Cloud-Native BI in the Age of Live Data and Everyday AI
Once, choosing a BI tool meant picking your favorite flavor of dashboard.
Today, it’s about deciding how close to the warehouse you want to live.
Both Astrato Analytics and Sigma Computing were born in the cloud-native era. They run directly on top of modern data clouds like Snowflake, ditching the extract-and-refresh cycles that defined legacy BI.
But by 2026, the question has changed from “Who can query my data fastest?” to “Who can make my data useful, safe, and actionable for everyone?”
Below, we examine where the two platforms now stand – feature by feature, layer by layer – to help BI leaders navigate a maturing market that’s moving from visualization to execution.
☁️ Cloud-Native Architecture: the pushdown era matures
Every modern BI tool now claims to be “cloud-native”.
But for data leaders, the real question isn’t where the tool lives – it’s how it behaves when the warehouse is doing the heavy lifting. Architecture defines your data latency, cost visibility, and governance model more than any feature ever will.
Both Astrato and Sigma are built on pushdown SQL, meaning queries execute directly inside your data warehouse instead of in an in-memory engine.
That alone puts them years ahead of legacy tools still duplicating and preloading data. But as warehouse-native BI matures, the nuance lies in how each tool manages that pushdown pipeline – and who pays the compute bill for every query.
⚡ Astrato → Transparent, Real-Time, Warehouse-Safe
Astrato is a pure pushdown architecture. Every query runs directly against the live warehouse – no staging tables, no silent refresh jobs, no hidden compute surprises at 3 a.m.
Caching happens transparently through Snowflake’s native result cache, not in an external layer.
Because Astrato never moves or materializes data, your governance, security, and lineage all remain under Snowflake’s control. Row- and column-level policies apply automatically, and queries are always auditable inside Snowflake history – the kind of transparency that keeps both IT and Finance happy.
The outcome: true “live” analytics without the cloud compute bloat that can quietly inflate bills.
⚙️ Sigma → Dynamic Tables, Dynamic Costs
Sigma’s architecture is also pushdown-based, but its performance strategy leans on Dynamic Tables and materialization.
When enabled, Sigma precomputes data snapshots for faster dashboard loads. It’s effective for high-volume aggregates or multi-join dashboards, but every refresh triggers compute jobs inside Snowflake.
For small deployments, the benefit of introducing dynamic tables is negligible, and for smaller datasets, counter-productive. At scale, refresh schedules and concurrency quickly translate into predictable but non-trivial compute spend.
Data freshness depends on how often those tables refresh, meaning “real-time” becomes “as fast as your cron job”. This can cause delays in decision making.
🧩 The short of it for BI Leaders
- Cost predictability: Astrato’s direct-query model lets you map every dashboard action to Snowflake credit usage. Sigma’s Dynamic Tables can blur that visibility under shared compute workloads.
- Governance inheritance: Astrato’s zero-replication model inherits warehouse security end to end. Sigma’s materialization layer adds another governance surface to monitor.
- Operational agility: With Astrato, schema changes or new joins are instantly available, no rebuilds or refresh waits. Sigma offers performance stability, but at the expense of spontaneity.
💡Verdict: Both tools are cloud-native. But Astrato represents the “mature pushdown era” – transparent, governed, and cost-aware – while Sigma sits in the performance-optimization camp, fast and familiar for spreadsheet users but with operational overhead that BI leaders will want to watch.
🎯 Guided Analytics: from dashboards to data apps
Five years ago, dashboards were the finish line of BI.
Today, they’re the starting point.
For BI leaders, the question has shifted from “Can my team build dashboards?” to “Can my business act on them in real time?”.
Both Astrato and Sigma deliver analytics in the cloud, but they approach guided experiences from opposite ends: Astrato from design-first app-thinking; Sigma from spreadsheet-first exploration.
⚡ Astrato → Dashboards That Do Things
Astrato treats every dashboard as a living application. Charts aren’t passive – they listen, react, and execute.
AI Insights objects constantly scan the sheet’s context, surfacing anomalies, summaries, and outliers automatically.
Then come Actions – Astrato’s differentiator. Users can trigger workflows directly from the analytics layer: write data back to Snowflake, approve budget changes, send Slack alerts, or update CRM records – all without leaving the page.
Design-wise, Astrato borrows from modern product UX, not traditional BI. Pixel-perfect control, branded themes, and white-label embedding turn it into a customer-facing analytics layer that looks like part of your product, not an iframe add-on.
In business terms, this shifts analytics from read-only reporting to executional intelligence – shrinking the gap between insight and action from days to seconds.
🧮 Sigma → Spreadsheet Meets Dashboard
Sigma’s heritage is its strength – and its ceiling. The familiar spreadsheet grid lowers the learning curve for analysts who think in formulas and pivot tables. It’s great for rapid data exploration and ad-hoc modeling.
But that same grid limits presentation. Dashboards are cell-based, layout flexibility is constrained, and cross-visual filtering must be wired manually.
Sigma’s new Workbook Apps bring some interactivity – forms, inputs, light workflows – yet the experience still revolves around tables, not storytelling.
For internal analysis teams, that’s fine. For organizations seeking unified, customer-facing analytics experiences, it’s a hurdle.
🧩 The short of it for BI Leaders
- Adoption vs. dependency: Astrato’s guided UX and no-code Actions broaden usage beyond analysts. Sigma stays analyst-centric.
- Execution speed: Astrato shortens the insight-to-action loop. Sigma excels at discovery but relies on external tools for follow-through.
- Experience control: Astrato’s pixel-perfect, brandable dashboards suit client portals and OEM models. Sigma’s spreadsheet DNA favors internal exploration over external presentation.
💡Verdict: Astrato guides users through analytics, Sigma keeps them inside spreadsheets.
🧠 AI & Insights: explainable intelligence you can trust
For BI leaders, AI is no longer about wow moments – it’s about how:
- How is the insight generated?
- How does it stay within governance rules?
- And how do you make AI useful without losing control of your data?
Both Astrato and Sigma embed AI directly into analytics workflows, but their philosophies diverge.
Astrato focuses on explainable, governed automation. Sigma emphasizes configurable AI within its semantic layer. One democratizes AI for business users, the other empowers technical teams.
⚡ Astrato → Governed, Explainable, and Context-Aware
Astrato’s AI lives where your data lives – in the warehouse.Its AI Insights objects are context-aware, reading the active filters, roles, and dashboard scope before generating summaries or anomaly detection.
That means the same dashboard can surface revenue margin anomalies to finance, customer churn risks to marketing, and forecast variances to operations – all from the same governed dataset.
Under the hood, Astrato routes queries through Snowflake Cortex, OpenAI, or Gemini, managed via the AI Provider Manager.
Admins can mix and match models, fine-tune access, and define prompt templates without any coding.
Key capabilities:
- One-click AI widgets that summarize charts or entire dashboards
- Role-aware responses based on warehouse security context
- Prompt templates with variable inputs for domain-specific use cases
- No-code exclusions to block sensitive or PII fields
- Execution inside Snowflake via Cortex SQL, so no data ever leaves the warehouse
Every generated insight can show why it appeared – listing the dataset, applied filters, and model type used. It’s AI you can audit, not just admire.
In short, Astrato turns AI into a governed partner for insight generation. It adds context and prevents chaos.
🧮 Sigma → Configurable Power, Developer-Heavy Setup
Sigma’s approach embeds AI deeper into its semantic layer through YAML-defined logic and configuration files.
It’s precise and powerful for engineering-led teams who want to define AI behavior declaratively, but it adds complexity for non-technical roles.
To set up AI prompts or contextual summaries, developers need to write YAML that maps datasets, joins, and metric definitions. The outcome is deterministic – great for reproducibility, less great for accessibility.
Sigma’s AI features can also connect with Snowflake Cortex or external models, but often require external API setup and key management. Tracing how an insight was generated typically means checking the configuration files, not the UI.
It’s AI for data engineers, not for business operators.
🧩 The short of it for BI Leaders
- Governance first: Astrato keeps AI execution inside the data boundary. Sigma extends AI through code, requiring developer governance.
- Accessibility: Astrato’s no-code AI Insights let anyone explore data safely. Sigma’s setup favors technical teams.
- Transparency: Astrato explains every result. Sigma requires configuration-file digging to reconstruct logic.
💡Verdict: Astrato democratizes AI without losing control. Sigma’s AI remains powerful, but DevOps-heavy.
⚙️ Self-Service BI: No-Code vs Low-Code
Every BI leader knows the paradox: the more powerful a tool becomes, the fewer people can use it.
Self-service is supposed to fix that, but most platforms still lean too far one way: either oversimplified and rigid, or open-ended and intimidating.
Astrato and Sigma represent two ends of that spectrum. Both run on governed, push-down models. But Astrato’s design language feels closer to a modern SaaS product. Sigma’s experience still speaks “spreadsheet”.
⚡ Astrato → BI for Everyone
Astrato treats self-service as a human-centered design challenge, not a data-engineering problem.
Its no-code measure builder converts natural business logic (“profit margin by region vs last quarter”) into SQL automatically.
AI Insights guide users toward the right visual for the data pattern they’re exploring. And because every chart inherits its definitions from the governed semantic layer, users can’t accidentally “break the truth”.
A marketing manager can filter campaigns, build a quick cohort chart, and schedule a PowerPoint export – all in minutes, without an IT ticket.
Scheduled reporting now comes built-in, bridging the gap between daily dashboards and monthly board decks.The result: business users actually use it.
Analysts spend less time rebuilding dashboards and more time refining the model: a virtuous loop of adoption and accuracy.
🧮 Sigma → Analyst-First Flexibility
Sigma still champions the analyst persona.
If you think in formulas, you’ll feel at home. Its spreadsheet interface makes ad-hoc exploration fast, flexible, and powerful.
But for non-technical roles, the very thing analysts love – open-ended formula syntax – becomes a learning cliff. Exploration is empowering when you speak SQL, but confusing when you don’t.
Sigma’s strength lies in enabling technical users to self-serve without waiting on IT.
Its weakness is scaling beyond that circle. For orgs where analysts build and everyone else consumes, Sigma fits beautifully. For those chasing true cross-functional adoption, it can feel like the door never fully opens.
🧩 The short of it for BI Leaders
- Adoption economics: Astrato’s consumer-grade UI expands the active-user base. Sigma deepens power-user engagement.
- Governed autonomy: Astrato’s no-code layer ensures every user query honors warehouse governance. Sigma offers freedom, but assumes formula fluency.
- Output velocity: Astrato collapses “ask → analyze → present” into one flow with built-in reporting. Sigma still relies on exports and external decks for storytelling.
💡Verdict: Astrato turns data consumers into creators. Sigma keeps its sweet spot with power users.
🔁 Data Apps & Writeback: the Execution Layer
The next frontier of BI isn’t prettier charts – it’s shorter loops between insight and action.
For decades, dashboards told users what happened. Now, data apps tell them what to do next – and let them do it right there.
Both Astrato and Sigma support interaction and input. But their philosophies differ: Astrato turns dashboards into governed applications, Sigma adds inputs inside its analytical grid.
⚡ Astrato → From Insight to Action
Astrato has evolved from visualization to execution layer.
Through its Actions framework, users can update warehouse tables, trigger approvals, or push data to external systems like Slack, ServiceNow, or Salesforce – all from within the dashboard.
Each action runs through the live warehouse connection, maintaining lineage, auditability, and role-aware permissions defined in Snowflake or Databricks.
You can log every event (who approved, when, what data changed), giving IT full visibility without removing business agility.
Typical workflows now include:
- Multi-step financial approvals (budget changes, accrual adjustments)
- Inventory and demand planning (restock thresholds, vendor updates)
- Customer operations (status changes, churn follow-ups)
In other words, the warehouse remains the single source of truth, Astrato simply gives it a user interface.
🧮 Sigma → Quick Edits, Not Workflows
Sigma’s Input Tables allow inline data entry and lightweight writeback – ideal for quick corrections, annotations, or scenario modeling.
It’s fast, intuitive, and governed by role permissions, but it stops short of full workflow orchestration.
There’s no native framework for multi-step approvals, branching logic, or cross-system triggers. For production-grade operational apps, teams typically integrate external automation tools or custom scripts.
That makes Sigma great for ad-hoc modeling, but less so for governed, repeatable business processes.
🧩 The short of it for BI Leaders
- Scope of action: Astrato turns dashboards into applications with workflow logic and external triggers. Sigma focuses on spreadsheet-style inputs for analysts.
- Governance: Astrato maintains full warehouse lineage and audit logs. Sigma’s inputs live within its app layer.
- Business impact: Astrato enables operational BI – decisions made and executed in one place. Sigma remains best for analytical iteration.
💡Verdict: Astrato bridges analytics and operations. Sigma still stops at the insight.
📊 Reporting: from snapshots to storytelling
For most organizations, analytics ends when the slide deck starts.
It’s the most expensive gap in BI, where analysts spend hours copying screenshots into PowerPoint just to brief leadership on “what the dashboard says”.
In 2026, that gap is closing.
Both Astrato and Sigma can deliver scheduled reports, but only one treats reporting as a communication experience, not an export function.
⚡ Astrato → Boardroom-Ready Exports
Astrato has turned reporting into an extension of live analytics, not a static afterthought.
With its PowerPoint Export feature, any dashboard can become a branded slide deck, complete with live charts and refreshed data snapshots, in seconds.
Add scheduled and cyclical deliveries and full support for Excel and PDF formats, plus AI-generated summaries that automatically highlight anomalies or trends, and Astrato now covers the “last mile” of analytics: insight communication.
A report isn’t just a picture of data: it’s a narrative, timestamped, consistent, and role-aware.
The same governed metrics and permissions that apply in dashboards carry into every export.
For BI leaders, this means real-time executive reporting without the copy-paste bottleneck – and with zero risk of outdated or inconsistent numbers.
🧮 Sigma → Spreadsheet Snapshots
Sigma’s reporting remains grounded in its workbook designer.
Users can export tables and visuals to PDF, CSV, or Excel, or schedule them for delivery to email or Slack. It’s simple and reliable – great for internal snapshots or quick distribution.
But Sigma’s exports are still static: no pixel-level composition, no dynamic storytelling, and limited flexibility for customer-facing presentations.
The experience mirrors the spreadsheet itself – functional, not narrative.
🧩 The short of it for BI Leaders
- Narrative delivery: Astrato transforms dashboards into ready-to-share presentations with live visuals and AI summaries. Sigma focuses on static exports.
- Governance and trust: Astrato’s reports inherit live metrics and role permissions, ensuring one source of truth. Sigma exports require manual oversight.
- Efficiency: Astrato eliminates the manual “deck-building” process entirely. Sigma maintains separate creation and communication steps.
💡Verdict: Astrato tells the story. Sigma shows the numbers.
🏗️ Data Modeling & Reusability: the semantics of trust
Ask any BI leader where analytics goes wrong, and you’ll hear the same answer: too many versions of the truth.
Every department builds its own KPIs, metrics drift, and reconciliation meetings eat more time than analysis.
That’s why the semantic layer – the shared definition of business logic – has become the new battleground for modern BI.
Both Astrato and Sigma tackle it differently: one visually, one through code.
⚡ Astrato → Visual Modeling, Real Reuse
Astrato treats modeling as a governed, visual experience, not a side project for data engineers.
Its modeling canvas supports multi-fact schemas, meaning you can connect multiple tables (sales, customers, regions, products) without flattening them into a single dataset.
Automatic join suggestions simplify relationships, while color-coded validation (green = perfect, amber = check) helps teams spot issues before they break dashboards.
At scale, this becomes a collaboration layer between data engineers (who define structure) and analysts (who build dashboards).
Key advantages include:
- One governed data model feeding multiple workbooks – no duplication or rework.
- AI-assisted measure generation in plain language (“Show me revenue per customer over time”).
- Integration with dbt and other semantic layers, so definitions built upstream flow straight into Astrato.
The result: consistency, reuse, and confidence. When everyone sees the same number, discussions move from ‘is it right?’ to ‘what do we do about it?’.
🧮 Sigma → Flat and Familiar
Sigma’s modeling philosophy is straightforward: each workbook has its own dataset.
That simplicity makes setup easy and exploration fast: analysts can define relationships or metrics directly in the workbook.
But at scale, that approach introduces friction.
Duplication creeps in as teams rebuild the same joins and calculations across dashboards. Complex joins or multi-fact logic often require manual SQL or YAML editing.
Sigma’s Data Models feature has improved governance, allowing reusable datasets and shared logic, but the system still orbits around individual workbooks rather than a centralized semantic layer.
It’s flexible, just not unified.
🧩 The short of it for BI Leaders
- Governance at scale: Astrato’s visual, reusable models create a single semantic layer that feeds every dashboard. Sigma’s workbook-based design risks metric drift.
- Collaboration: Astrato’s modeling canvas bridges technical and business roles. Sigma stays within the analyst workflow.
- Integration: Astrato connects seamlessly to dbt and other upstream semantic tools. Sigma’s YAML-based approach favors engineering teams comfortable with code.
💡Verdict: Astrato’s reusable models scale with governance. Sigma’s simplicity suits smaller, analyst-led teams.
🔐 Governance & Compliance: trust that scales
Governance is the invisible foundation of every analytics platform. BI leaders rarely get credit for it, until something goes wrong.
The challenge isn’t adding controls. It’s making them automatic, auditable, and inherited.
Both Astrato and Sigma enforce warehouse-level security, but Astrato extends it across every layer of the experience, including embedded apps, writeback, and AI.
⚡ Astrato → Governance by Design
Astrato’s architecture treats governance as a first-class citizen:
- Full inheritance of Snowflake’s Row Access and Column Masking policies
- Configurable JWT and SSO propagation for multi-tenant SaaS portals
- SOC 2 Type II and ISO 27001 certification, proving enterprise compliance
Everything from role-based visibility to field-level permissions flows directly from the warehouse, meaning your compliance team doesn’t need to reinvent policies in a BI layer.
🧮 Sigma → Good, Not Granular
Sigma relies on Snowflake’s roles and warehouse permissions for baseline security. That’s sufficient for departmental analytics, but less flexible for SaaS scenarios or enterprises managing thousands of external viewers.
Fine-grained masking and tenant-level segregation require additional configuration or separate workspaces. Audit logs exist but remain manually exportable.
🧩 The short of it for BI Leaders
- Inheritance: Astrato enforces warehouse policies automatically. Sigma requires parallel setup.
- Auditability: Astrato’s logs feed directly into your monitoring stack. Sigma’s visibility is limited to platform exports.
- Multi-tenancy: Astrato is ready for embedded and OEM models. Sigma suits departmental analytics.
💡Verdict: Astrato feels enterprise-ready. Sigma satisfies departmental needs.
📈 Scalability & Performance: speed is nothing without control
At enterprise scale, performance isn’t just about query time – it’s about predictability.
When thousands of users hit dashboards at once, how a BI tool manages compute, caching, and concurrency determines both your experience and your Snowflake bill.
⚡ Astrato → Speed That Scales With You
Astrato’s architecture stays deliberately transparent: every query runs directly in your warehouse, using Snowflake’s compute, cache, and role-based permissions. There are no shadow tables, middle layers, or surprise jobs running in the background.
In a real customer benchmark, 6 billion+ rows returned in 1.3 seconds on a medium Snowflake warehouse – and that result wasn’t cached locally, but leveraged Snowflake’s native result cache.
Because Astrato avoids replication, performance scales linearly with warehouse size – you can tune cost and speed the same way you already manage Snowflake workloads.
Key points for BI leaders:
- Transparent compute: all usage visible in Snowflake billing.
- Predictable costs: no extra storage, no double-counted compute.
- Native caching: governed and secure, using your warehouse cache.
- Elastic scalability: performance improves simply by resizing your warehouse.
Astrato’s design means fewer knobs to tune and fewer bills to explain.
📊 Sigma → Smart Performance, With Maintenance
Sigma’s Dynamic Tables bring an intelligent caching layer that can accelerate dashboards for teams running frequent queries on large datasets.
They pre-compute and store query results inside Snowflake, trading live freshness for speed.
The benefit: dashboards render fast, even with heavy concurrency.
The cost: compute runs on refresh, not on demand, so every scheduled update consumes Snowflake credits whether someone views the dashboard or not.
Sigma offers controls to manage these refresh intervals, and for organizations with stable, repeatable workloads (e.g., daily KPIs or month-end reports), this can be efficient.
However, the model requires ongoing tuning, balancing refresh frequency, table size, and concurrency settings.
Key points for BI leaders:
- Performance via materialization: fast for recurring queries.
- Compute trade-off: background jobs add cost.
- Partial visibility: compute billed to Snowflake but not surfaced clearly in Sigma’s UI.
- Best for steady-state workloads: predictable, low-volatility datasets.
Sigma’s architecture rewards teams willing to manage refresh cycles, but less so those needing spontaneous exploration at scale.
🧩 The short of it for BI Leaders
- Astrato scales linearly with your warehouse: speed and cost remain predictable.
- Sigma accelerates queries through materialization but adds background compute management.
- Both perform well: Astrato optimizes for real-time governance, Sigma for repeatable performance.
💡Verdict: Astrato scales with your warehouse. Sigma scales with your structure.
🧰 Developer Experience & Extensibility: analytics that plays well with others
For modern BI leaders, analytics doesn’t stop at the dashboard.It weaves into customer portals, internal tools, and automated workflows.
That’s where extensibility – APIs, SDKs, and CI/CD hooks – defines whether your BI platform becomes a living system or just another SaaS app.
⚡ Astrato → Built for Builders
Astrato treats analytics like infrastructure: modular, composable, and programmable. Everything you can do in the UI, you can also automate through its REST API and lightweight Actions SDK.
Developers can:
- Trigger dashboards via webhooks when new data lands in Snowflake.
- Embed Astrato components directly inside React, Angular, or Vue apps, with full event handling.
- Manage assets through CI/CD pipelines or Terraform modules for version-controlled deployments.
- Call Astrato Actions from external systems (e.g., send Slack alerts, update CRM records, or push data back to Snowflake).
Astrato’s APIs align with the modern DataOps + DevOps stack, meaning analytics can finally sit alongside application code, not apart from it.
☕️ For Developers
Example: Send a POST request to Astrato’s API to refresh an embedded dashboard whenever new data lands in Snowflake.
Response time: milliseconds.
Result: live customer portal updates without touching the dashboard UI.
The philosophy: open by design. Astrato doesn’t try to do everything – it integrates with everything.
🔄 Sigma → APIs in Progress
Sigma takes a more conservative approach to extensibility.
Its primary focus remains within the governed workbook environment, keeping analytics tightly bound to Sigma’s UI and semantic layer.
There’s now an API for content management and embedding, and limited automation endpoints for scheduling or refreshing datasets.
But it’s still a closed-loop system: most workflows happen inside Sigma’s platform rather than extending outward.
For data teams that prefer centralization and controlled governance, that’s a plus – fewer moving parts, tighter control.
For developer-led organizations embedding analytics into SaaS products or operational systems, it means more manual work.
☕️For Developers:
- Embedding: Sigma supports iframe-based embeds, but event hooks are limited.
- Automation: Dataset refresh and scheduling via API are available but narrow in scope.
- Governance trade-off: Greater central control, less developer agility.
Sigma’s model is secure and stable, it just doesn’t invite the same level of external orchestration yet.
🧩 The short of it for BI Leaders
- Astrato: API-first design integrates with your broader data stack – ideal for OEMs, SaaS teams, and automated reporting environments.
- Sigma: Prioritizes internal governance and controlled environments over developer freedom.
- Trendline: Both are evolving fast: Astrato already fits DataOps workflows, Sigma is building toward them.
💡Verdict: Astrato plays nicely with the modern DevOps toolkit. Sigma plays it safe.
💸 Pricing & Licensing: scaling cost with value, not headcount
Modern BI isn’t just about capability, it’s also about how fairly you pay for it.
As analytics spreads from internal teams to external users, licensing models can decide whether your data strategy scales or stalls.
⚡️ Astrato → Transparent, Usage-Based Flexibility
In 2025, Astrato moved to a usage-based model built for the cloud generation of BI.
Instead of charging per seat, Astrato aligns cost with actual usage – compute, access, and value delivered.
Three main tiers cover the spectrum of users:
- Creator: full authoring, modeling, and AI access.
- Explorer: interactive dashboard and report exploration.
- Viewer: governed consumption, perfect for embedded analytics.
Volume-based pricing means you can expose analytics to thousands of customers in your SaaS portal without paying per viewer license.
Other inclusions that matter to BI leaders:
- AI & Cortex integrations: no add-on costs – all included in the base plan.
- Scheduled & cyclical reports: built-in, not an upgrade.
- Embedded analytics: usage-based rather than user-count-based.
- Governance features: enterprise-grade controls by default, not gated behind premium tiers.
The result is a linear cost curve: as usage grows, so does value – not complexity.
🧮 Sigma → Familiar Licensing, Familiar Trade-Offs
Sigma continues to follow the traditional BI pricing playbook: per-creator and per-viewer licensing, with additional fees for AI, advanced governance, and scheduled reporting.
This model works well for centralized analytics teams where the number of authors and consumers is known and stable.
However, for organizations embedding analytics into customer portals or distributing dashboards to thousands of light viewers, costs can grow exponentially.
To Sigma’s credit, the model brings predictability for departmental use and aligns neatly with procurement processes already in place at many enterprises.
But in customer-facing scenarios, the economics get tricky: each new viewer counts as another license, even if they only log in once a month.
Key points for BI leaders:
- Predictable for internal BI: clear per-user costs.
- Costly for external access: no true usage-based option.
- AI access: available, but billed as an add-on.
- Scheduled reports: part of premium features.
Sigma’s structure reflects a more traditional analytics footprint – efficient for internal teams, less so for scaled, embedded, or AI-driven environments.
🧩 The short of it for BI Leaders
- Astrato ties price to usage, not headcount – ideal for SaaS and enterprise-scale deployments.
- Sigma remains license-based – predictable, but rigid as user numbers grow.
- AI and reporting add-ons make Sigma’s total cost of ownership higher over time.
💡Verdict: Astrato scales cost with value. Sigma scales cost with users.
⚖️ Final Thoughts: two paths up the same mountain
Astrato and Sigma share a heritage: both were born in the cloud, built around live-query architectures that replaced extract-and-load drudgery with real-time insight.
But by 2026, their philosophies have diverged – not in capability, but in intent.
✍️The Bottom Line
Sigma remains a solid, dependable choice for analyst-led teams, especially those who thrive in formulas, prefer tight spreadsheet control, and value governed workbooks over pixel-perfect dashboards.
Astrato, by contrast, has expanded the definition of BI itself. It’s become an execution layer, where analysis, action, and AI coexist directly on top of the warehouse.
Dashboards are no longer static screens. They’re interactive data apps where users can explore, act, and automate in real time.
See how Astrato brings together live data, AI insights, and real-time actions, all in one interface. Book a demo here.
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