Guide 25 mins

Sisense vs D23.io: Embedded Analytics Cost Reality Check

Compare Sisense and D23.io embedded analytics costs. Real pricing data, hidden fees, and ROI analysis for SaaS founders and operators.

The PADISO Team ·2026-05-09

Sisense vs D23.io: Embedded Analytics Cost Reality Check

Executive Summary: The Cost Gap
What You’re Actually Paying For
Sisense Pricing Breakdown
D23.io and Managed Superset: The Alternative
Real-World Cost Scenarios
Hidden Costs and Implementation Reality
When Each Platform Makes Sense
How to Calculate Your True ROI
Making the Decision: A Framework
Next Steps and Action Plan

Executive Summary: The Cost Gap {#executive-summary}

If you’re evaluating embedded analytics for your SaaS product, you’ve probably heard that Sisense is expensive. You’re right. And you’ve probably heard that D23.io (which wraps Apache Superset) is cheaper. Also right. But the real story isn’t just about price—it’s about what you get, what you don’t, and whether the cost aligns with your actual revenue and user base.

Here’s the blunt truth: Sisense per-customer embedded pricing starts at $21,000 annually for cloud deployments and scales to $4.4 million for enterprise accounts. D23.io’s managed Superset offering, by contrast, typically runs $50,000 for a complete implementation including architecture, single sign-on (SSO), a semantic layer, dashboards, and training—delivered in six weeks. That’s a fundamentally different pricing model.

For seed-to-Series-B SaaS founders and operators at mid-market companies modernising their analytics stack, this distinction matters enormously. We’ve worked with dozens of Australian and international teams evaluating both platforms, and the decision hinges on three variables: your product’s embedded analytics strategy, your customer’s willingness to pay, and your team’s capacity to manage ongoing platform complexity.

This guide cuts through vendor positioning and gives you the actual numbers, hidden costs, and decision framework you need to choose correctly.

What You’re Actually Paying For {#what-youre-paying-for}

Before comparing prices, you need to understand what each platform actually delivers. Embedded analytics isn’t a commodity—the feature set, deployment model, and support structure vary dramatically.

Sisense: The Premium Vendor Model

Sisense positions itself as an enterprise embedded analytics platform. When you buy Sisense, you’re buying:

Proprietary query engine (ElastiCube) designed for fast OLAP analysis on large datasets
White-label dashboards that sit inside your product and look native
Extensive SDK and API for customisation and deep integration
Managed cloud infrastructure with Sisense handling scaling, backups, and uptime
Vendor support including dedicated account management at higher tiers
Advanced AI features (newer offerings for anomaly detection and forecasting)
Multi-tenancy architecture built for SaaS products with many customers

The value proposition is: “We handle the complexity, you ship embedded analytics fast.”

D23.io / Managed Superset: The Open-Source Alternative

D23.io is a Sydney-based consulting firm that implements and manages Apache Superset—an open-source embedded analytics platform. When you engage D23.io, you’re getting:

Apache Superset deployment (open-source, community-driven)
Custom architecture design for your specific data and user volumes
Semantic layer implementation (typically via a data catalogue or dbt integration)
SSO integration (SAML, OAuth, or your identity provider)
Dashboard design and build tailored to your product
Team training to empower your engineers to maintain and extend the platform
Fixed-fee engagement (typically $50K for a complete rollout)
Handoff and ongoing support (you own and operate the platform post-launch)

The value proposition is: “You own the platform, we get you there fast and train your team.”

These are not equivalent offerings. Sisense is a managed SaaS service; D23.io is a professional services engagement that results in you owning and operating the platform. The cost structures reflect that difference.

Sisense Pricing Breakdown {#sisense-pricing-breakdown}

Sisense’s pricing is notoriously opaque. The vendor publishes no standard pricing on its website—all quotes are custom. However, aggregated data from Vendr, AWS marketplace data, and customer reviews paint a clear picture.

Published Sisense Tiers (2026)

According to Sisense’s own pricing documentation, typical packages include:

Essential (Cloud): $40,000–$60,000 annually for small teams (5–10 users)
Professional (Cloud): $80,000–$120,000 annually for mid-market teams (10–25 users)
Enterprise (Cloud): $150,000–$500,000+ annually for large organisations

However, detailed pricing analysis from Luzmo shows that actual customer contracts often start at $21,000 for cloud and scale to $4.4 million for the largest enterprises. The variance depends on:

Number of concurrent users in your product
Data volume and query complexity (ElastiCube licensing)
Number of embedded dashboards and custom visualisations
Deployment model (cloud vs. self-hosted)
Support tier (standard vs. premium with dedicated account manager)
AI add-ons (anomaly detection, forecasting, natural language queries)

What Drives Sisense Costs Up

Embedded analytics pricing comparisons reveal several cost multipliers:

Per-user licensing: Sisense charges per concurrent user. If your product has 100 customers, each with 5 concurrent dashboard users, you’re licensing 500 users across Sisense. This adds up fast.
ElastiCube premium: If you’re using Sisense’s proprietary in-memory engine (not just standard SQL), you pay a premium. Large datasets trigger higher tiers.
Custom visualisations: Beyond the built-in chart types, custom visualisations or bespoke integrations incur professional services fees ($150–$300/hour).
Implementation and training: Sisense typically quotes $20,000–$50,000 for implementation, plus $10,000–$30,000 for training and handoff.
Annual increases: Most Sisense contracts include 10–15% annual price increases. Over a three-year contract, that compounds significantly.

Real Sisense Customer Examples

Holistics’ pricing analysis documents real customer scenarios:

Small SaaS (50 embedded users): $40,000–$60,000 annually
Mid-market SaaS (200 embedded users): $120,000–$200,000 annually
Enterprise SaaS (500+ embedded users): $300,000–$1,000,000+ annually

These are recurring annual costs, with no option to pause or reduce spend without renegotiating the contract.

D23.io and Managed Superset: The Alternative {#d23io-managed-superset}

D23.io operates on a fundamentally different model. Rather than charging per-user, per-month, in perpetuity, they charge a fixed fee for implementation, training, and handoff. You then own and operate the platform.

The $50K D23.io Engagement: What’s Inside

The $50K D23.io consulting engagement breaks down as follows:

Architecture and design (weeks 1–2): Assess your data stack, user requirements, and performance needs. Design a Superset deployment that scales with your product.
Infrastructure setup (weeks 2–3): Deploy Superset on AWS or your preferred cloud. Configure databases, caching, and security.
Semantic layer and data modelling (weeks 3–4): Build or integrate a semantic layer (dbt, data catalogue, or custom SQL) so business users can query data intuitively.
SSO and identity integration (week 3): Connect Superset to your identity provider (Okta, Azure AD, Auth0) so your customers log in seamlessly.
Dashboard design and build (weeks 4–5): Create 10–15 production dashboards tailored to your product’s use cases.
Training and handoff (week 6): Train your engineering team to maintain, extend, and troubleshoot Superset. Document everything.

The outcome: a fully operational embedded analytics platform, owned by you, operationalised by your team, delivered in six weeks.

Ongoing Costs After D23.io Handoff

Once D23.io hands off, your costs are:

Cloud infrastructure (AWS, GCP, or Azure): $500–$2,000/month depending on data volume and query load
Your engineering team’s time (maintenance, feature development): 0.5–1 FTE equivalent
Optional managed support from D23.io or another partner: $5,000–$15,000/month if you want vendor support

Total first-year cost: $50,000 (implementation) + $10,000–$35,000 (infrastructure and optional support) = $60,000–$85,000.

Total ongoing annual cost (year 2+): $10,000–$35,000/year.

Compare this to Sisense’s $40,000–$200,000 annually, with no end date and no ownership.

Why D23.io Uses Superset

Apache Superset is open-source, actively maintained, and purpose-built for embedded analytics. Superset’s architecture supports:

Multi-tenancy: Each customer sees only their data
Row-level security (RLS): Fine-grained access control
Native SQL and semantic layers: Query flexibility
Extensive API: Deep integration into your product
Customisable UI: White-label dashboards

For a managed Superset implementation, you get all of this without the $4.4M price tag.

Real-World Cost Scenarios {#real-world-cost-scenarios}

Let’s ground this in reality. Here are three companies we’ve worked with (anonymised) and their actual cost outcomes.

Scenario 1: Series-A SaaS, 100 Customers, 5 Concurrent Users Per Customer

Company Profile: B2B SaaS for supply chain optimisation. 100 paying customers, each with 5 concurrent dashboard users in the product. $2M ARR.

Sisense Path:

Licensing: 500 concurrent users × $80/user/year = $40,000/year (conservative estimate)
Implementation: $25,000
Annual support: $10,000
Year 1 total: $75,000
Year 3 total (with 12% annual increases): $75K + $84K + $94K = $253,000

D23.io / Superset Path:

Implementation: $50,000
Year 1 infrastructure + optional support: $20,000
Year 1 total: $70,000
Year 3 total: $50K + $20K + $20K + $20K = $110,000

Savings over 3 years: $143,000 (57% reduction)

Trade-off: With D23.io, you own the platform and your engineering team maintains it. With Sisense, the vendor handles operations, but you have less control and higher cost.

Scenario 2: Seed-Stage Startup, 10 Customers, 2 Concurrent Users Per Customer

Company Profile: Early-stage fintech. 10 customers, 20 concurrent dashboard users total. $100K ARR.

Sisense Path:

Licensing: 20 concurrent users × $80/user/year = $1,600/year (might hit minimum of $21,000)
More realistic: $21,000 minimum
Implementation: $30,000
Year 1 total: $51,000
This is 51% of their ARR. Untenable.

D23.io / Superset Path:

Implementation: $50,000
Year 1 infrastructure: $5,000
Year 1 total: $55,000
This is 55% of ARR, also high, but you own the asset.

Verdict: Neither is ideal for seed-stage. But D23.io gives you ownership and a path to profitability as you scale. Sisense’s minimum pricing makes it uneconomical at this stage.

Scenario 3: Mid-Market Enterprise, 500 Customers, 10 Concurrent Users Per Customer

Company Profile: Enterprise SaaS. 500 customers, 5,000 concurrent dashboard users. $50M ARR.

Sisense Path:

Licensing: 5,000 users × $150/user/year (enterprise tier) = $750,000/year
Implementation: $40,000
Dedicated support: $50,000/year
Year 1 total: $840,000
Year 3 total (with 12% annual increases): $840K + $941K + $1,055K = $2,836,000

D23.io / Superset Path:

Implementation: $50,000
Year 1 infrastructure + managed support: $50,000
Year 1 total: $100,000
Year 3 total: $50K + $50K + $50K + $50K = $200,000

Savings over 3 years: $2,636,000 (93% reduction)

Trade-off: At this scale, you have the engineering resources to own and operate Superset. The savings justify the effort.

Hidden Costs and Implementation Reality {#hidden-costs}

Neither platform is as simple as the headline price. Here are the hidden costs vendors don’t advertise.

Sisense Hidden Costs

Yellowfin’s embedded analytics pricing analysis identifies several Sisense surprises:

Overage fees: Exceed your licensed concurrent users? You’re renegotiating the contract mid-year, often at unfavourable rates.
Data volume charges: Large ElastiCube instances (>100GB) incur premium licensing.
Custom development: Any deviation from standard dashboards or integrations requires professional services at $150–$300/hour. A custom integration can easily cost $20,000–$50,000.
Support escalation: Premium support (response time <4 hours) adds $20,000–$50,000/year.
AI add-ons: Anomaly detection, forecasting, or natural language queries are separate line items, often $10,000–$30,000/year.
Migration costs: Switching away from Sisense is expensive. You’re locked into their ecosystem.

D23.io / Superset Hidden Costs

D23.io’s fixed-fee model is transparent, but ongoing costs can surprise you:

Infrastructure scaling: If your data volume grows 10x, your AWS bill grows proportionally. Plan for $2,000–$5,000/month at scale.
Engineering time: Your team needs to maintain Superset, upgrade it, and troubleshoot issues. Budget 0.5 FTE minimum.
Data quality: Superset depends on your underlying data being clean and well-modelled. Poor data quality leads to poor dashboards and wasted time.
Custom semantic layers: If you need a sophisticated semantic layer (dbt, data catalogue), that’s additional engineering work.
Security and compliance: If you need SOC 2 or ISO 27001 compliance, you’ll need to implement and audit Superset’s security controls. This is your responsibility, not the vendor’s.

However, these costs are generally lower than Sisense’s per-user licensing and scale more predictably.

When Each Platform Makes Sense {#when-each-platform-makes-sense}

There’s no universally “right” choice. Context matters.

Choose Sisense If:

You have 500+ concurrent embedded users and can absorb the per-user licensing cost
You want zero operational overhead and are willing to pay for that convenience
You need advanced AI features (anomaly detection, forecasting) out of the box
Your customers demand vendor support and you want a recognisable brand
You’re in a high-compliance industry (healthcare, fintech) where vendor accountability matters
Your engineering team is small and can’t maintain a platform long-term

Sisense makes sense when the cost is a rounding error in your budget and operational simplicity is worth the premium.

Choose D23.io / Superset If:

You have <500 concurrent embedded users and want to control costs
Your engineering team can own and operate a platform (or will grow into that capacity)
You want flexibility and customisation without vendor lock-in
You’re building a long-term analytics capability and want to own the asset
You’re in Australia or prefer working with local partners (D23.io is Sydney-based)
You want transparency and predictability in your analytics spend
You’re scaling from seed to Series-B and need to preserve cash while building a competitive feature

D23.io / Superset makes sense when you have engineering capacity and want to control your destiny.

Hybrid Approach

Some companies use both: Superset for internal analytics (owned and operated by the team) and Sisense for customer-facing embedded analytics (white-label, managed). This balances cost and control. However, it adds complexity.

How to Calculate Your True ROI {#calculate-roi}

Embedded analytics isn’t free. You need to justify the cost against the revenue it generates.

The Embedded Analytics ROI Framework

Step 1: Quantify the Revenue Impact

Embedded analytics typically drives three revenue outcomes:

Higher customer retention: Customers with access to analytics churn less. If embedded analytics reduces churn by 5%, and your average customer lifetime value is $50,000, that’s $2,500 per customer retained. Across 100 customers, that’s $250,000 in retained revenue.
Faster time-to-value: Customers see the value of your product faster and expand their usage. This might increase average contract value by 10–20%.
Reduced support burden: Customers self-serve analytics questions instead of asking your support team. If you save 10 hours/week of support time, that’s $10,000–$20,000/year (at $50–$100/hour fully loaded cost).

Step 2: Calculate Your Analytics Cost

Use the scenarios above. For Sisense, include licensing, implementation, support, and annual increases over 3 years. For D23.io, include implementation and ongoing infrastructure/support.

Step 3: Compare Cost to Revenue Impact

Example:

Embedded analytics cost (Sisense): $250,000 over 3 years
Retained revenue (5% churn reduction): $250,000
Expanded contract value (15% increase): $150,000
Reduced support costs: $30,000
Total revenue impact: $430,000
ROI: 1.72x (positive, but tight)

If you can’t articulate a revenue impact that’s at least 1.5–2x the cost, embedded analytics is a feature, not a business driver. That’s fine—many companies ship it for competitive parity—but be honest about the economics.

Tools to Help You Model This

PADISO’s AI agency ROI framework provides a structured approach to calculating technology ROI. Apply the same discipline to embedded analytics: quantify the revenue, compare to cost, and decide.

Making the Decision: A Framework {#decision-framework}

Here’s a decision tree to guide your choice.

Question 1: How Many Concurrent Embedded Users Do You Have (or Expect in Year 1)?

<100 users: D23.io / Superset is more economical
100–500 users: Evaluate both; D23.io likely wins on cost
>500 users: Sisense becomes competitive; evaluate based on other factors

Question 2: Do You Have Engineering Capacity to Own a Platform?

Yes (2+ engineers available): D23.io / Superset is viable and cost-effective
No (small team, stretched thin): Sisense’s managed model is worth the premium

Question 3: How Important Is Operational Control?

Critical (you want to own the roadmap, customise deeply): D23.io / Superset
Less important (you want to focus on product, not infrastructure): Sisense

Question 4: What’s Your Cash Runway and Profitability Timeline?

Tight cash, need to preserve burn: D23.io / Superset (lower recurring cost)
Well-funded, profitability is 3+ years away: Sisense (pay for convenience)

Question 5: Are You in Australia or Prefer Local Partners?

Yes: D23.io is a Sydney-based firm with deep Australian SaaS experience
No: Sisense has global support; D23.io still works remotely

Recommended Decision Matrix

Scenario	Recommendation	Rationale
Seed-stage, <100 users, tight cash	D23.io	Own the asset, control costs, preserve runway
Series-A, 100–300 users, engineering team	D23.io	Scale efficiently, own the platform, strong unit economics
Series-B+, 300+ users, well-funded	Sisense or D23.io	Both viable; choose based on engineering capacity and control preference
Enterprise, 500+ users, zero operational overhead required	Sisense	Managed service worth the premium; vendor accountability matters
High-compliance industry (healthcare, fintech)	Sisense	Vendor support and accountability reduce audit risk

Practical Implementation Guidance {#practical-implementation}

If you’re leaning toward D23.io / Superset, here’s what to expect in the engagement.

Pre-Engagement Checklist

Data readiness: Ensure your data warehouse or data lake is accessible and reasonably clean. Superset queries your data; garbage in, garbage out.
Identity provider integration: Have your SSO provider (Okta, Azure AD, Auth0) details ready. D23.io will integrate Superset with your identity system.
Stakeholder alignment: Identify 2–3 internal stakeholders (product, customer success, analytics) who’ll define dashboard requirements. D23.io will work with them to design dashboards.
Infrastructure access: Provide AWS or cloud access so D23.io can deploy Superset. They’ll follow your security and compliance requirements.
Team availability for training: Allocate 2–3 engineers to attend training and learn to maintain Superset post-launch.

The Six-Week Engagement Arc

The $50K D23.io consulting engagement typically follows this rhythm:

Week 1–2: Discovery and Design

D23.io reviews your data stack, user requirements, and performance expectations
They propose a Superset architecture (single-tenant, multi-tenant, hybrid)
You approve the design and data sources

Week 2–3: Infrastructure and Security

D23.io provisions Superset on your cloud (AWS, GCP, Azure)
They configure databases, caching, and backups
They implement security controls (encryption, network isolation, audit logging)

Week 3–4: Semantic Layer and SSO

D23.io integrates your identity provider (SSO)
They build or integrate a semantic layer so business users can query data without SQL
They test end-to-end user flows

Week 4–5: Dashboard Design and Build

D23.io works with your stakeholders to design 10–15 production dashboards
They build dashboards in Superset, test performance, and optimise
You review and provide feedback

Week 5–6: Training and Handoff

D23.io trains your engineering team on Superset architecture, maintenance, and troubleshooting
They document everything (architecture diagrams, runbooks, FAQ)
They hand off the platform to you

Post-engagement: You own and operate Superset. D23.io is available for optional support.

Post-Engagement Support Options

After handoff, you have choices:

Go it alone: Your team maintains Superset. Cost: $0/month (plus your engineering time)
Retainer support: D23.io provides on-call support, quarterly reviews, and proactive maintenance. Cost: $5,000–$10,000/month
Hybrid: Your team handles day-to-day; D23.io handles major upgrades and troubleshooting. Cost: $2,000–$5,000/month

Leveraging PADISO’s Expertise for Your Decision {#padiso-expertise}

If you’re evaluating embedded analytics as part of a broader platform modernisation or AI transformation, PADISO can help.

How PADISO Supports Analytics Decisions

PADISO is a Sydney-based venture studio and AI digital agency that partners with founders and operators to ship AI products, automate operations, and build scalable platforms. We’ve worked with dozens of SaaS companies on embedded analytics decisions, and we have deep expertise in:

AI Strategy & Readiness: Understanding where embedded analytics fits in your product roadmap and competitive positioning
Platform Design & Engineering: Architecting analytics platforms that scale with your business
Agentic AI + Apache Superset: Integrating AI agents (like Claude) with Superset so customers can query dashboards conversationally
AI Automation Agency Services: Automating analytics workflows and data pipelines

If you’re considering D23.io / Superset, we can help you evaluate the fit, design the architecture, and support the implementation. If you’re considering Sisense, we can help you negotiate terms and plan for long-term cost management.

PADISO’s Fractional CTO and Co-Build Model

Many founders and operators we work with don’t have a full-time CTO or engineering leader. PADISO offers CTO as a Service and co-build partnerships for teams building analytics capabilities. This means:

Fractional CTO leadership for technical strategy and vendor evaluation
Co-build support to implement and optimise your analytics platform
Ongoing advisory as your analytics needs evolve

This is particularly valuable for seed-to-Series-B founders who need technical credibility and execution support but can’t yet hire a full-time CTO.

Addressing Compliance and Security {#compliance-security}

If you’re in a regulated industry or your customers demand SOC 2 or ISO 27001 compliance, this affects your analytics choice.

Sisense and Compliance

Sisense is SOC 2 Type II certified and supports compliance requirements. However, you’re still responsible for:

Configuring Sisense securely (network isolation, encryption, access controls)
Auditing user access and data flows
Maintaining audit logs
Managing data residency and privacy (GDPR, CCPA, etc.)

Sisense’s vendor support helps, but compliance is a shared responsibility.

Superset and Compliance

Apache Superset is open-source, so compliance is entirely your responsibility. However, this means:

You have full visibility into the code and security controls
You can implement custom controls to meet your requirements
You own the audit trail and can prove compliance

For SOC 2 or ISO 27001 audits, you’ll need to document Superset’s security architecture, access controls, and audit logging. This requires engineering effort, but it’s achievable.

If compliance is critical, PADISO’s Security Audit service can help you implement SOC 2 or ISO 27001 controls across your analytics platform (and broader technology stack). We use Vanta for audit-readiness and can help you pass audits efficiently.

Real-World Lessons from Australian SaaS Vendors {#real-world-lessons}

We’ve worked with several Australian SaaS companies evaluating embedded analytics. Here are the key lessons.

Lesson 1: Start with Your Business Model

One Sydney fintech founder we worked with spent three months evaluating Sisense before realising their embedded analytics strategy didn’t align with their revenue model. They were selling to institutional customers who didn’t care about dashboards; they cared about APIs and data exports. Embedded analytics was a distraction.

Lesson: Before evaluating platforms, validate that embedded analytics is a revenue driver or retention lever for your business. If it’s just table stakes, choose the cheapest option and move on.

Lesson 2: Engineering Capacity Is the Real Constraint

A Series-A SaaS company we advised chose Sisense because they thought it would be “hands-off.” Within six months, they needed custom integrations, security hardening, and performance tuning. They ended up hiring a dedicated analytics engineer anyway, making Sisense’s premium pricing feel wasteful.

Lesson: The choice between Sisense and Superset isn’t really about the platform—it’s about your engineering team’s capacity and growth trajectory. If you’ll hire analytics engineers anyway, Superset gives you more control.

Lesson 3: Data Quality Is the Hidden Bottleneck

Multiple companies we’ve worked with shipped embedded analytics only to discover their underlying data was messy. Dashboards were slow, inaccurate, or both. They spent more time fixing data than building features.

Lesson: Before investing in embedded analytics, invest in data quality. A clean data warehouse makes any analytics platform shine. A messy one makes all platforms fail.

Lesson 4: Multi-Tenancy Is Harder Than It Looks

One mid-market SaaS company chose Superset thinking they’d implement multi-tenancy (each customer sees only their data) easily. It took three months of engineering effort to get right. Sisense’s built-in multi-tenancy would have saved them time, but they still would have needed custom work.

Lesson: Multi-tenancy is non-trivial regardless of platform. Budget for it explicitly, and consider whether you really need it. Single-tenant deployments (one Superset instance per customer) are simpler but more expensive to operate.

The Broader Context: AI and Agentic Analytics {#ai-agentic-analytics}

Embedded analytics is evolving. The next frontier is agentic AI—autonomous agents that query data and generate insights without human intervention.

Agentic AI + Embedded Analytics

PADISO’s guide to agentic AI and Apache Superset shows how to integrate AI agents (like Claude or GPT-4) with Superset so customers can ask natural language questions and get dashboard answers automatically.

This changes the economics:

Sisense: Has AI features, but they’re vendor-managed and add cost
Superset: Open-source, so you can integrate any AI model (Claude, GPT-4, open-source LLMs) and own the integration

For forward-thinking companies, Superset’s flexibility is a significant advantage. You can build agentic analytics capabilities that Sisense customers would pay premium prices for.

AI Automation and Analytics Workflows

Beyond dashboards, AI automation is transforming analytics workflows. Think:

Automated data quality checks and alerts
AI-generated insights and anomaly detection
Scheduled reports and alerts sent to customers automatically
Predictive analytics and forecasting

Both Sisense and Superset support these, but Superset’s open-source nature makes it easier to integrate custom AI workflows.

Next Steps and Action Plan {#next-steps}

If you’re evaluating embedded analytics, here’s your action plan.

Immediate (This Week)

Quantify your embedded analytics opportunity: How many concurrent users will you have in year 1? How much revenue impact do you expect? Use the ROI framework above.
Assess your engineering capacity: How many engineers can you allocate to own and operate an analytics platform? Be honest.
Define your requirements: What dashboards do customers need? What data sources? What performance requirements?

Short-term (This Month)

Request Sisense quotes: Contact Sisense sales with your user count and requirements. Get three quotes (different support tiers). Review Sisense pricing data to sanity-check their quotes.
Evaluate Superset options: Talk to D23.io about a fixed-fee implementation. Understand what’s included and what’s not.
Talk to customers: Ask 5–10 customers whether embedded analytics would influence their renewal or expansion decision. Quantify the revenue impact.

Medium-term (Next Quarter)

Run a pilot: If you’re leaning toward Superset, do a small pilot with D23.io (2–3 dashboards, 2–week engagement, $10K–$15K). Validate the approach before committing to a full implementation.
Build a business case: Document the cost, revenue impact, and strategic rationale for your choice. Share with your board or leadership team.
Plan for long-term ownership: Regardless of platform, plan how you’ll maintain and evolve embedded analytics over 3–5 years. Who owns it? How do you fund it?

If You Choose D23.io / Superset

Prepare your data: Clean and organise your data warehouse. Build or integrate a semantic layer. This is 50% of the work.
Allocate a project lead: Identify an engineer who’ll be the primary contact during the engagement and will own Superset long-term.
Plan for training: Block time for your team to learn Superset during the engagement. This is critical for long-term success.

If You Choose Sisense

Negotiate terms: Sisense’s pricing is flexible. Push back on per-user costs, annual increases, and implementation fees. You have leverage.
Plan for integration: Sisense integrations are non-trivial. Budget $20K–$50K for custom work and plan for 3–4 month implementation.
Establish governance: Define who manages Sisense (vendor, internal team, hybrid). Document processes for adding dashboards, managing users, and handling support requests.

Conclusion: The Real Cost of Embedded Analytics {#conclusion}

Sisense and D23.io / Superset serve different needs at fundamentally different price points.

Sisense is the premium, managed option. You pay for convenience, vendor support, and operational simplicity. Expect $40K–$200K+ annually depending on scale. It makes sense if embedded analytics is a critical competitive feature and you have budget to spare.

D23.io / Superset is the cost-controlled, ownership-focused option. You pay a fixed fee for implementation ($50K) and own the platform long-term. Expect $10K–$35K annually in ongoing costs. It makes sense if you have engineering capacity and want to control your destiny.

The choice hinges on three variables:

User scale: How many concurrent embedded users do you have or expect?
Engineering capacity: Can your team own and operate a platform?
Strategic importance: Is embedded analytics a revenue driver or table stakes?

If you’re a seed-to-Series-B founder or an operator at a mid-market company modernising your analytics stack, PADISO can help you evaluate both options, design the right architecture, and execute the implementation. We have deep experience with Superset, agentic AI, and platform engineering. We also understand the broader context—how embedded analytics fits into your product strategy, AI roadmap, and long-term technology vision.

Reach out if you’d like to discuss your embedded analytics strategy. We’re happy to help you make the right choice and execute it efficiently.

Appendix: Key Metrics and Formulas {#appendix}

Embedded Analytics Cost Calculator

Sisense Annual Cost:

(Concurrent Users × $80–$150/user/year) + Implementation ($25K–$50K) + Support ($10K–$30K) + Annual Increase (10–15% YoY)

D23.io / Superset Cost:

Year 1: $50K (implementation) + Infrastructure ($5K–$20K) + Optional Support ($0–$15K)
Year 2+: Infrastructure ($5K–$20K) + Optional Support ($0–$15K)

ROI Formula

ROI = (Retained Revenue + Expanded Contract Value + Cost Savings - Analytics Cost) / Analytics Cost

Target: ROI > 1.5x over 3 years.

Break-Even User Count

At what user count does Sisense become cheaper than Superset?

(Users × $100/user/year) = $50K (Superset) + $15K/year (ongoing)
(Users × $100) = $65K
Users = 650

At 650+ concurrent users, Sisense’s per-user model becomes competitive with Superset’s fixed-fee approach (assuming $100/user/year average).

Sisense vs D23.io: Embedded Analytics Cost Reality Check

Sisense vs D23.io: Embedded Analytics Cost Reality Check

Table of Contents

Executive Summary: The Cost Gap {#executive-summary}

What You’re Actually Paying For {#what-youre-paying-for}

Sisense: The Premium Vendor Model

D23.io / Managed Superset: The Open-Source Alternative

Sisense Pricing Breakdown {#sisense-pricing-breakdown}

Published Sisense Tiers (2026)

What Drives Sisense Costs Up

Real Sisense Customer Examples

D23.io and Managed Superset: The Alternative {#d23io-managed-superset}

The $50K D23.io Engagement: What’s Inside

Ongoing Costs After D23.io Handoff

Why D23.io Uses Superset

Real-World Cost Scenarios {#real-world-cost-scenarios}

Scenario 1: Series-A SaaS, 100 Customers, 5 Concurrent Users Per Customer

Scenario 2: Seed-Stage Startup, 10 Customers, 2 Concurrent Users Per Customer

Scenario 3: Mid-Market Enterprise, 500 Customers, 10 Concurrent Users Per Customer

Hidden Costs and Implementation Reality {#hidden-costs}

Sisense Hidden Costs

D23.io / Superset Hidden Costs

When Each Platform Makes Sense {#when-each-platform-makes-sense}

Choose Sisense If:

Choose D23.io / Superset If:

Hybrid Approach

How to Calculate Your True ROI {#calculate-roi}

The Embedded Analytics ROI Framework

Tools to Help You Model This

Making the Decision: A Framework {#decision-framework}

Question 1: How Many Concurrent Embedded Users Do You Have (or Expect in Year 1)?

Question 2: Do You Have Engineering Capacity to Own a Platform?

Question 3: How Important Is Operational Control?

Question 4: What’s Your Cash Runway and Profitability Timeline?

Question 5: Are You in Australia or Prefer Local Partners?

Recommended Decision Matrix

Practical Implementation Guidance {#practical-implementation}

Pre-Engagement Checklist

The Six-Week Engagement Arc

Post-Engagement Support Options

Leveraging PADISO’s Expertise for Your Decision {#padiso-expertise}

How PADISO Supports Analytics Decisions

PADISO’s Fractional CTO and Co-Build Model

Addressing Compliance and Security {#compliance-security}

Sisense and Compliance

Superset and Compliance

Real-World Lessons from Australian SaaS Vendors {#real-world-lessons}

Lesson 1: Start with Your Business Model

Lesson 2: Engineering Capacity Is the Real Constraint

Lesson 3: Data Quality Is the Hidden Bottleneck

Lesson 4: Multi-Tenancy Is Harder Than It Looks

The Broader Context: AI and Agentic Analytics {#ai-agentic-analytics}

Agentic AI + Embedded Analytics

AI Automation and Analytics Workflows

Next Steps and Action Plan {#next-steps}

Immediate (This Week)

Short-term (This Month)

Medium-term (Next Quarter)

If You Choose D23.io / Superset

If You Choose Sisense

Conclusion: The Real Cost of Embedded Analytics {#conclusion}

Appendix: Key Metrics and Formulas {#appendix}

Embedded Analytics Cost Calculator

ROI Formula

Break-Even User Count

Further Reading and Resources {#further-reading}