Best Sisense Alternatives for Embedded, Multi-Tenant Analytics (2026)

The right Sisense alternative depends on what you embed and who uses it. SaaS teams that want governed, multi-tenant dashboards and AI for their customers at a predictable flat price should look at Holistics, GoodData or Luzmo. Teams that want analytics to feel like native React components inside their product, and are prepared to write more front-end code, should look at Embeddable or DataBrain. Teams already standardized on Microsoft, Google Cloud or Snowflake usually end up comparing Power BI Embedded, Looker or Sigma, mostly on how much their incumbent contract already covers. Teams that need something cheap and simple for a few hundred customers often start with Metabase. ThoughtSpot Embedded fits products where natural-language search is the feature customers pay for.

Sisense is an embedded analytics platform built for software companies that ship dashboards and data experiences inside their own applications. Sisense ingests data (often into its in-memory ElastiCube engine), models it, and serves it to thousands of customers through an SDK with white-labeling and tenant-level security. That model still works for many companies. Teams that leave in 2026 mostly cite unpredictable pricing, the operational weight of ElastiCubes, and the custom front-end work needed to make embedded analytics feel native.

This guide compares nine alternatives on the questions that decide embedded analytics projects in practice: how each one isolates tenants, how it embeds, what it costs as your customer count grows, and what moving off Sisense actually involves.

Why teams leave Sisense, and when they should stay

Teams leave Sisense for four recurring reasons, both in our own conversations and on review sites and community threads.

1. Cost that is hard to forecast. Quote-based contracts, add-ons for connectors and AI, and credits that meter AI usage make it difficult to know what next year's bill will look like when the customer base doubles. Product teams that resell analytics need a cost they can put in a pricing model, and a bill that moves with usage they cannot control does not fit one.
2. ElastiCube operations. Cubes need build schedules, memory headroom and someone to debug failed builds. Teams that have since moved their data into Snowflake, BigQuery or Databricks often find they are maintaining a second copy of their data model inside Sisense for little benefit, especially when a cube per tenant multiplies the work.
3. Front-end effort for a native feel. Teams that want embedded dashboards to match their design system, beyond theming, usually need Compose SDK development or JavaScript customization. Some teams accept that engineering work; others want to avoid it.
4. Modeling that lives in a GUI. Even with Git sync, analysts build the data model in Sisense's visual modeler and export it as JSON. Teams whose analytics engineers already work in dbt and code review tend to want the semantic model itself to be readable, reviewable code.

Some teams have good reasons to stay. Sisense still suits teams that have built a large Compose SDK front end they would have to rewrite, for companies that need self-hosted or on-premises deployment for contractual or regulatory reasons, and for workloads where an in-memory cube outperforms querying the warehouse (high-concurrency dashboards on modest data volumes, for example). If none of the four problems above costs you money or engineering time, a migration will probably cost more than it saves.

How embedded analytics pricing models differ

Most comparison pages skip pricing, yet pricing decides more embedded analytics evaluations than any other factor. Embedded vendors use four broad models, and each one behaves differently as your customer base grows. (Our embedded analytics pricing guide covers these models in more depth.)

Model	How it charges	Who uses it	What happens at 5x growth
Per viewer or per seat	A fee for each end user (sometimes with cheaper viewer tiers)	Tableau, Looker per-user add-ons, ThoughtSpot Analytics, Metabase beyond included seats	Cost scales linearly with customers, so products with many low-frequency viewers pay the most
Capacity or compute	A fixed price for dedicated capacity, regardless of user count	Power BI Embedded (A/F SKUs)	Predictable until you outgrow the capacity, then a step change to the next SKU
Credits or query metering	Credits consumed by queries, dashboard views or AI usage	Sisense self-serve tiers (AI credits), ThoughtSpot usage-based Pro, Luzmo usage component	Hard to forecast, because usage depends on customer behavior you do not control
Flat platform fee by tenant or end-user band	A fixed annual fee that covers a range of end users or workspaces	Holistics embedded, DataBrain, Luzmo platform tiers, GoodData (platform fee plus workspaces)	Predictable within the band, with a known price for the next band

You should build a two-year cost model at the start of the evaluation. Price each shortlisted vendor at your current number of tenants and active end users, then at 2x and 5x, and ask each vendor in writing which line items change.

Multi-tenancy patterns, and which tools support them

Tenant isolation separates embedded analytics from internal BI, and the right tool depends on how your product already stores customer data. Three patterns cover almost every SaaS architecture.

In Holistics, the embed token carries each viewer's tenant and row-level permissions, so one dashboard serves every customer with only their own data.

1. Shared model with row-level security. All tenants live in the same tables, and every query is filtered by a tenant ID carried in a signed token or user attribute. This pattern is the simplest and most common, and it relies entirely on the vendor's row-level security.
2. Schema, dataset or workspace per tenant. Each customer gets its own schema or logical workspace, often with shared dashboard definitions deployed into each one. This pattern suits products that customize metrics per customer or that need hard logical separation.
3. Database per tenant. Each customer's data sits in a separate database or warehouse, and the analytics layer routes each viewer's queries to the right connection at runtime. This pattern is common in regulated industries and in older single-tenant SaaS architectures.

Tool	Shared model + RLS	Schema / workspace per tenant	Database per tenant
Sisense (baseline)	Data security rules	Dedicated models per tenant	Dynamic ElastiCube, separate servers
Holistics	Row-level permissions from signed embed attributes	Schema routing via user attributes	Dynamic data sources from the embed payload
GoodData	Data filters in the logical data model	Workspace per tenant with a shared parent model (its core design)	Per-workspace data source mapping
Luzmo	Token-level parameter filters	Per-tenant dataset overrides	Per-tenant connection overrides
Embeddable	Security context passed with each embed	Per-tenant data models in code	Per-tenant connection routing
DataBrain	Tenant-level filters	Tenant schema mapping	Per-tenant data source configuration
Power BI Embedded	RLS roles with effective identity	Workspace per tenant	Separate semantic models per tenant
ThoughtSpot Embedded	Row-level security rules	Orgs per tenant, with publishing from a primary Org	Per-Org connections
Looker	Access filters and user attributes	Per-tenant models or explores	Connection switching via user attributes
Metabase	Data sandboxing (Pro/Enterprise)	Per-tenant collections and permissions	Database routing (Pro/Enterprise)
Sigma	User attributes driving RLS	Per-tenant workspaces	Per-tenant warehouse roles or connections

This table only shows which patterns each tool supports. Your own PoC should test them: two users from different tenants log in side by side, and neither should see the other's rows, exports or AI answers (see the checklist further down).

What should a Sisense alternative offer?

Sisense customers usually ship analytics to their own customers, so a replacement has to pass two tests: what end users see inside the product, and what the vendor costs as the customer base grows. We used the criteria below to compare the tools in this guide.

• A semantic modeling layer that grounds AI answers. An assistant that queries a governed semantic layer chooses from metrics the data team has already defined and reviewed, applies the same permissions as every dashboard, and returns numbers that match the reports people already rely on, so its answers are grounded in agreed definitions rather than in whatever the model inferred from column names. Customer-facing analytics makes this even more important: an AI answer that disagrees with the dashboard next to it creates support tickets, and one that reaches across tenant boundaries creates a security incident.
• Embedding method and white-labeling. Vendors embed through signed iframes, JavaScript SDKs or React components, and the right choice depends on how native the analytics must feel and how much front-end work your team will take on. White-labeling should cover colors, fonts and domains.
• A white-label AI assistant. Customers increasingly expect to ask questions in plain language inside the product. The assistant should carry your brand rather than the vendor's, answer from the same governed metrics as your dashboards, and respect each tenant's permissions.
• Multi-tenancy. The tool should support the tenancy pattern your architecture already uses (shared model with row-level security, schema or dataset per tenant, or database per tenant), as described in the section above.
• End-user self-service. Customers increasingly expect to filter, drill and build their own reports, and AI-powered self-service lets them ask questions without your team building every view in advance.
• Data architecture. Warehouse-native tools remove the cube-building and refresh work that ElastiCubes require, at the cost of depending on warehouse performance and spend.
• Developer workflow. Models and dashboards defined as code, Git review and separate environments make it safer to ship analytics changes to every customer at once.
• A pricing model that scales with your customer count. Per-viewer, credit-based and query-metered pricing all grow with adoption, while flat or capacity pricing is easier to forecast; the pricing section above explains the trade-offs.
• Security and deployment. SSO for embed authentication, data residency options, compliance coverage (SOC 2, HIPAA where relevant) and, for some buyers, self-hosting.

The best Sisense alternatives in 2026

1. Holistics: the full BI experience inside your product, at a flat price

Holistics is an AI analytics platform that lets product teams bring the entire BI experience into their own app. Customers can drag and drop to build their own dashboards and reports, or chat with a white-label AI assistant to get the answers they need. AI answers come from a governed semantic layer and inherit the viewer's row-level permissions, so a tenant's users only ever query their own data.

Holistics defines everything as code: charts, dashboards, metrics and models. Product teams can therefore extend and customize the embedded experience well beyond theming, and match their product's brand with deep UI customization and complete styling control. Every change goes through a Git workflow (analytics-as-code), so your team reviews and approves updates before they reach customers. Compared with Sisense, Holistics also queries your warehouse directly, so you have no cube to build or maintain, and it charges a flat embedded fee by end-user band rather than a quote that moves with usage.

Every embedded tier includes a custom report builder with the AI assistant, advanced drills and interactions, and an embedded team workspace where a customer's users save and share what they build. Embeds load through a signed URL whose JWT payload carries the viewer's identity, permissions and filters, and for database-per-tenant architectures, dynamic data sources route each viewer's queries to the right database or schema based on attributes in the embed token.

The white-label AI assistant answers customers' questions from the semantic layer and respects their row-level permissions.

How Holistics compares to Sisense:

Capability	Sisense	Holistics
Embedding method	iframe, Embed SDK, Compose SDK (React/Angular/Vue)	Signed iframe (JWT), fully white-labeled
Customization	Theming, plus Compose SDK or JavaScript development	Charts, dashboards, metrics and models defined as code, with complete styling control
Data engine	ElastiCube (in-memory) or Live models	Warehouse-native, with optional caching
Semantic layer	Visual modeler, exported to JSON for Git	AML, authored as code with types and reusable modules
Multi-tenancy	Data security rules, per-tenant models, Dynamic ElastiCube	Row-level permissions, schema routing, dynamic data sources
End-user self-service	Limited without custom development	Drag-and-drop dashboard and report builder, advanced drills, embedded team workspace
AI for end users	Simply Ask, assistant, narratives	White-label AI assistant, grounded in the semantic layer, permission-aware
Pricing	Quote-based (self-serve tiers withdrawn mid-2026)	Published flat tiers by end-user band

Pricing (October 2026): Holistics publishes flat embedded plans. Small is $3,000/month (billed annually) for up to 5,000 embedded end users, Medium is $6,000/month for up to 15,000, and Large is $12,000/month for up to 50,000, each including 10 internal analytics users, with no per-viewer fees, no query metering and no mid-term overage bills; every tier has the same feature set, including white-labeling and the AI assistant, and deployments above 50,000 end users are sized individually (embedded pricing). Internal analytics plans start at $800/month (Entry) and $1,000/month (Standard), each billed annually with the first 10 users included and $12.50/month per additional user, so a 15-user Standard deployment is $12,750 per year.

Best fit: SaaS teams with data in a modern warehouse that want one governed metric layer serving both internal reporting and customer-facing dashboards, analytics engineers who prefer reviewing model changes in pull requests, and products with thousands of low-frequency viewers where per-viewer pricing does not work.

Limitations: Holistics embeds through a signed iframe rather than a React component SDK, so teams that want every chart rendered as a native component in their own front end will find Compose SDK, Embeddable or Luzmo's components more flexible. Holistics requires a SQL data warehouse or database and does not bundle ingestion or an in-memory engine. The code-based modeling layer also assumes someone on the team is comfortable with SQL and modeling as code. (For a closer look at white-labeling across vendors, see our white-label analytics platforms guide.)

2. GoodData: workspace-per-tenant embedded analytics for large tenant counts

GoodData is an embedded analytics platform designed around multi-tenancy, where each customer gets a workspace that inherits a shared logical data model and metric definitions from a parent. Metrics are written in MAQL, GoodData's metric language, and the platform exposes most functionality through APIs, a React SDK (GoodData.UI) and declarative definitions that can be managed as code. GoodData Cloud is the hosted product, and GoodData.CN offers a containerized deployment for teams that need to self-host, which makes it one of the few alternatives that can match Sisense on deployment flexibility.

GoodData product demo (video: GoodData).

Capability	Sisense	GoodData
Embedding method	iframe, Embed SDK, Compose SDK	React SDK, web components, iframe
Multi-tenancy	Data security rules, per-tenant models	Workspace hierarchy with inherited models
Semantic layer	Visual modeler	Logical data model plus MAQL metrics
Deployment	Cloud, private cloud, on-prem	Cloud, or self-hosted GoodData.CN

Pricing: Quote-based. GoodData describes its Professional plan as a platform fee plus a charge per workspace, and a competitor's analysis puts the Professional plan at about $1,500/month before workspace fees.

Best fit: Software companies with hundreds or thousands of tenants that need per-tenant customization on top of shared definitions, and teams that want an API-first, as-code deployment process.

Limitations: MAQL is specific to GoodData and takes time to learn. Per-workspace pricing needs careful modeling if every customer becomes a workspace. Visualization options are narrower than Tableau or Power BI, and the community and third-party resources are smaller than the larger platforms.

3. Luzmo: managed embedded dashboards with component SDKs

Luzmo (formerly Cumul.io) is built only for customer-facing analytics. It offers an embedded dashboard editor, a Flex SDK that renders individual charts as components in React, Angular or Vue, multi-tenant security through short-lived embed tokens with parameter filters, and Luzmo IQ for natural-language questions. Product teams shortlist it because it delivers polished, white-labeled dashboards to customers quickly, without a large modeling project.

Luzmo IQ, Luzmo's embedded AI assistant (video: Luzmo).

Capability	Sisense	Luzmo
Embedding method	iframe, Embed SDK, Compose SDK	Embedded dashboards, Flex SDK components
Data engine	ElastiCube or Live	Live queries and Luzmo-hosted datasets
End-user editing	Requires custom work	Built-in embedded dashboard editor
Pricing	Quote-based	Published entry tiers plus usage

Pricing: Luzmo's own 2026 comparison pages list plans from €1,995/month billed annually, with an adoption-based usage component. Older directory listings show lower entry tiers, so confirm current packaging before budgeting.

Best fit: SaaS product teams that want to ship customer-facing dashboards quickly, with end-user editing and AI, and whose metric logic is relatively simple or already modeled upstream.

Limitations: Luzmo has no code-based semantic layer comparable to AML, LookML or MAQL, so complex metric logic tends to live in the warehouse or in individual datasets. Usage-based components make cost less predictable at scale. It is built for external analytics, so teams that also want to replace internal BI usually need a second tool.

4. Embeddable: developer-first components that feel native

Embeddable is a developer toolkit for building embedded analytics as code. Developers define data models and build components in React (or wrap their own chart library), and Embeddable renders them as web components inside the host application, where non-developers can then assemble dashboards from those approved building blocks. It works most like Sisense's Compose SDK, except that your own front-end team owns more of the UI.

Embeddable's dashboards, self-serve and AI chat in two minutes (video: Embeddable).

Capability	Sisense	Embeddable
Embedding method	Compose SDK and iframe	Web components built from your React code
Look and feel	Theming plus SDK customization	Uses your own components, so it matches by design
Modeling	Visual modeler	Data models defined in code (Cube-based)
Pricing	Quote-based	Quote-based (a third-party listing shows a $499/month Lite tier)

Pricing: Embeddable has historically required a sales conversation. A February 2026 third-party listing shows a free tier and a Lite plan at $499/month, so confirm pricing with Embeddable directly.

Best fit: Product teams with front-end capacity who want embedded analytics to look and behave exactly like the rest of their application.

Limitations: Embeddable needs more engineering effort up front than managed dashboard tools, since your team owns the components. It is a smaller vendor with a shorter track record, and it suits internal self-service BI less well.

5. DataBrain: flat-priced embedded dashboards for SaaS teams

DataBrain is a newer embedded analytics vendor aimed at SaaS companies that want to replace incumbents such as Sisense. It embeds through web components, supports tenant-level filtering and per-tenant data source mapping, includes an AI chat feature for end users, and publishes flat monthly pricing. A customer case study cited by SmartData Collective describes a company that moved to DataBrain and reported savings of around $300,000.

DataBrain product walk-through, including search and embedding (video: DataBrain).

Capability	Sisense	DataBrain
Embedding method	iframe, Embed SDK, Compose SDK	Web components, iframe
Multi-tenancy	Data security rules, per-tenant models	Tenant filters, per-tenant data sources
Pricing	Quote-based	Published flat tiers

Pricing (October 2026): Growth costs $999/month and Pro costs $1,995/month; enterprise plans are quoted.

Best fit: Small and mid-sized SaaS teams that want fast customer-facing dashboards at a predictable price.

Limitations: DataBrain is a younger product with a smaller ecosystem and a lighter semantic modeling layer than code-first tools, and it has limited use as an internal BI platform.

6. Power BI Embedded: capacity pricing for Microsoft-centered stacks

Power BI Embedded lets ISVs embed Power BI reports into their applications using the "app owns data" model, where the application authenticates with a service principal and passes an effective identity so that row-level security roles filter each tenant's data. Microsoft documents both RLS-based multi-tenancy and workspace-per-tenant designs. (Our Power BI Embedded guide covers setup and pricing in more detail.)

A Power BI report embedded in an application.

Capability	Sisense	Power BI Embedded
Embedding method	iframe, Embed SDK, Compose SDK	JavaScript SDK over Power BI's embed API
Data engine	ElastiCube or Live	Import (in-memory) or DirectQuery
Semantic layer	Visual modeler	DAX measures per semantic model
Pricing	Quote-based	Azure capacity (A SKUs) or Fabric F SKUs

Pricing: Power BI Embedded charges for capacity rather than per viewer. An A1 SKU is roughly $735/month at list price and Fabric capacity starts at F2 (about $263/month), with larger SKUs for production concurrency. Report authors still need Power BI Pro licenses ($14/user/month).

Best fit: ISVs already invested in Azure and Microsoft 365 whose customers recognize and expect Power BI.

Limitations: Capacity sizing is hard to predict and tends to step up sharply once concurrency grows. Report authoring depends on Power BI Desktop, which is Windows-only. DAX measures live in individual semantic models, so keeping metrics consistent across tenant workspaces takes discipline. Theming beyond Power BI's look requires work.

7. ThoughtSpot Embedded: natural-language search as the product feature

ThoughtSpot Embedded (formerly ThoughtSpot Everywhere) embeds ThoughtSpot's search, Liveboards and Spotter AI agent into applications through the Visual Embed SDK, a TypeScript library that is the primary embedding method rather than an iframe wrapper. ThoughtSpot handles multi-tenancy with Orgs, where content created in a primary Org can be published to tenant Orgs, and TML (ThoughtSpot's modeling language) can be deployed across environments through ThoughtSpot's GitHub integration. Our ThoughtSpot alternatives guide covers its internal BI product in more depth.

Adding ThoughtSpot to an application with ThoughtSpot Embedded (video: ThoughtSpot for Developers).

Capability	Sisense	ThoughtSpot Embedded
Embedding method	iframe, Embed SDK, Compose SDK	Visual Embed SDK (TypeScript), REST APIs
End-user experience	Dashboards	Search, Liveboards, Spotter AI
Multi-tenancy	Data security rules, per-tenant models	Orgs plus row-level security
Version control	Git sync of dashboards and models	TML deploy via GitHub

Pricing: Embedded Enterprise is quote-only. A free Developer edition covers up to 10 users and 25 million rows for one year. ThoughtSpot's internal Analytics product lists Essentials from $25 and Pro from $50 per user per month (billed annually), with a usage-based Pro option at $0.10 per credit.

Best fit: Products whose customers want to ask ad-hoc questions in plain language, backed by a well-modeled warehouse.

Limitations: Accurate search depends on significant up-front modeling. Spotter was still in beta for the embedded product as of mid-2026. Visualization control is narrower than dashboard-first tools, and enterprise pricing is usually higher than Sisense rather than lower.

8. Looker: governed embedding for Google Cloud teams

Looker offers signed and private embedding through its Embed SDK, with LookML providing a centralized, code-based semantic layer and access filters driven by user attributes for tenant isolation. Looker can also switch database connections per tenant through user attributes. (See our Looker embedded analytics guide for details.)

A Looker dashboard embedded in an application.

Capability	Sisense	Looker
Embedding method	iframe, Embed SDK, Compose SDK	Signed embed URLs, Embed SDK
Semantic layer	Visual modeler	LookML, authored as code in Git
Multi-tenancy	Data security rules, per-tenant models	Access filters, user attributes, connection switching
Pricing	Quote-based	Quote-based; embed licensed separately

Pricing: Looker is quote-based. Third-party data commonly puts Looker Standard at $35,000–$60,000/year and enterprise contracts at around $150,000/year, with embedded usage licensed on top.

Best fit: Enterprises on Google Cloud with analytics engineers who can own LookML and a large analytics budget.

Limitations: LookML specialists are scarce, models get harder to maintain as they grow, embed pricing often drives teams away from Looker, and Google has shifted product investment toward Gemini and Looker Studio.

9. Metabase: the low-cost starting point

Metabase is an open-source BI tool that many SaaS teams use for their first customer-facing dashboards. Static embedding with signed JWTs is available in the open-source edition, and the paid editions add interactive embedding, the Embedded Analytics SDK for React, data sandboxing for row- and column-level security, and database routing for database-per-tenant setups.

A Metabase dashboard, the same view its interactive embedding exposes.

Capability	Sisense	Metabase
Embedding method	iframe, Embed SDK, Compose SDK	Static (signed) embeds, interactive embedding, React SDK
Semantic layer	Visual modeler	Lightweight models and metrics in the UI
Multi-tenancy	Data security rules, per-tenant models	Sandboxing, database routing (paid editions)
Pricing	Quote-based	Free self-hosted; published cloud tiers

Pricing (October 2026): Open source is free to self-host. Cloud Starter is $100/month plus $6/user, Pro is $575/month plus $12/user, and Enterprise starts around $20,000/year. Metabase AI is billed at $3.75 per million tokens.

Best fit: Early-stage SaaS products with simple embedding needs and a small budget, and engineering teams willing to self-host.

Limitations: Metric definitions drift without a strong semantic layer, per-user pricing on paid tiers adds up for large viewer counts, and white-labeling and multi-tenant features sit in paid editions. Most teams replace it as they grow rather than keep it as a long-term platform.

Other options worth a look

• Sigma embeds warehouse-native workbooks through signed URLs, with user attributes driving row-level security. It suits products whose customers think in spreadsheets, and contracts are quote-based (third-party data puts the median around $62,000/year).
• Qrvey targets SaaS companies with a self-hosted, AWS-deployed embedded analytics stack and flat pricing.
• Explo offers embedded dashboards and report builders for SaaS teams with fast setup.
• Cube is a semantic layer and API rather than a dashboard tool, and pairs well with Embeddable or a custom front end.

How the Sisense alternatives compare

Tool	Embedding method	Data engine	Semantic layer	Multi-tenancy patterns	AI for end users	Pricing model (Oct 2026)	Best for
Holistics	Signed iframe, white-labeled	Warehouse-native	AML (code)	RLS, schema routing, DB per tenant	AI report builder (permission-aware)	Flat, from $3,000/mo for ≤5,000 end users	Governed, code-defined embedded + internal BI
GoodData	React SDK, web components	Warehouse-native	LDM + MAQL	Workspace per tenant	Yes	Platform fee + per workspace (quote)	Very large tenant counts, self-host option
Luzmo	Dashboards, Flex SDK	Live + hosted	Dataset-level	Token filters, per-tenant overrides	Luzmo IQ	From €1,995/mo + usage	Fast, polished customer dashboards
Embeddable	Web components from your code	Via Cube models	Code (Cube)	Security context, per-tenant routing	Limited	Quote; third-party lists $499/mo Lite	Native-feeling, developer-owned UI
DataBrain	Web components	Warehouse-native	Light	Tenant filters, per-tenant sources	AI chat	$999 / $1,995 per month	SMB SaaS on a budget
Power BI Embedded	JS SDK	Import or DirectQuery	DAX per model	RLS, workspace per tenant	Copilot (capacity-gated)	Azure/Fabric capacity	Microsoft-centered ISVs
ThoughtSpot Embedded	Visual Embed SDK	Warehouse-native	TML models	Orgs + RLS	Spotter (beta in embed)	Quote	Search-first customer analytics
Looker	Signed embed, Embed SDK	Warehouse-native	LookML (code)	Access filters, connection switching	Gemini features	Quote; embed extra	Google Cloud enterprises
Metabase	Static, interactive, React SDK	Direct queries	Light	Sandboxing, DB routing (paid)	Metabot	Free OSS; from $100/mo cloud	Early-stage, low budget

A PoC checklist for any Sisense replacement

A two-week proof of concept on your own data will tell you more than any comparison page, including this one. The checks below most often separate a good demo from a good deployment.

• Tenant identity through signed tokens. Your backend should mint the embed token, and the tenant ID and permissions in it should be impossible for the browser to change.
• A cross-tenant leakage test. Two users from different tenants should open the same dashboard side by side, and neither should see the other's rows, filter values, drill-throughs or cached results.
• Exports and scheduled reports. CSV, Excel and PDF exports, along with emailed reports, should respect the same row-level rules as the live dashboard.
• AI answers that inherit permissions. Natural-language questions should return only the asking user's data, and the vendor should be able to explain which model and data the AI can see.
• Theming and white-labeling. The embedded experience should match your design system and domain without visible vendor branding, and you should know how much of that needs custom code.
• Performance at realistic concurrency. Your heaviest dashboard should load acceptably with the number of simultaneous viewers you expect at peak.
• Pricing at 2x and 5x. Each vendor should confirm in writing what you would pay at double and five times your current tenant and end-user counts.

How to migrate off Sisense

Most Sisense migrations take one to three months for a typical SaaS product, and most of the work goes into the data model and security rules rather than the dashboards. (For the wider trade-off, see our build vs. buy embedded analytics guide.)

In Holistics, dashboards are defined as code, so rebuilt dashboards can be generated, reviewed and compared before cutover.

1. The inventory comes first. Sisense's usage data shows which dashboards and widgets customers actually open, and in most deployments a minority of dashboards carry most of the traffic, so the migration scope is usually smaller than the dashboard count suggests.
2. ElastiCube logic moves upstream. Joins, custom columns and transformations inside cubes are best rebuilt in your warehouse (often in dbt) or in the new tool's semantic layer, so that the logic is no longer tied to any BI vendor.
3. Data security rules map to the new tenant model. Each Sisense data security rule needs an equivalent row-level permission, workspace or connection route, and this mapping should be tested with the cross-tenant check above before any customer sees it.
4. Embed authentication gets replaced. Your backend's Sisense SSO or token logic is swapped for the new vendor's signed-token flow, which is usually a few days of backend work.
5. Customers move in cohorts. Teams that run both platforms in parallel and move a small group of customers first (internal accounts, then friendly customers, then everyone) catch problems before they reach every customer.
6. The contract sets the deadline. Sisense renewals are typically annual, so plan to finish the cutover a full reporting cycle before the renewal date.

Frequently asked questions

What is the best Sisense alternative for multi-tenant SaaS?

The answer depends on how your product stores customer data. For a shared database with a tenant ID column, Holistics, Luzmo and DataBrain handle row-level isolation through signed embed tokens with little setup. For a separate database per customer, Holistics (dynamic data sources), Looker (connection switching) and Metabase (database routing) can route each viewer's queries at runtime. For thousands of tenants that each need customized metrics on top of shared definitions, GoodData's workspace hierarchy was designed for that case.

What are the best Sisense alternatives for embedded analytics in SaaS?

Holistics, GoodData, Luzmo, Embeddable and DataBrain are the alternatives built primarily or substantially for embedded, customer-facing analytics. Power BI Embedded, Looker, ThoughtSpot Embedded and Metabase are general BI tools with embedding options, which makes them a better fit when you also want to replace internal BI with the same platform.

How much does Sisense cost?

Sisense's enterprise and OEM pricing is quote-based. Self-serve tiers introduced in early 2026 (Launch at $399/month and Grow at $1,299/month) were reportedly removed from the public pricing page in June 2026. Vendr data across 97 deals puts the average Sisense contract at about $137,000 per year, and large OEM deployments cost more once connectors, AI credits and additional capacity are added. (Our Sisense pricing guide covers the details.)

Is there an open-source Sisense alternative?

Metabase is the most widely used open-source option for embedded dashboards, with static signed embedding in the free edition. Apache Superset is another open-source choice, though it needs more engineering to embed securely. Both require you to run and secure the infrastructure, and multi-tenant features in Metabase sit in the paid editions. See more: Best Open-source BI Tools

Does Sisense support Git version control?

Yes. Sisense Linux 2022.10 and later can sync dashboards and data models to a Git repository as JSON files. Merges and conflict resolution happen in the remote repository rather than in the Sisense interface, and data security settings are excluded from the repo. Code-first tools such as Holistics, Looker and GoodData go further by making the semantic model itself the code that analysts write and review.

Do I need to replace ElastiCubes with a data warehouse?

Most Sisense alternatives query a warehouse or database directly, so ElastiCube transformations need a new home. Teams that already run Snowflake, BigQuery, Redshift, Databricks or PostgreSQL usually move that logic into dbt or the new tool's semantic layer. Teams without a warehouse should budget for one, or choose a vendor that hosts datasets for you, such as Luzmo.

How long does it take to migrate from Sisense?

A typical SaaS product with a few dozen customer-facing dashboards takes one to three months, most of it spent rebuilding cube logic and validating tenant security. Products with deep Compose SDK customization take longer, since that front-end code has to be rebuilt against the new vendor's SDK or embed method.

Which Sisense alternative has the most predictable pricing?

Vendors with flat platform pricing by end-user band or tenant count are the easiest to forecast. Holistics (flat embedded tiers by end-user band, no per-viewer fees and no query metering) and DataBrain (flat monthly tiers) publish their prices. Capacity-based Power BI Embedded is predictable until you outgrow a SKU. Per-viewer, credit-based and query-metered models are the hardest to forecast as your customer base grows.

How we evaluated these tools

Holistics makes one of the products in this list, so we have tried to make our reasoning checkable. We assessed each tool on embedding method, multi-tenancy patterns, semantic modeling, AI for end users, deployment options and published pricing, using vendor documentation and pricing pages checked in October 2026, supplemented by third-party contract data where vendors do not publish prices. Where sources disagreed, we used the most recent figure and said so. We list Holistics' own limitations alongside everyone else's, and we encourage anyone shortlisting tools to run the PoC checklist above on their own data rather than relying on any vendor's comparison, including ours.