---
title: "In-App Platform Pricing: What You Actually Pay For"
description: "MAU, events, seats, and impressions are not the same unit. A per-unit price only means something once you know what it counts."
publishedAt: "2026-08-25T18:18:00.000Z"
updatedAt: "2026-08-25T18:18:00.000Z"
author: "Ritul Singh"
categories: []
canonical: "https://www.digia.tech/post/in-app-platform-pricing-what-you-actually-pay-for"
---

# In-App Platform Pricing: What You Actually Pay For

****

**TL;DR**

- Pricing pages in this category are deliberately hard to compare, because the units differ.
- A per-unit price only means something once you know what the unit actually counts, and MAU, events, seats, impressions, and surfaces are five genuinely different things billed under headlines that all look similar.
- This article covers the five pricing models in this category and who each one favours.
- It covers the definitional differences in what counts as an MAU that produce very different bills for identical usage.
- It covers the hidden line items that turn a headline number into a real one.
- It covers how to model your own cost across the major pricing models at three different app scales.
- It covers where costs spike unexpectedly, and what running a CEP plus a layered in-app tool costs against consolidating onto one platform.
- It covers how to build the ROI case in terms a CFO will actually accept, and the negotiation levers that matter more than the headline rate.
- Sourcing note: Vendor pricing figures are drawn from published pricing pages and independently verified market data gathered earlier in this research series, cited at each point, and general SaaS pricing and negotiation benchmarks are attributed to their sources throughout.

Every vendor in this category will tell you their pricing is simple. It rarely is, and the reason is not dishonesty. It is that the unit each vendor bills against, an MAU, an event, a seat, an impression, a deployed surface, was chosen to reflect how that specific vendor's product creates value, which means two platforms quoting what looks like a similar number per unit can produce wildly different bills for identical actual usage. Comparing headline rates without first understanding what each rate is actually metered against is the single most common and most expensive mistake in this category's procurement process.

## The Five Pricing Models

**MAU-based.** The most common model in this category, billing against monthly active users regardless of how many events, campaigns, or messages each user generates. [Pendo prices this way, with a free tier up to 500 MAUs and paid tiers scaling from roughly $15,000 to over $140,000 annually depending on volume and feature tier](https://www.digia.tech/post/pendo-alternatives-consumer-mobile-apps/). This model favours a vendor whose product delivers value proportional to reach, and it favours a buyer whose usage per user is heavy, since the bill does not change whether a user triggers one campaign or fifty.

**Event-volume-based.** Billing against the number of tracked events or messages sent, rather than the size of the user base. This model favours a buyer with a large but lightly engaged user base, since a huge MAU count with low per-user event volume produces a smaller bill than the same MAU count under a pure MAU model. It penalises a buyer running many campaigns per user, regardless of total audience size.

**Seat-based.** Billing per named user of the platform itself, the marketer or growth team member configuring campaigns, not the end user receiving them. This is the classic B2B SaaS model, and it is the one most poorly suited to a consumer-scale in-app platform, since it has no relationship at all to the actual audience the platform is serving.

**Impression-based.** Billing against the number of times an in-app element is actually displayed, closer to an advertising-inventory model than a SaaS one. [Plotline prices against a combination of MAUs and impressions, with a published Starter plan from $499 per month](https://www.digia.tech/post/plotline-alternatives-indian-consumer-apps/), which is a hybrid rather than a pure impression model, but it illustrates the category's move toward metering the thing closest to actual delivered value rather than a proxy for it.

**Flat platform fee.** A fixed price regardless of scale within a defined band, sometimes paired with a usage cap. This model favours predictability over precision, and it is the model buyers should push toward specifically when their own usage is genuinely difficult to forecast, since it removes the risk of a surprise bill tied to a metric the buyer cannot yet estimate confidently.

No model is inherently better. Each one shifts risk between vendor and buyer in a specific direction, and the right model for a given team depends on which side of that risk, unpredictable cost growth versus unpredictable revenue from a platform underpriced for its own usage pattern, the team is better positioned to absorb.

## What Counts as an MAU

[The formula for MAU is trivial: the count of unique users who performed a qualifying action within a 30-day window, counted once no matter how often they return. The decision that actually determines the number is what counts as a qualifying action, and most teams, and most vendors, set that bar in very different places](https://kissmetrics.io/blog/what-is-mau). [Define active as "loaded a page" or opened the app, and MAU includes users who barely engaged at all. Define it as a genuine core value action, and the number gets considerably smaller and more honest](https://kissmetrics.io/blog/what-is-mau).


![Analytics dashboard showing daily active users (DAU) by geography, DAU/MAU engagement trends, month-over-month top event users, and top event users by media source over the last 30 days.](https://cdn.sanity.io/images/53loe8pn/production/bda18ec640517338e691bdd67d66b16dd3ebe5e2-1672x941.png?w=1200&fit=max&auto=format)


This matters directly for pricing because two vendors both claiming to bill "per MAU" can produce different bills for the exact same underlying user base, purely because their qualifying-action threshold differs. A vendor that counts any app open as an active user will report, and bill against, a larger MAU figure than a vendor that only counts a session including a meaningful in-app interaction, even when both are looking at identical raw usage data. [For B2B-oriented platforms specifically, an additional distinction matters: account-level MAU versus user-level MAU, since a customer paying for coverage across 500 named users but showing only 200 monthly logins signals a different usage pattern than a platform reporting the raw 500 regardless of actual engagement](https://www.sifthub.io/glossary/monthly-active-user), and a vendor billing against the larger, unqualified number is charging for capacity the buyer is not actually using.

The practical step before any vendor comparison: request the exact definition of "active" each vendor bills against in writing, not verbally in a sales call, and run your own historical usage data through that specific definition before comparing headline per-MAU rates. A lower per-unit price against a looser active-user definition can produce a higher total bill than a higher per-unit price against a stricter one.

## The Hidden Line Items

The headline number on a pricing page is rarely the number that appears on the actual invoice, and the gap is consistent enough across this category to name specifically.


![Invoice-style graphic titled “The Hidden Price of AI Membership,” listing four charges: Inference surcharge $56.80, Token overage $37.25, Model upgrade $72.15, and Data formatting fee $25.30, on a light cream background.](https://cdn.sanity.io/images/53loe8pn/production/3cfd5436e50a8c84223ab923e281e329a4ea8c92-1672x941.png?w=1200&fit=max&auto=format)


**Implementation fees.** A one-time cost for initial setup, data migration, and integration work, frequently not disclosed until a sales conversation has progressed past the initial pricing discussion. [Forrester's 2026 SaaS Transparency Study found only 58% of buyers viewed complete pricing information before making a purchase decision, with the remaining 42% committing based on partial information and discovering hidden costs only after signing](https://influenceflow.io/resources/pricing-information-for-saas-platforms-complete-2026-guide/).

**Premium support tiers.** Dedicated account management, priority response SLAs, and direct implementation support are frequently gated behind a higher tier or sold as a separate add-on, and [these services rarely appear in base pricing despite representing significant real value in any serious negotiation](https://influenceflow.io/resources/pricing-information-for-saas-platforms-complete-2026-guide/).

**Additional environments.** [Installing a platform across multiple products, brands, or staging and production environments frequently carries additional cost beyond the primary account](https://www.digia.tech/post/appcues-alternatives-native-mobile-apps/), a detail that matters directly for any team running separate apps for different markets or business units, since a single quoted price rarely covers a full multi-app portfolio without a separate line item.

**Data export.** Pulling raw event or campaign performance data out of a platform, for use in an internal warehouse or a different analytics tool, is sometimes a standard feature and sometimes a paid add-on, and this distinction is worth confirming explicitly before signing, since it directly affects a buyer's ability to switch vendors later without losing historical performance data.

**Overage rates.** [Open-ended consumption charges are where budgets actually break, and the specific negotiation defence is capping the per-unit overage rate contractually, so even usage that exceeds the committed volume has a ceiling rather than an unbounded per-unit charge](https://zylo.com/blog/saas-pricing-trends). [Setting internal consumption alerts at 50%, 75%, 90%, and 100% of any committed threshold gives a team lead time to adjust usage or escalate before overage charges actually hit](https://zylo.com/blog/saas-pricing-trends).

## Modelling Your Own Cost

The inputs to gather before running any comparison: current and projected MAU at 6 and 12 months, average events or campaigns per active user per month, number of connected environments or apps, and whether the team expects to need premium support or dedicated implementation help in the first year.


![Team Cost Overview dashboard showing filters for timeframe and interval, total amortized cost of $688,664, total list cost of $7,399,685, a donut chart breaking down costs across AWS, Google Cloud, Datadog, Kubernetes, and Azure, a monthly backend-versus-infrastructure cost trend, and a price-per-million-transactions line chart.](https://cdn.sanity.io/images/53loe8pn/production/2226d81ec48fe9002b67d7d4dea6ef44cd0c4887-1672x941.png?w=1200&fit=max&auto=format)


A worked comparison across three app scales, using the pricing structures documented across this research series, illustrates how differently the models respond to growth.

**Small app, roughly 50,000 MAU.** An MAU-based platform at this scale typically sits in an entry or near-entry tier, [comparable to CleverTap's Essentials plan starting at $75 per month for up to 5,000 MAU, scaling up through defined bands to 100,000 MAU](https://www.digia.tech/post/clevertap-alternatives-7-tools-better-in-app-ui/), meaning a 50,000 MAU app sits mid-band on a platform priced this way. A flat-fee or hybrid impression-based platform at this scale, such as [Plotline's published Starter tier from $499 per month](https://www.digia.tech/post/plotline-alternatives-indian-consumer-apps/), may already sit near or above its base tier depending on impression volume, which is the specific scenario where a smaller app's actual event-generation intensity, not just its MAU count, determines which pricing model is cheaper.

**Mid-scale app, roughly 2 million MAU.** This is the range where the divergence between models becomes most visible. A pure MAU-metered platform's bill scales close to linearly with this growth. An event-volume model's bill depends entirely on how many campaigns per user the team is actually running, which means a lean, infrequent-campaign team can end up paying meaningfully less under this model than under a pure MAU model at the same audience size, while a team running frequent, high-touch campaigns can end up paying more.

**Large app, 20 million-plus MAU.** At this scale, a pure per-MAU model without a volume-discount band produces a bill that most consumer apps at this scale cannot sustain against their own per-user revenue, which is precisely the scale-inversion mismatch [documented in detail for Pendo's MAU-metered pricing against a high-volume, low-ARPU consumer app profile](https://www.digia.tech/post/pendo-alternatives-consumer-mobile-apps/). At this scale, a quoted, deployment-scoped price, negotiated specifically against the buyer's actual usage pattern rather than read off a published tier table, is almost always the more realistic path, which is why most platforms serving consumer apps at this scale, Digia included, quote per deployment rather than publishing a fixed rate card.

## Where Costs Spike Unexpectedly

**Viral growth.** A sudden, unplanned MAU spike from a viral moment or a major press hit produces a genuine tension under an MAU-metered model: the exact event that validates the product's growth is the same event that triggers a bill increase, frequently before the corresponding revenue has caught up. Teams should confirm in advance how a vendor handles a sudden spike, whether the bill adjusts immediately at the next billing cycle or whether there is a grace period or banding buffer that absorbs a short-term surge.

**Seasonal peaks.** A commerce or fintech app with a strong seasonal pattern, festival-period traffic, tax-season activity, faces a version of the same problem on a predictable, recurring basis rather than a one-time surprise. This is a case where negotiating a committed annual volume banded around the actual peak-adjusted average, rather than the off-peak baseline, prevents a recurring overage charge every single year at the same predictable time.

**Event instrumentation expansion.** As a growth team matures its measurement practice and adds more tracked events, campaign triggers, and audience segments, an event-volume-metered platform's bill grows even if the underlying MAU has not, purely because the team is now measuring and acting on more signals per user than before. This is a specific, easy-to-miss cost driver, since it is triggered by the team's own improving practice, not by user growth.

**Multi-app portfolios.** A business operating separate apps for different markets, brands, or product lines faces the additional-environment cost covered above, multiplied across every app in the portfolio, and this cost should be modelled explicitly as a portfolio-wide total, not estimated by multiplying a single app's quote by the number of apps, since most vendors offer some form of portfolio discount that a per-app estimate would miss entirely.

## The Layered-Setup Question

Running a full customer engagement platform for segmentation and orchestration, plus a dedicated in-app layer for rendering, is a two-vendor cost, and the honest question is when that two-vendor cost is actually cheaper than consolidating onto a single platform's bundled in-app module.

The scenarios where layering is genuinely cheaper: when the existing CEP's in-app module is being paid for regardless, as part of a bundle already covering push, email, and SMS, the marginal cost of adding a dedicated in-app layer is only the layer's own price, not a full platform switch. [This is the specific architecture Digia and comparable layered tools are built around, integrating directly with CleverTap, MoEngage, or WebEngage and reusing existing segments rather than duplicating the CEP's own targeting engine](https://www.digia.tech/post/clevertap-alternatives-7-tools-better-in-app-ui/), which means the layered cost is additive to an existing, already-justified spend, not a second full platform bill.

The scenario where consolidation is cheaper: when a team's in-app needs are narrow enough that a single platform's bundled module genuinely covers them, adding a second vendor introduces cost, integration overhead, and a second contract to manage, for a capability gap that may not be large enough to justify the additional spend. [A migration to a different full CEP, by contrast, typically requires rebuilding segmentation and re-testing journey automation over six to twelve weeks](https://www.digia.tech/post/moengage-alternatives-in-app-messaging-consumer-apps/), which is a materially larger cost and disruption than either layering or staying on the existing bundled module, and is only justified when the underlying dissatisfaction extends beyond rendering into the CEP's core segmentation or pricing itself.

## Building the ROI Case

[The value chain that connects an in-app spend to a number a CFO will accept runs from campaign to activation event to retention improvement to LTV delta to annual revenue impact, and the case breaks most commonly at the retention-improvement link, from a missing holdout comparison, and the LTV-delta link, from ARPU not being segmented by retained versus churned users](https://www.digia.tech/post/roi-of-in-app-engagement-business-case-leadership/). Applied specifically to a pricing decision, this means the ROI case for a specific platform choice should not stop at "this platform is cheaper per MAU." It should connect the platform's actual measured impact, holdout-verified retention or conversion lift, to the revenue that lift produces, and only then compare that revenue against the platform's total cost, including every hidden line item covered above.


![SaaS dashboard showing 3,200 customers, $8,500 LTV, and a 14-month runway. The dashboard includes Revenue Growth with $125,000 revenue, +12.5% MRR growth, $1.5M ARR, and $45K monthly burn rate; a Cohort Retention percentage table; a Net Dollar Retention chart; and a Gross Margin gauge showing 68% and “Healthy Margin.”](https://cdn.sanity.io/images/53loe8pn/production/41946b41768393047088d5b980d11bd5e9f01749-1672x941.png?w=1200&fit=max&auto=format)


[The cost comparison between in-app re-engagement and paid re-acquisition, customer acquisition typically running $200 to $1,500 per customer against retention costs of $15 to $85, frequently produces the single most persuasive number in a leadership presentation](https://www.digia.tech/post/roi-of-in-app-engagement-business-case-leadership/), because it requires leadership to trust two costs the business is already tracking elsewhere, not a projected model built specifically to justify the platform spend under discussion. A pricing decision framed this way, what does this platform cost against what re-acquiring the users it retains would cost instead, is a materially stronger case than a feature-by-feature comparison of one vendor's price against another's.

## Negotiation Levers

[Starting negotiations six months ahead of a renewal date yields up to 39% savings, compared to roughly 14% savings for teams that begin the process just 30 days out, a gap that reflects a real asymmetry: vendors track renewal dates closely and frequently delay contract drafts specifically to create artificial urgency against a buyer negotiating late](https://busistack.com/enterprise-saas-contract-negotiation-tips/).

**Annual commitment.** [Multi-year contract premiums, the additional discount for locking in two to three years versus one, hit their highest recorded level in 2025, at roughly 2.6 percentage points of additional discount on top of standard annual pricing](https://busistack.com/enterprise-saas-cost-comparison-guide/), and [negotiating a three-year term specifically can secure a 20 to 40% discount off list price for teams confident in their platform choice](https://influenceflow.io/resources/pricing-information-for-saas-platforms-complete-2026-guide/). The trade-off is reduced flexibility if the platform choice turns out to be wrong, which is why this lever is best used only after a genuine trial period has validated the fit, not as a first-contract commitment.

**MAU banding.** Negotiating a wider band at a fixed price, rather than a precise per-unit rate that recalculates continuously, protects against the viral-growth and seasonal-spike scenarios covered above, converting an unpredictable variable cost into a predictable fixed one within a defined range.

**Pilot pricing.** A scoped, time-boxed pilot at reduced or waived cost, tied to a specific, pre-agreed success metric, is a standard and reasonable ask, and it is the mechanism that should precede any multi-year commitment, since it produces real usage data against the buyer's own actual conditions rather than a vendor's published benchmark.

**The terms that matter more than headline rate.** [Volume commitment, committing to more MAU, events, or seats, moves the negotiation needle more than contract term length alone](https://busistack.com/enterprise-saas-cost-comparison-guide/), which means a buyer with genuine growth confidence has more leverage offering a volume commitment than simply asking for a lower headline rate. Beyond rate, the specific contract terms worth negotiating explicitly: a capped overage rate rather than an open-ended one, an exit clause without a punitive termination fee, a defined and reasonable annual price escalator rather than an unstated one applied at renewal, and clarity on which support tier and implementation assistance is included versus billed separately.

## Topics Not in the Brief That Teams Should Know

**AI-feature premiums are a fast-growing, frequently under-disclosed cost category.** [Platforms adding generative AI capabilities typically charge 20 to 40% more than their base tier, and organisations needing these capabilities should budget 25 to 35% higher than a prior-year baseline when evaluating a renewal or new contract](https://influenceflow.io/resources/pricing-information-for-saas-platforms-complete-2026-guide/). Any team evaluating a platform's AI-assisted segmentation, creative generation, or campaign review capability should confirm explicitly whether that capability is bundled or metered as a separate premium, since this is one of the fastest-moving cost categories in the entire market as of 2026.

**Annual price escalators compound silently if not capped in the original contract.** [Average annual SaaS price increases now run 8 to 12%, with more aggressive vendors implementing 15 to 25% hikes, and when hidden mechanisms like migration fees and credit multipliers are included, effective increases can reach 20 to 30% in a single renewal cycle](https://medium.com/@aymane.bt/the-future-of-saas-pricing-in-2026-an-expert-guide-for-founders-and-leaders-a8d996892876). A contract with no capped escalator clause is exposed to this full range at every renewal, which is a specific, negotiable term worth fixing at signing rather than discovering at the first renewal.

**Red flags worth treating as decision criteria, not just friction.** [No published pricing at all, vague tier names without clear feature boundaries, undisclosed overage charges, aggressive lock-in penalties, a required multi-year minimum with no pilot option, and the absence of any money-back or exit provision are consistent warning signs across this category](https://influenceflow.io/resources/pricing-information-for-saas-platforms-complete-2026-guide/), and a vendor exhibiting several of these simultaneously warrants weighting the negotiation and contract review more heavily than the platform's feature list alone would suggest.

**Bundled multi-product discounts can offset a higher per-product price, but only if the bundle is genuinely needed.** [Vendors offering multiple relevant products, engagement, analytics, or a CDP, will frequently negotiate a bundle discount rather than pricing each separately](https://saasreviewer.io/how-to-negotiate-saas-pricing-for-better-enterprise-discounts-in-2026/), but this only produces genuine savings if every bundled component is something the team would have purchased separately anyway, not a discount that exists to justify paying for capability the team does not actually need.

## Key Takeaways

The five pricing models in this category, MAU-based, event-volume-based, seat-based, impression-based, and flat platform fee, each shift cost risk between vendor and buyer in a different direction, and the right model depends on which risk a given team is better positioned to absorb, not which headline rate looks lowest.

What counts as an active user varies significantly between vendors, and two platforms both billing "per MAU" can produce very different bills for identical underlying usage, purely because their qualifying-action threshold differs, which makes requesting the exact billing definition in writing a mandatory first step before any comparison.

Hidden line items, implementation fees, premium support, additional environments, data export, and overage rates, routinely turn a headline number into a materially higher real one, and 42% of buyers report committing to a SaaS purchase based on incomplete pricing information.

Cost modelling should be run across at least three app scales using a team's own actual usage pattern, since the model that is cheapest at 50,000 MAU is frequently not the model that stays cheapest at 2 million or 20 million MAU.

Cost spikes concentrate around viral growth, seasonal peaks, expanding event instrumentation, and multi-app portfolios, and each of these should be explicitly modelled and, where possible, negotiated for in advance rather than discovered as a surprise bill after the fact.

Layering a dedicated in-app tool on top of an existing CEP is genuinely cheaper when the CEP spend is already justified for its other channels, and consolidation is genuinely cheaper only when in-app needs are narrow enough that a single bundled module actually covers them.

The negotiation levers that matter more than headline rate are volume commitment, a capped overage rate, a defined escalator, and starting the negotiation process six months rather than thirty days before a renewal date, which alone has been shown to nearly triple the achievable discount.

## Further Reading

**From Digia Engage:**

- [CleverTap Alternatives: 7 Tools for Teams Who Want Better In-App UI](https://www.digia.tech/post/clevertap-alternatives-7-tools-better-in-app-ui/) — CleverTap's published entry pricing and tier structure referenced in this article's modelling section
- [Pendo Alternatives for Consumer Mobile Apps](https://www.digia.tech/post/pendo-alternatives-consumer-mobile-apps/) — the full MAU-metered pricing mechanics and the B2B-to-consumer scale mismatch this article's large-scale modelling builds on
- [Plotline Alternatives for Indian Consumer Apps](https://www.digia.tech/post/plotline-alternatives-indian-consumer-apps/) — Plotline's published starting price and hybrid MAU-plus-impression model
- [MoEngage Alternatives for In-App Messaging on Consumer Apps](https://www.digia.tech/post/moengage-alternatives-in-app-messaging-consumer-apps/) — the full CEP migration cost and timeline this article's layered-setup section references
- [Appcues Alternatives for Native Mobile Apps](https://www.digia.tech/post/appcues-alternatives-native-mobile-apps/) — the multi-environment additional cost structure referenced in this article's hidden line items section
- [The ROI of In-App Engagement: Business Case for Leadership](https://www.digia.tech/post/roi-of-in-app-engagement-business-case-leadership/) — the full value chain and cost-comparison framework this article's ROI section is built on
- [Digia Engage](https://www.digia.tech/products/nudges) — deployment-scoped pricing built around consumer-app economics rather than a B2B seat or unbanded MAU rate card

**External Sources:**

- [What Is MAU? Monthly Active Users Defined and Calculated](https://kissmetrics.io/blog/what-is-mau) — Kissmetrics (the qualifying-action definitional variance underlying this article's MAU section)
- [Monthly Active User (MAU), Glossary](https://www.sifthub.io/glossary/monthly-active-user) — SiftHub (account-level versus user-level MAU distinction for B2B-oriented platforms)
- [2026 SaaS Pricing Trends Driving Up Enterprise Costs](https://zylo.com/blog/saas-pricing-trends) — Zylo (overage rate capping and consumption alert thresholds)
- [How to Negotiate SaaS Contracts for Better Pricing](https://josys.com/article/how-to-negotiate-saas-contracts-for-better-pricing) — Josys (hidden fee categories and contract lock-in patterns)
- [SaaS Pricing Models & Hidden Costs Guide 2026](https://influenceflow.io/resources/pricing-information-for-saas-platforms-complete-2026-guide/) — InfluenceFlow (58% partial-information purchase statistic; AI-feature premium data; negotiation red flags)
- [How to Negotiate SaaS Pricing for Better Enterprise Discounts in 2026](https://saasreviewer.io/how-to-negotiate-saas-pricing-for-better-enterprise-discounts-in-2026/) — SaaS Reviewer (volume and bundling negotiation tactics)
- [Enterprise SaaS Cost Comparison Guide 2026](https://busistack.com/enterprise-saas-cost-comparison-guide/) — BusiStack (multi-year contract premium data from 15,000+ enterprise contracts)
- [Enterprise SaaS Contract Negotiation Tips](https://busistack.com/enterprise-saas-contract-negotiation-tips/) — BusiStack (the six-months-ahead negotiation timing advantage)
- [The Future of SaaS Pricing in 2026](https://medium.com/@aymane.bt/the-future-of-saas-pricing-in-2026-an-expert-guide-for-founders-and-leaders-a8d996892876) — Aymane Boutbati, Medium (annual price escalator ranges and hybrid pricing adoption trends)

_Digia Engage is priced per deployment, scoped to a consumer app's actual usage pattern rather than a fixed seat count or an unbanded MAU rate card, and integrates directly with an existing CleverTap, MoEngage, or WebEngage account so a layered setup adds cost only for the in-app layer itself, not a duplicated segmentation engine. [Book a demo](https://www.digia.tech/book-a-demo) to model your own cost against your specific usage pattern, or read the [ROI business case guide](https://www.digia.tech/post/roi-of-in-app-engagement-business-case-leadership/) for the full framework this article's ROI section draws on._