Best Sisense Alternatives in 2026: 15 Tools for Embedded and Internal Analytics

Updated August 2026

Luzmo is the best Sisense alternative for a product team shipping customer-facing dashboards inside its own SaaS app without seat licences, GoodData is the closest match if you need governed multi-tenant analytics managed as code, and Microsoft Power BI is the strongest pick for an internal BI team that bought Sisense for in-house reporting and now wants a per-user price it can read off a web page. Every tool below is evaluated on which of those two jobs it actually does, and every price comes from the vendor's own pricing page as of August 2026.

That split matters more here than in any other alternatives shortlist, because Sisense serves two audiences that shop for completely different things. Product and engineering leaders bought it to white-label dashboards into a customer-facing application, and their real peer set is the embedded analytics vendors. Internal BI teams bought it to run in-house reporting, and their peer set is Power BI, Tableau, and the rest of the internal platforms. Sections 1 through 9 below are for the first group, sections 10 through 15 for the second, and each one says plainly which reader it is written for. If you have not narrowed the category yet, start with the business intelligence tools roundup.

Key Facts

• Gartner predicts that by 2027, 75% of new analytics content will be contextualized for intelligent applications through generative AI, rather than consumed in a standalone BI tool (Gartner, June 2025).
• 76% of organizations already use embedded analytics internally, per Reveal's 2026 embedded analytics survey (Reveal).
• 31.4% of organizations say they use embedded analytics specifically to generate higher revenue, and 62% credit embedded analytics or BI with contributing to productivity gains, per Reveal's survey series (Reveal).
• Sisense publishes no price, and third-party benchmark data puts the average annual Sisense contract at $56,831 across 126 purchases, ranging from $18,139 to $178,681 (reported by Vendr, February 2026). That near ten-fold spread is what a quote-only model looks like from the buyer's side.
• The global BI software market is projected to reach $43.7 billion in 2026, growing at a 9.3% CAGR through 2033 (Grand View Research).

Quick Comparison Table

Two Sisense Buyers, Two Completely Different Shortlists

Before comparing anything, work out which buyer you are. Sisense sits in an unusual spot because it genuinely serves both, and a shortlist built for the wrong one wastes a quarter.

If you are the first buyer, skip to section 1. If you are the second, the internal platforms start at section 10, and the honest answer is that you probably over-bought when you chose Sisense in the first place: an embedded platform carries multi-tenancy and SDK machinery you never used.

Why Sisense Shortlists Reopen in 2026

Start with what Sisense gets right, because teams do evaluate away and come back. ElastiCube is a genuine architectural differentiator: an in-memory, in-chip columnar engine that pre-processes and compresses data so embedded dashboards stay fast under many concurrent tenants, without hammering a source database on every click. The white-label depth is real too. Compose SDK gives component-level control rather than an iframe, and Sisense keeps shipping against it, adding an MCP Server in the 2026.1 release and a Compose SDK plugins framework plus an embeddable assistant in 2026.2 (Sisense product roundup, 2026.2). For a product team that needs analytics to look and behave like a native feature, that is not a small thing.

So why do shortlists reopen anyway?

There is no published price anywhere. The pricing page names two tiers, Self-Serve and Enterprise ("built to your specifications"), with a free trial, a "talk to us" button, and no dollar figure at either one. Every evaluation starts with a sales call, which is fine at renewal and painful when you are sizing a build-or-buy decision in a planning cycle. Third-party benchmark data shows the effect: Vendr reports an average annual Sisense contract of $56,831 across 126 purchases, ranging from $18,139 to $178,681. Two similar companies can pay wildly different amounts, and you cannot tell in advance which one you will be.

ElastiCube is a data layer you have to own. The engine that makes Sisense fast is also a second copy of your data, and someone has to model it, schedule its builds, size it and keep it in sync. Teams that adopted a cloud warehouse after Sisense often find they maintain the same joins twice, once in dbt and once in an ElastiCube. That duplication is the most common technical reason a contract gets re-examined.

Warehouse-native rivals skip that layer entirely. Luzmo, Explo, Sigma and Qlik's direct-query modes read Snowflake, BigQuery or Databricks live. No cube to build, no refresh window to babysit, and your warehouse's own access controls carry through.

AI features arrive with a second meter. Sisense Managed LLM is paired with Sisense Credits, a consumption model for AI spend. Reasonable as a billing design, and another variable to forecast on top of a contract you could not forecast in the first place.

For a direct three-way look at how Sisense stacks up against two adjacent platforms, see Qlik Cloud Analytics vs ThoughtSpot vs Sisense.

Who Publishes a Price and Who Does Not

This is the table most Sisense leavers actually want, because pricing opacity is usually the reason the search started. Six of the sixteen platforms discussed on this page publish nothing you can budget against, and two more publish only half of what you need.

Two things worth reading off that table. Published pricing correlates loosely with company size and not at all with product quality: Luzmo publishes and Sigma does not, and both are credible. And if the opacity itself is your dealbreaker, the embedded shortlist gets short fast, because Luzmo is the only purpose-built embedded vendor here that prints a number.

Embedded-First Alternatives, Sections 1 to 9

These nine are for the first buyer: product and engineering leaders putting analytics inside a customer-facing application. The first four are embedded-native. The last five are platforms with a real embedding story attached to a broader product.

1. Luzmo - The Only Embedded Vendor Here That Publishes a Price

Luzmo (formerly Cumul.io) is built for exactly one job: analytics inside somebody else's SaaS product. There are no feature tiers and no seat licences, just one plan called Embedded Everywhere. You pay a platform fee plus usage measured in monthly active users, AI conversations, or both, so the bill tracks how many of your customers open a dashboard rather than how many accounts exist in your database. White label is on from day one rather than an enterprise upsell, internal builders are included, and the embed surfaces reach past the app into chat, MCP and agent contexts. Warp data acceleration covers 100 million rows on the base plan, Luzmo's answer to the problem ElastiCube solves, without asking you to own a cube.

The honest limitation is scope. Luzmo is not trying to be your internal enterprise BI platform, and it will not replace a Sisense deployment doing double duty for in-house reporting.

Pricing: Embedded Everywhere from EUR 1,995/month billed annually. Platform fee plus usage (monthly active users and/or AI conversations), with 500 AI conversations and 100 million rows included, white label from day one, and no seat licences (Luzmo pricing).

Best for: SaaS product teams that want customer-facing dashboards live in weeks, with a price they can put in a budget before talking to sales.

Sizing fit: Product companies from roughly 20 to 500 employees, where the user count is your customer base, not your headcount.

Stage fit: The moment analytics becomes a feature you sell or a retention lever rather than an internal reporting job.

2. Explo - Customer-Facing Reports Without Building a Reporting Product

Explo is the other genuinely embedded-native option on this list, and its product line reads like a checklist of everything a SaaS team ends up building by hand: Dashboard for customer-facing analytics, Report Builder AI for self-service report creation by your end users, Data Share for pushing exports directly to customers, Email for scheduled report delivery, Host for embedding without standing up a separate portal, and Global Datasets for the modeling layer underneath. That Report Builder piece is the one worth pausing on, because "let our customers build their own reports" is the feature request that turns a two-week embed project into a two-quarter one.

The catch is the same one that sent you here: Explo publishes no price and links only to a trial request, so sizing it means a sales conversation. Its compliance posture (SOC 2 Type 2, HIPAA, dedicated and region-specific hosting) suggests it sells into regulated buyers, reassuring for healthcare and fintech products and largely irrelevant otherwise.

Pricing: No published price. The site offers a trial and a contact form only, with no figures at any tier.

Best for: Product teams that need customers to build and schedule their own reports, not just view a fixed dashboard.

Sizing fit: Small and mid-size product companies, roughly 10 to 300 employees, where engineering time is the scarce resource.

Stage fit: Strongest when customer report requests land in your support queue faster than engineering can absorb them.

3. GoodData - Governed Multi-Tenant Analytics, Managed as Code

GoodData rebranded to GoodData.AI in April 2026 and rebuilt its query engine on open-source components (Apache Arrow, DuckDB, Apache Iceberg), but its pitch to a Sisense shopper is unchanged: a headless, governed Logical Data Model that many applications and AI agents can query consistently, managed through an Analytics as Code workflow with a CLI and Git integration. If ElastiCube's appeal was "one governed model powering every tenant," this is the closest philosophical replacement, expressed as reviewable code rather than a cube you rebuild in a UI. Gartner moved GoodData from Niche Player to Visionary in the 2026 Magic Quadrant for Analytics and BI Platforms.

The pricing model is the part most relevant to an embedded buyer. Professional is priced per workspace: a platform fee plus the number of workspaces, with unlimited users and unlimited data inside them. Since a workspace usually maps to a tenant, your bill scales with how many customers you serve rather than how many people log in, the cleanest match to embedded economics on this page. GoodData does not publish the figures, so you still need the sales call, but the shape of the bill is knowable in advance.

Pricing: No published price. Professional is per-workspace (platform fee plus number of workspaces, unlimited users and data, one environment); Enterprise is custom use-case pricing with three environments and a 99.5% uptime SLA. AI queries, dedicated clusters, multi-region and self-hosted deployment are on-demand add-ons (GoodData pricing).

Best for: SaaS companies with many tenants that want one governed model, version-controlled, serving every customer and every AI agent.

Sizing fit: Anywhere the tenant count is meaningful, from dozens of customers up to enterprise platforms with thousands.

Stage fit: Once analytics has to stay consistent across multiple applications and surfaces, not just one dashboard.

4. Domo (Domo Everywhere) - Free Seats, Metered Consumption

Domo inverts the licensing question entirely: it does not charge for user seats at all. The product is a pool of consumption credits committed annually, spent on storage, table updates, workflows and ML inference. Domo Everywhere is the embedding and distribution layer, and the combination is genuinely attractive for a specific shape of problem, namely pushing dashboards out to a large and unpredictable end-customer base where you cannot forecast how many named users will exist next year. If your Sisense quote scaled with tenants and you hated it, free seats read like an obvious win.

The tradeoff is that credit forecasting is its own discipline. A heavy workflow, an aggressive refresh schedule or a new ML pipeline moves consumption in ways a per-seat bill never would, and Domo publishes no price, no tier names and no figures, so the first estimate comes from a sales conversation rather than arithmetic. For a fuller look, see our Domo alternatives guide.

Pricing: No published price. Seats are free; the product is a pool of consumption credits committed annually and refreshed each billing cycle.

Best for: Organizations distributing dashboards to a large or unpredictable end-customer base where seat counting would be the wrong unit.

Sizing fit: Almost any size, because consumption rather than headcount is the budgeting variable.

Stage fit: Once broad distribution matters more than deep, code-managed modeling.

5. Qlik Cloud Analytics - Capacity Pricing With a Modern Embed Toolkit

Qlik earns its place on an embedded shortlist for two reasons. Its associative in-memory engine indexes relationships across the whole dataset up front instead of requiring pre-defined joins for every path, so drill-down inside an embedded surface feels less rigid than a fixed dashboard. And the qlik-embed web component library, introduced in 2024, replaced the older integration approaches with something closer to how modern front ends work, sharing sessions and cutting both code complexity and load time, from plain HTML through React and Svelte. Qlik Answers adds natural-language querying on top.

The commercial model is the real story though. Qlik prices on capacity, not seats, and above the Starter tier users cost nothing, the most Sisense-like thing about it without the quote-only opacity. The caveat sits at the bottom of the ladder: Starter caps at 10 users and 10 GB and cannot buy extra capacity, so an embedded deployment realistically starts at Standard. Note too that the old per-user Qlik Sense Business plan is gone, so any per-user Qlik figure in an older comparison is wrong. If Qlik is the incumbent rather than the candidate, see Qlik Sense alternatives.

Pricing: Starter $300/month billed annually (10 users, 10 GB, no extra capacity purchasable); Standard $825/month billed annually (unlimited users, 25 GB, expandable in 25 GB blocks); Premium $2,750/month billed annually (unlimited users, 50 GB); Enterprise custom from 250 GB.

Best for: Teams that want capacity-based economics and a modern embed toolkit, without a quote-only contract.

Sizing fit: Standard and above suit mid-market and enterprise. Starter is an evaluation tier, not an embedding tier.

Stage fit: Once data volume is a more predictable growth metric for you than user count.

6. ThoughtSpot Embedded - Natural-Language Search as a Product Feature

ThoughtSpot's differentiator is search rather than dashboards: users type or ask a question and it queries the model directly, with Spotter AI agents handling the agentic side. Embedded into your product that becomes a feature you can market, because "ask your data a question" demos better than another chart grid. ThoughtSpot's Models (formerly Worksheets) are its semantic layer, exportable as TML, and it integrates with the dbt Semantic Layer, which helps if you want to stop maintaining two copies of your metric logic the way ElastiCube made you.

The commercial fit is where it gets awkward for embedded buyers. ThoughtSpot prices per user: Essentials from $25/user/month for 5 to 50 users, Pro from $50/user/month up to 1,000. That works cleanly internally and gets expensive fast if every customer needs a seat. The free embedded developer tier (10 users, 25 million rows, one year) is a useful evaluation runway, but it does not change the shape of the production bill.

Pricing: Essentials from $25/user/month billed annually (5 to 50 users, up to 25M rows); Pro from $50/user/month billed annually (up to 1,000 users, 250M rows, Spotter at 25 queries per user per month); Enterprise custom and unlimited. Embedded Developer tier free for one year (10 users, 25M rows).

Best for: Products where letting end users ask questions in plain language is a differentiator worth paying per user for.

Sizing fit: Essentials suits 5 to 50 users, Pro up to 1,000. Beyond that you are in a custom contract.

Stage fit: Once natural-language analytics is a competitive requirement rather than a nice extra.

7. Metabase - The Cheapest Credible Path, With One Big Caveat

Metabase is the first place most teams look when the goal is "spend less than we do on Sisense," and for self-hosters the math is hard to argue with: the open-source edition is free, self-hosted, with unlimited users and no cap. It supports static embedding, interactive embedding and an embedded analytics SDK, so a small SaaS team can genuinely ship customer-facing dashboards without a licence line item, provided somebody owns the infrastructure.

The caveat is specific and expensive, so read it before building a business case. On Metabase Cloud, embed viewers count as users. The pricing page says so directly: both your internal team building analytics and the users of your embeds count toward your licence tier. Cloud Starter at $100 per month covers five users, so if 400 customers open an embedded dashboard you pay for 400 users at $6 each per month on top. Self-hosted open source is the version that makes embedded economics work here, not Cloud. Watch the billing basis too: Metabase publishes $100 per month and $90 per month billed annually ($1,080 a year), so a comparison quoting $90 and one quoting $100 are describing the same plan on different terms. If Metabase is your incumbent, see Metabase alternatives.

Pricing: Open Source free, self-hosted, unlimited users. Cloud Starter $100/month, or $90/month billed annually ($1,080/year), for the first 5 users, extra users $6 per user/month or $65 per user/year. Pro $575/month, or $517.50/month billed annually ($6,210/year), for the first 10 users, extra users $12 per user/month or $130 per user/year. Enterprise custom, starting at $20,000/year. Yearly billing saves 10% (Metabase pricing).

Best for: Small SaaS teams that can self-host and want embedded dashboards without a licence per customer.

Sizing fit: Best under roughly 200 employees. Larger orgs usually outgrow the governance model.

Stage fit: A natural first embedded analytics layer, or a deliberate simplification for a team that never needed ElastiCube.

8. Preset (Managed Apache Superset) - Open Source Core, Viewer Licences in Blocks

Preset is managed Apache Superset, the open-source project originally built at Airbnb, and it exists for teams whose real objection to Sisense is lock-in rather than price. Self-hosted Superset is free forever if you want to own the operations; Preset removes the hosting burden while keeping you on the same open engine, so there is no proprietary format holding your dashboards hostage at renewal. Superset's Datasets serve as a lightweight modeling layer, and assets export as YAML for version control.

For embedding, Preset prices viewer access separately and in blocks: Embedded Dashboard Viewer Licences start at $500 per month for 50 licences. That is a middle path between Metabase's per-user counting and Luzmo's usage-based platform fee, and it is predictable, which counts for something. It also caps how gracefully you scale, since 5,000 embedded viewers means a lot of blocks. White-label depth is where Preset trails the purpose-built embedders, so evaluate that carefully if analytics has to look indistinguishable from the rest of your UI.

Pricing: Starter $0 forever, up to 5 users. Professional $20/user/month billed annually ($25/user/month billed monthly), unlimited users. Enterprise custom. Embedded dashboards are an add-on: Embedded Dashboard Viewer Licences from $500/month for 50 licences. Self-hosted Apache Superset is free and open source.

Best for: Engineering-led teams that want an open engine underneath their embedded analytics, with a predictable viewer licence cost.

Sizing fit: Starter fits under 5 users. Professional and the viewer blocks scale through mid-market.

Stage fit: Anywhere avoiding lock-in outweighs needing the deepest white-label control.

9. Holistics - Modeling as Code, With Unlimited Embedded Viewers

Holistics is the modeling-discipline option in this group. Its Analytics Modeling Language (AML) defines dimensions, measures and relationships as code, checked into Git with genuine two-way sync, branching and code review. For a team whose ElastiCube models had drifted into "nobody quite knows how this measure is calculated," moving that logic into reviewable code is a real upgrade rather than a lateral move.

For embedded use, the relevant detail is that Holistics treats embedded analytics as a separate offering with unlimited dashboard viewers, quoted rather than published. That is the right unit for customer-facing work, but it means the published tiers below tell you what your internal team costs, not what your embed costs.

Pricing: Entry $960/month, or $800/month billed annually (first 10 users). Standard $1,200/month, or $1,000/month billed annually (first 10 users). Security Compliance Suite $2,400/month, or $2,000/month billed annually. Extra users $15/month, or $12.50 billed annually ($18 and $15 respectively on SCS). Embedded analytics with unlimited dashboard viewers is quoted separately (Holistics pricing).

Best for: Teams that want metric definitions in Git and a published internal price, with embedded viewers quoted on top.

Sizing fit: Entry and Standard fit 10 to 50 internal users, with the embedded tier covering the customer-facing side.

Stage fit: Once metric governance is a recurring argument and you want code review to settle it.

Internal-BI-First Alternatives, Sections 10 to 15

These six are for the second buyer: teams that bought Sisense for in-house dashboards and are re-evaluating. Most of them can embed, and a couple do it well, but their centre of gravity is internal reporting and their pricing reflects that.

10. Microsoft Power BI - The Cheapest Published Seat Price Here

If your Sisense deployment is really an internal BI deployment, Power BI is the obvious first candidate and usually the cheapest. Pro at $14.00 per user per month paid yearly is the lowest fully published per-seat rate on this page, semantic models give you a reusable governed metric layer, and Microsoft Fabric's Git integration stores those models in TMDL format so changes get real diffs and code review. For any organization already living in Microsoft 365, Teams and Azure, the ecosystem depth is the actual draw, and Copilot handles AI-assisted DAX and report generation on top.

Two caveats. Per-user pricing means the bill grows with headcount, which is fine internally and wrong for customer-facing work: for embedding you move to Power BI Embedded or Fabric capacity, and neither publishes a flat figure, which puts you back in the conversation you left Sisense to avoid. And embedding a Microsoft-branded surface inside your own product carries branding and UX constraints the specialists do not have. If Power BI is the incumbent instead, see Power BI alternatives, and Power BI vs Tableau covers the internal two-horse race.

Pricing: Free personal account; Pro $14.00/user/month paid yearly; Premium Per User $24.00/user/month paid yearly; Power BI Embedded and Fabric capacity quoted and variable by region.

Best for: Internal BI teams standardized on Microsoft that want governed models at a readable per-seat price.

Sizing fit: Small teams up through enterprise. Fabric capacity, not the seat price, is the constraint at scale.

Stage fit: The moment a company standardizes its data stack on Microsoft.

11. Tableau - Best Internal Visual Analysis, Priced Per Role

Tableau is still the reference point for visual exploration: drag-and-drop analysis, the deepest chart library in the category, and fast ad hoc discovery. The VizQL Data Model and Tableau Pulse are its moves toward a more centralized metrics layer, and the Salesforce and Agentforce integrations bring conversational querying to Tableau data. As a Sisense replacement for internal reporting, it is a straightforward upgrade on visualization depth and a sideways move on governance.

Its pricing structure is where it stops being an embedded candidate. Tableau leads with editions, Standard from $15 per user per month and Enterprise from $35 per user per month, both billed annually, and within Standard the role rates are Creator $75, Explorer $42 and Viewer $15 per user per month billed annually, with an annual contract required on every product. Naming the role matters, because "Tableau starts at $15" is true only for view-only users and misleading for anyone who builds. Embedding customer-facing dashboards under a per-role licence gets expensive quickly. See Tableau alternatives if Tableau is the incumbent, or Tableau vs Looker for the governance comparison.

Pricing: Tableau Standard from $15 USD per user per month billed annually, with role rates of Creator $75, Explorer $42 and Viewer $15 per user per month billed annually. Tableau Enterprise starts at $35 per user per month billed annually. Tableau Cloud+ and the Tableau+ bundle are quoted. Annual contract required.

Best for: Internal analyst teams that prioritize visual depth and ad hoc exploration over embedded distribution.

Sizing fit: Small analyst teams to large enterprises. Licence cost, not complexity, is the lever.

Stage fit: Once dashboard polish and visual storytelling matter as much as metric consistency.

12. Looker (Google Cloud Core) - Governed Metrics, Same Pricing Opacity

Looker is worth evaluating if what you actually want from a Sisense replacement is one governed definition of every metric. LookML is a git-based modeling language that defines dimensions, measures and joins as code, and it enforces consistency in a way ElastiCube never really did. There is a dedicated Embed edition too, so customer-facing use is supported rather than an afterthought.

But be clear-eyed about the tradeoff. Looker publishes no price at all: Standard, Enterprise and Embed editions are quote-only on 1, 2 or 3 year terms, each including one production instance, 10 Standard users and 2 Developer users. If pricing opacity is why you are leaving Sisense, you would be swapping one quote-only vendor for another with a longer minimum term attached. The one published figure is Conversational Analytics data-token overage from 1 October 2026: $3.00 per 1 million input data tokens and $20.00 per 1 million output data tokens. For a fuller shortlist, see Looker alternatives.

Pricing: No published price. Standard, Enterprise and Embed editions, quote only, on 1, 2 or 3 year terms, each including one production instance, 10 Standard Users and 2 Developer Users. Published overage from 1 October 2026 for Conversational Analytics: $3.00 per 1M input data tokens and $20.00 per 1M output data tokens.

Best for: Internal data teams on BigQuery that want governed, code-defined metrics and can live with a quoted contract.

Sizing fit: Mid-market upward, where a dedicated data team can maintain LookML.

Stage fit: Once metric consistency across the company is a bigger problem than dashboard count.

13. Sigma Computing - A Spreadsheet on the Warehouse, No Cube Required

Sigma's pitch runs directly against ElastiCube's architecture. Instead of pre-processing data into an in-memory model, business users work in a spreadsheet interface directly on live warehouse data in Snowflake, Databricks or BigQuery, including write-back input tables for what-if scenarios. Governance lives in the warehouse's own access controls rather than a separate model, which is exactly the "skip the data layer" argument that makes warehouse-native tools attractive to Sisense leavers with a modern stack.

For finance and revenue teams who would rather work in rows and columns than learn a modeling language, it is one of the easiest internal adoption stories in this category. The frustration is familiar: Sigma publishes no price, offering only a free trial and a demo request. Credible vendor, no help at all on the specific axis that sent you shopping.

Pricing: No published price. Free trial and demo request only.

Best for: Finance, revenue and ops teams that want spreadsheet-native self-service on a warehouse they already trust.

Sizing fit: Roughly 50 to 2,000 employees, wherever warehouse governance is already established.

Stage fit: Once a real cloud warehouse is in place and business users are ready to query it directly.

14. Amazon QuickSight - Session-Based Capacity for Spiky Embedded Usage

QuickSight is the AWS-native answer, and its pricing model deserves more attention from embedded buyers than it gets. Alongside conventional per-user rates (Author $24, Reader $3 per user per month, with Author Pro at $40 and Reader Pro at $20), AWS publishes reader capacity pricing that bills sessions instead of seats: 500 sessions per month for $250 per month, then $0.50 per additional session, with annual commitments starting at 50,000 sessions per year for $20,000. For a product where most customers open a dashboard once a month, paying per session rather than per named user changes the arithmetic completely, and unlike almost every embedded vendor here, AWS prints the numbers.

The limits are the usual AWS ones. It assumes you are already on AWS, the authoring experience is functional rather than delightful, and white-label polish trails the specialists. Note also the $250 per month per-account infrastructure fee that kicks in once you have any Pro user, Q&A via topics, or dashboard Q&A turned on.

Pricing: Author $24 and Reader $3 per user/month; Author Pro $40 and Reader Pro $20 per user/month. Reader capacity pricing: 500 sessions per month for $250 per month, $0.50 per additional session, with annual options from 50,000 sessions per year at $20,000 per year. A $250/month per-account infrastructure fee applies once the account has any Pro user or Q&A enabled (AWS QuickSight pricing).

Best for: AWS-native teams embedding dashboards where usage is occasional and spiky rather than daily.

Sizing fit: Small teams to enterprise. Session volume rather than headcount is the budgeting variable.

Stage fit: Once your data already lives in AWS and you want billing to track actual usage.

15. Grafana - Operational Dashboards, Not Business BI

Grafana belongs here only for a specific kind of Sisense user: one whose dashboards are operational and time-series, showing infrastructure health, device telemetry, job throughput or usage metrics rather than revenue and pipeline. For that job it is excellent and very cheap, with a free forever tier at 14-day retention, Cloud Pro from a $19 per month platform fee plus usage, and visualization billed at $8.00 per active user per month. Grafana Enterprise adds custom branding for the login page, logos and dashboard footer, iframe embedding with authentication tokens lets a customer portal load dashboards inside its own interface, and Grafana Labs runs an OEM partner programme for exactly this pattern.

What it is not is a business intelligence platform. No semantic layer, no governed metric definitions, and the modeling story assumes a time-series source rather than a warehouse star schema. If your Sisense dashboards are financial or commercial, this is the wrong tool no matter how good the price looks.

Pricing: Free forever (14-day retention). Cloud Pro from $19/month platform fee plus usage, with Grafana visualization at $8.00 per active user per month. Enterprise from a $25,000/year commitment.

Best for: Products whose embedded dashboards are operational, telemetry or usage metrics rather than business reporting.

Sizing fit: Free and Pro suit small to mid-size teams. Enterprise is a real commitment at $25,000 a year.

Stage fit: When the analytics you ship are about system behaviour, not commercial performance.

Migrating Off ElastiCube: What Does Not Port

Whichever direction you go, plan for what has to be rebuilt, because very little of a Sisense deployment moves automatically.

ElastiCube models do not export. The in-chip data model, its custom SQL tables, its relationships and its build schedules all have to be re-implemented in the target's own layer, whether that is a dbt project feeding a warehouse-native tool, a GoodData Logical Data Model, a Power BI semantic model or Holistics AML. No converter preserves the logic across tools.

Compose SDK integrations get rewritten, not ported. Every embedded widget wired through Sisense's SDK is application code against Sisense's component API. Moving to qlik-embed web components, Luzmo's embed layer or an iframe means rewriting that surface, usually the largest single line in the migration estimate.

Row-level security and tenant isolation get rebuilt. Sisense's data security rules do not transfer, and each target implements multi-tenancy its own way: GoodData through workspaces, Luzmo through per-tenant datasets and embed tokens, Power BI through RLS roles. Map this before you sign, because it decides whether your new bill scales with tenants or with users.

White-label theming and custom widgets carry over as design intent only. CSS overrides, themed components and bespoke widget code all get recreated in the new vendor's theming system. Scheduled deliveries, alerts and data actions need recreating too, since none of them export as configuration.

There is a genuine silver lining. If you have adopted a cloud warehouse since you bought Sisense, the modeling work in your dbt project is portable, and the ElastiCube was probably duplicating logic that already lived there. In that case moving to a warehouse-native tool is not really a migration, it is deleting a layer.

Sizing and Persona Fit

Stage Fit

How to Choose: Decision Framework

Decide which buyer you are first, then let the billing unit narrow the list.

What to Do Next

Answer the buyer question first, in writing, because it decides everything else: are your dashboards going in front of your customers or your colleagues? Then take your top two candidates from that half of the list and rebuild one real thing in each, the dashboard your users open most and the tenant isolation rules behind it. Give yourself two weeks and do not sit through a scripted demo for this part. Time how long it takes to theme the output so it looks like your product, and get a written quote from any vendor that does not publish a price before you get attached to the tooling. The gap between what you assumed a quote-only vendor would charge and what it actually charges is the number that decides most of these migrations.

Camellia writes about business intelligence and analytics tooling for B2B teams. Pricing verified against vendor pricing pages in August 2026.