Embedded Analytics Pricing in 2026: 20 Vendors Compared

Embedded analytics pricing is hard to compare because vendors do not charge for the same thing. One platform bills for named users, another for monthly active users, sessions, data capacity or platform access. Many enterprise vendors do not publish a price at all.

That means a $500 plan is not automatically cheaper than a $1,000 plan. The better question is: what makes your bill increase when customer adoption grows?

We checked current vendor pricing pages in September 2026 and compared 20 embedded analytics platforms on published prices, billing models and the factors most likely to affect cost at scale.

Embedded analytics pricing comparison

The table below uses current public pricing where the vendor provides it. “Custom quote” means we could not find a current public embedded analytics rate on the vendor's own site.

Platform	Public pricing signal	Main billing model	What makes cost grow
Luzmo	From €1,995/mo	Platform fee + adoption-based usage	MAUs, AI conversations or both; optional capacity
Metabase	$575/mo + $12/user	Platform + users	Named users
Amazon QuickSight	From $250/mo for 500 sessions	Sessions/capacity	Dashboard sessions
Qlik	From $300/mo	User/capacity	Users or data capacity
Preset	Embedded viewers from $500/mo	Viewer licenses	Embedded viewers
Toucan	From €445/mo	Plan	Plan and AI usage
Holistics	Custom embedded pricing	Custom	Contract structure
DataBrain	From $999/mo	Flat feature tier	Plan/features
Explo	From $1,995+/mo	Platform + scale	Customer deployment scale
Reveal	Custom quote	Fixed	Contract
Embeddable	Custom quote	Flat subscription	Project scope
Tableau Embedded	Contact sales	Role/usage/capacity	Licensing model selected
Power BI Embedded	Variable	Capacity	Compute/node usage
Looker Embed	Custom quote	Platform + users	Platform, users and usage
Sisense	Custom quote	Tailored	Scale and deployment
Domo	Custom quote	Consumption	Credits/usage
ThoughtSpot Embedded	Developer route free; Enterprise custom	Flexible	Deployment/use case
Qrvey	Custom quote	Flat edition	Edition
GoodData	Custom	Workspace/subscription	Workspaces and contract
Omni	Custom	Sales-led	Contract

Prices and packaging change, so treat this table as a buying starting point rather than a quote.

We checked vendor-owned sources first. Luzmo publishes one complete plan, Embedded Everywhere, starting at €1,995 per month billed annually, with a platform fee plus adoption-based usage. Metabase's embedded Pro tier starts at $575 per month plus $12 per user, while Amazon QuickSight publishes both per-reader and capacity-based options.

Why embedded analytics prices are so difficult to compare?

Traditional BI pricing usually assumes a relatively controlled group of employees. Customer-facing analytics changes the economics.

A SaaS company may have 20 employees building reports and 20,000 customers viewing them. A pricing model that works perfectly for internal BI can become expensive once every external viewer needs a license.

The opposite can also happen. Usage pricing may be attractive when thousands of customers open a dashboard only once or twice per month. A large flat platform fee could cost more at low adoption.

This is why the billing unit matters more than the starting price.

Consider Metabase and QuickSight. Metabase Pro charges a platform fee plus named users. QuickSight can charge for Reader sessions instead. A product with 1,000 provisioned users but only 100 monthly dashboard users can produce a very different bill under those two models.

The same issue appears higher in the market. Domo uses consumption-based pricing with unlimited users, Qlik has moved much of its Cloud Analytics offer toward data-capacity pricing and Qrvey markets flat-rate licensing with unlimited tenants and users.

There is no universally cheapest model. There is a model that fits your adoption pattern better.

Most popular embedded analytics pricing models

1. Per-user or per-viewer pricing

The bill grows with the number of licensed people who can use analytics.

Metabase is a clear public example. Its Pro plan costs $575 per month, with the first 10 users included and additional users at $12 per month. Both internal analytics users and users accessing embeds count toward the license.

This model is easy to forecast when the user base is small and stable. It becomes more difficult for a SaaS product where thousands of customers may eventually receive analytics access.

2. Monthly active user or adoption-based pricing

Adoption-based pricing ties at least part of the bill to how customers actually use analytics rather than how many accounts or internal seats exist. Monthly active users are one common usage meter.

Luzmo uses this model in a broader form. Its Embedded Everywhere plan starts at €1,995 per month, billed annually, with a platform fee plus usage based on monthly active users, AI conversations or a combination of both. Internal builders do not require additional seat licenses.

The advantage is that cost can follow real customer adoption rather than provisioned accounts or internal team size. Buyers still need to understand which usage meter applies to their deployment and how the bill changes at higher adoption levels.

3. Session or usage pricing

Instead of charging for people, the vendor meters what they do.

Amazon QuickSight's embedded-oriented capacity option starts at $250 per month for 500 Reader sessions. A session is a 30-minute period. Larger annual capacity packages reduce the published per-session rate.

Usage pricing can work well when the number of accounts is high but dashboard activity is light or irregular. It also means successful adoption can increase the bill quickly, so model both normal and peak usage.

4. Capacity, compute or data pricing

Capacity models charge for infrastructure or another resource pool rather than viewer count.

Power BI Embedded uses Azure capacity and bills according to the node resources deployed and how long they run. Microsoft describes the model as pay as you go and bills active capacity at an hourly rate.

Qlik Cloud Analytics also uses capacity as a major value meter. Standard and Premium plans primarily measure Data for Analysis rather than users.

These models can make sense for predictable workloads, but product teams need to model data and compute growth, not only user growth.

5. Flat platform pricing

A flat model attempts to separate the bill from the number of viewers or interactions.

DataBrain publishes a $999 monthly Growth plan with unlimited seats and embeds. Reveal and Qrvey also position their embedded offers around predictable fixed or flat-rate licensing, although Reveal and Qrvey require a quote for the actual contract amount.

Flat pricing is attractive when analytics adoption should grow aggressively across a product. Buyers still need to check which features, environments and support levels sit inside the quoted package.

6. Custom or hybrid enterprise pricing

Many enterprise platforms combine multiple units and only disclose the final price after scoping.

Looker's Embed edition uses a custom annual quote, with platform and user licensing as separate components. Sisense also directs Enterprise buyers to a tailored contract based on the deployment. Tableau supports several embedded licensing structures rather than one universal embedded price.

“Custom” does not automatically mean expensive or unpredictable. It does mean the buyer cannot compare the platform from the homepage alone.

What the public prices actually tell you

A published starting price is useful, but it does not tell you the production cost on its own.

Take Metabase. At the current monthly Pro rate, 100 licensed users would produce roughly:

$575 + 90 × $12 = $1,655 per month.

At 1,000 users, the same public formula becomes:

$575 + 990 × $12 = $12,455 per month.

That calculation is possible because Metabase publishes the platform fee, included users and additional-user rate. Large embedded deployments can move to tailored Enterprise pricing where the standard per-seat model does not fit.

QuickSight works differently. The public embedded-friendly capacity model starts with 500 Reader sessions for $250 per month. Once those sessions are consumed, additional sessions cost $0.50 under that monthly plan. Larger annual packages have lower published session rates.

Those numbers cannot be compared to Metabase based on user count alone. You need to know how often each customer opens analytics.

The same rule applies to Luzmo. Embedded Everywhere starts at €1,995 per month, billed annually, but the final bill also includes adoption-based usage. Depending on the deployment, that usage can be measured through monthly active users, AI conversations or both. Luzmo does not publish every usage threshold on the public page, so it would be misleading to invent a 10,000-user price.

When a vendor does not provide the inputs needed for a calculation, “cannot be calculated from public pricing” is more useful than a fake estimate.

How costs change as your SaaS grows

Three scenarios tell you more than one starting price.

Scenario	What to model
Early rollout	100 active analytics users, low concurrency, one production environment
Growth	1,000 active users, multiple tenants, more builders and support requirements
Wide adoption	10,000+ users, variable usage, enterprise security and heavier query load

For each vendor, ask what changes between those scenarios.

A per-user tool may rise almost linearly with user count. Session pricing may remain low until engagement grows. Capacity pricing may stay stable until the workload exceeds the purchased pool. A flat platform contract may remain predictable, but a higher feature or deployment tier could become necessary.

The key question is not simply:

“What will this cost today?”

It is:

“What happens to this bill if analytics becomes one of our most-used product features?”

That is the scenario SaaS teams should want.

Costs that can sit outside the headline price

The software fee is only one line in the embedded analytics budget.

Cost	Question to ask the vendor
Implementation	Is onboarding or implementation included in the subscription?
Production environments	Do dev, staging and production require separate licenses or instances?
SSO and security	Does SSO, SCIM, audit logging or advanced access control require a higher tier?
Dedicated infrastructure	What changes if we need a dedicated VPC, private cloud or on-prem deployment?
AI usage	Are AI questions, agents or generated insights metered separately?
Warehouse compute	Does the analytics workload create extra Snowflake, BigQuery or other cloud cost?
Support and SLA	Is priority support included or sold separately?
Professional services	Do custom integrations, migrations or advanced modeling create project fees?

These details can alter the comparison considerably.

Luzmo, for example, states that white-labeling, self-service analytics, AI, APIs and SDKs are included in the platform fee. Expert implementation support, stronger SLAs and source-code escrow are optional services, while Warp can be added as capacity for higher-volume analytics workloads rather than as a higher feature tier.

Looker provides another emerging example. Its current pricing documentation states that Conversational Analytics receives an included pool of data tokens, with overage charging scheduled to begin on October 1, 2026.

That is exactly the kind of cost a buyer could miss if the evaluation only compares dashboard licenses.

Which pricing model fits your SaaS product?

The answer depends on how users consume analytics.

Product pattern	Model worth considering
Small number of high-value users	Per-user can be simple
Large account base with uneven usage	MAU or session pricing can fit
Infrequent embedded reporting	Session/usage pricing may be efficient
Predictable heavy workload	Capacity pricing can be easier to budget
Fast-growing customer-facing feature	Flat or adoption-aligned pricing can reduce viewer-license friction
Complex regulated deployment	Custom enterprise pricing may be unavoidable

Do not choose a pricing model only because it is cheap at launch.

A SaaS business should also ask how well analytics cost maps to customer revenue. If a $50 customer generates $20 of analytics cost, the feature has a structural margin problem. If analytics supports a premium tier, retention or expansion revenue, a higher platform fee may be easier to justify.

For teams considering the alternative, the calculation should also include the cost of building analytics in-house, not just vendor subscription fees. We have a separate analysis of the engineering, maintenance and opportunity cost of building analytics in-house.

How Luzmo pricing works

Luzmo now uses one complete plan called Embedded Everywhere, starting at €1,995 per month, billed annually. The commercial model combines a platform fee with adoption-based usage rather than separating customer-facing capabilities across Starter, Premium and Enterprise feature tiers.

The platform fee includes the complete customer-facing analytics product, including white-labeling, self-service analytics, governed AI conversations, APIs, SDKs and internal builders. Luzmo states that there are no additional seat licenses for the internal team and no upgrade ladder for customer-facing product features.

Usage then follows customer adoption. Depending on the deployment, it can be measured through monthly active users, AI conversations or a combination of both. The plan includes 500 AI conversations per month.

Luzmo also includes 100 million rows and offers Warp as optional data-acceleration capacity for higher-volume workloads. Warp is positioned as a capacity top-up rather than a separate feature tier or Enterprise upgrade.

Enterprise requirements change the deployment rather than the product feature set. Private infrastructure, custom SLAs and deeper compliance requirements can be handled through an Enterprise deployment, while implementation support, stronger SLAs and source-code escrow are available as optional services.

That does not automatically make Luzmo the cheapest option for every company. A product with very light usage may prefer a session-based model. A team already standardized on Microsoft infrastructure may find Power BI's capacity economics easier to absorb.

The advantage of published starting prices is that a product team can at least create an initial budget before entering a sales process.

Questions to ask before signing an embedded analytics contract

A good pricing conversation should produce enough information to model your product at today's adoption and at several times that scale.

Ask	Why it matters
What is the primary billable unit?	Reveals what makes cost grow
Who counts as a user?	Provisioned and active users are very different
What happens when usage exceeds our plan?	Exposes overage risk
Are dev and staging included?	Prevents environment surprises
Which security features require an upgrade?	Matters before enterprise deals
Is white-labeling included?	Core requirement for many SaaS products
Is customer self-service included?	May trigger a premium tier
How is AI usage billed?	New cost center in 2026
Are exports and scheduled reports metered?	Heavy reporting can affect usage
What support is included?	Production analytics often needs an SLA
Can we forecast the bill at 3× and 10× adoption?	Tests pricing predictability
What sits outside the license?	Reveals the actual total cost

If a vendor cannot explain what happens to the bill at 10× adoption, the starting quote is not enough to make the decision.

Embedded analytics pricing: the bottom line

There is no single cheapest embedded analytics platform.

Current public offers range from usage and viewer-based models to flat subscriptions, MAU pricing, data capacity and fully custom enterprise contracts. The right comparison therefore starts with the billing unit, not the number displayed on the pricing page.

Before choosing a vendor, model the same product scenario across every shortlisted platform. Use the same number of active users, tenants, dashboard sessions, environments and expected data volume. Add security, AI, implementation and infrastructure costs where they apply.

Then repeat the calculation at several times your current scale.

That is the price that matters.