) |
| Ease of use | High | Very high (simplest UI) |
| Customisation | Good | Limited |
| Scaling | Automatic | Manual (self-hosted) |
| Compliance | SOC 2-ready | Requires configuration (self-hosted) |
| Support | D23.io team | Community |
Winner for mid-market: Depends. Metabase self-hosted is cheaper but requires engineering. Metabase managed is similar cost to D23.io but simpler UI. D23.io wins if you need customisation and compliance.
The Platform Engineering Advantage
D23.io works best as part of a broader platform engineering strategy.
Many mid-market teams are modernising their data infrastructure: building data warehouses, implementing real-time pipelines, consolidating databases, moving to cloud-native architectures.
D23.io integrates into this journey.
D23.io in a Modern Data Stack
A typical mid-market data architecture looks like:
Data Sources (Postgres, APIs, Kafka)
→ ETL/ELT (dbt, Fivetran, Stitch)
→ Data Warehouse (Snowflake, BigQuery, Redshift, ClickHouse)
→ Analytics & Visualisation (D23.io)D23.io sits at the end of this pipeline. It consumes clean, transformed data and makes it accessible to business users.
This architecture is cheaper and faster than legacy BI stacks. And it’s what PADISO’s platform engineering teams typically recommend for mid-market modernisation projects.
Integration With Data Warehouses
D23.io connects natively to modern data warehouses:
- Snowflake: High-performance queries, cost-effective scaling.
- BigQuery: Serverless, integrates with Google Cloud ecosystem.
- Redshift: AWS-native, integrates with your AWS architecture.
- ClickHouse: High-throughput analytics, real-time dashboards.
For teams running platform engineering projects in Sydney, Melbourne, or Brisbane, D23.io is a natural fit into the data stack.
Cost Optimisation Across the Data Stack
When you’re designing a modern data architecture, cost matters. Following Google Cloud best practices for cost-effective GCP workloads and AWS cost optimisation guidance, you want to avoid unnecessary data duplication and processing.
D23.io helps here. Instead of exporting data from your warehouse into a separate BI tool, D23.io queries your warehouse directly. No data duplication. No extra storage costs. You pay only for the queries you run.
This is especially important if you’re using Azure cloud services or other cloud platforms where data transfer and storage costs compound.
Security Across the Data Stack
D23.io integrates with modern security frameworks. Following NIST’s Secure Software Development Framework and OWASP security best practices, D23.io:
- Encrypts all data in transit and at rest.
- Implements role-based access control.
- Logs all access and changes.
- Integrates with compliance frameworks (SOC 2, ISO 27001).
When you’re building a SOC 2-ready platform, D23.io is a compliant component from day one.
Getting Started: Migration and Implementation
If you’re considering D23.io, here’s what the journey looks like.
Phase 1: Assessment (1–2 weeks)
What happens:
- You audit your current visualisation infrastructure: existing dashboards, data sources, users.
- D23.io team assesses your data sources and complexity.
- You define success metrics: cost savings, time-to-value, compliance requirements.
Deliverables:
- Current state assessment.
- Migration plan.
- Cost-benefit analysis.
- Timeline and resource requirements.
Phase 2: Setup and Configuration (2–4 weeks)
What happens:
- D23.io instance is provisioned.
- Data sources are connected and tested.
- Access controls are configured.
- Compliance integrations (Vanta, etc.) are set up.
Deliverables:
- Live D23.io instance.
- Configured data sources.
- RBAC configured.
- Compliance framework integrated.
Phase 3: Migration (2–8 weeks, depending on complexity)
What happens:
- Existing dashboards are recreated in D23.io.
- Data is validated.
- Users are trained on the new platform.
- Old system is decommissioned.
Deliverables:
- All dashboards migrated.
- Users trained.
- Old infrastructure decommissioned.
- Cost savings realised.
Phase 4: Optimisation (Ongoing)
What happens:
- New dashboards are built as needed.
- Data sources are added.
- Users are trained on advanced features.
- Performance is monitored and optimised.
Deliverables:
- Ongoing support from D23.io.
- Regular performance reviews.
- Continuous optimisation.
Key Success Factors
To make D23.io implementation successful:
- Executive sponsorship. Make sure leadership understands the business case and supports the project.
- Clear data ownership. Define who owns each data source and dashboard.
- User training. Invest in training so your team can self-serve dashboards.
- Phased rollout. Start with high-value dashboards, then expand.
- Performance monitoring. Track metrics: dashboard load times, query performance, user adoption.
Working With PADISO for Platform Engineering
If you’re modernising your data infrastructure as part of a broader platform engineering project, PADISO can help.
PADISO’s platform engineering teams work with mid-market companies across Australia and the US to:
- Design and build modern data architectures.
- Implement data warehouses and ETL pipelines.
- Integrate analytics and visualisation tools like D23.io.
- Ensure compliance and security throughout the stack.
We’ve worked with teams in Sydney, Melbourne, Brisbane, and across the US (New York, Los Angeles, Chicago, Boston, Seattle, Austin, Dallas, Atlanta, and Miami).
If you want to discuss how D23.io fits into your data strategy, reach out to PADISO’s services team or check out case studies from similar companies.
Summary: Why Mid-Market Teams Choose D23.io
Mid-market teams face a specific problem: they need custom visualisation infrastructure that’s fast, cheap, secure, and audit-ready.
D23.io solves this problem.
Compared to alternatives:
- vs. Enterprise BI (Tableau, Looker): D23.io is 30–50% cheaper and faster to deploy.
- vs. Self-hosted Superset: D23.io eliminates engineering overhead and compliance burden.
- vs. Homegrown dashboards: D23.io is faster and more maintainable than bespoke development.
The business case is clear:
- Cost: $18K–$30K per year vs. $50K+ for self-hosted or $60K+ for enterprise BI.
- Speed: Dashboards live in hours, not weeks.
- Compliance: Audit-ready from day one.
- Scalability: Infrastructure scales automatically as your data grows.
- Flexibility: Integrates into modern data stacks and platform engineering projects.
For mid-market teams moving fast and building right, D23.io is the obvious choice.
Next Steps
If you’re evaluating D23.io:
- Assess your current state. How much are you spending on visualisation infrastructure today? How much engineering time is it consuming?
- Define your requirements. What dashboards do you need? What data sources? What compliance requirements?
- Run the numbers. Compare D23.io cost vs. your current cost (including engineer time).
- Talk to the team. D23.io offers free consultations to understand your needs and show you what’s possible.
- Start small. Migrate your highest-value dashboards first. Prove the value. Scale from there.
The teams that move fastest are the ones that outsource infrastructure and focus on insights. D23.io makes that possible.
Additional Resources
For teams building modern data infrastructure, these resources are worth reviewing:
- Web performance optimisation fundamentals matter for fast-loading dashboards, especially for remote teams and international users.
- Google’s web performance learning resource covers speed and responsiveness best practices applicable to analytics platforms.
- Cloud Security Alliance research on cloud threats is essential reading for teams evaluating hosted solutions.
For more information about D23.io and how it fits into your platform strategy, visit PADISO’s products page or contact the services team.