iOS App Analytics Guide — TelemetryDeck vs PostHog Implementation (2026)

I shipped my first three apps without analytics. Zero instrumentation, zero visibility, zero clue why users churned after three days. I would stare at App Store Connect downloads — the only number I had — and wonder why people were uninstalling. It was like driving blindfolded.

When I finally added TelemetryDeck to my fourth app, I discovered that 68% of users never completed onboarding. They dropped off on screen two because a permission prompt scared them. A one-line copy change fixed it, and seven-day retention jumped from 11% to 29%. That single insight paid for years of analytics tooling.

This guide walks you through everything: why analytics matter for indie developers, how to choose between TelemetryDeck and PostHog, how to implement both in Swift, the exact events every app should track, and how to stay compliant with App Tracking Transparency and GDPR. If you are shipping an iOS app in 2026 without analytics, you are guessing. Let us stop guessing.

Why Analytics Matter for Indie iOS Developers

App Store Connect gives you downloads, crash counts, and revenue. That is like running a restaurant where the only feedback you get is how many people walked through the door and how much money was in the register at closing time. You have no idea which dishes people ordered, where they sat, or whether the waiter was rude.

Product analytics close that gap. With proper instrumentation you can answer specific questions:

• Where do users drop off? If 40% abandon onboarding on step 3, you know exactly what to fix.
• Which features drive retention? If users who use feature X have 3x higher 30-day retention, you know what to double down on.
• Is the paywall converting? If 200 users see the paywall daily but only 2 convert, the problem is pricing or positioning — not traffic.
• Do new versions improve or regress? Comparing retention cohorts between v1.2 and v1.3 tells you whether your latest release actually helped.
• What do power users do differently? Understanding the behavioral gap between one-time users and subscribers reveals your product's "aha moment."

The ROI is straightforward: one insight from analytics can 2-3x your retention or conversion rate. No amount of feature work competes with that leverage.

The Privacy-First Approach: ATT, GDPR, and Apple's Rules

Before choosing a tool, you need to understand the privacy landscape in 2026. Apple has made it very clear: user privacy is non-negotiable on their platform.

App Tracking Transparency (ATT)

Since iOS 14.5, apps that track users across other apps and websites must show the ATT prompt. Here is the critical distinction most developers miss: first-party analytics do not require the ATT prompt. If you are tracking events within your own app to understand your own users, and you are not sharing that data with third parties for advertising, you do not need ATTrackingManager.requestTrackingAuthorization().

Both TelemetryDeck and PostHog (self-hosted) fall into the first-party category. Firebase Analytics can too, but Google's data practices make this murky, and Apple reviewers have flagged apps for it.

GDPR and International Compliance

If your app is available in the EU (and it is, unless you specifically exclude those storefronts), you must comply with GDPR. The good news: anonymous, aggregated analytics with no personal data do not require consent under GDPR. TelemetryDeck is specifically designed for this. PostHog requires more careful configuration — you need to disable personal data collection and ideally self-host in the EU.

App Store Privacy Nutrition Labels

When you submit to the App Store, you must declare your data collection practices. With TelemetryDeck, you can honestly check "Data Not Collected" for all categories because it uses differential privacy and never collects personal identifiers. With PostHog, you will likely need to declare "Analytics — Usage Data" linked to the user unless you strip all identifiers.

Rule of thumb: if your analytics tool requires the ATT prompt or makes you declare personal data collection, it is costing you users. Up to 80% of users deny tracking when prompted. Choose tools that let you skip the prompt entirely.

TelemetryDeck vs PostHog vs Firebase vs Mixpanel

Here is how the four most relevant analytics platforms compare for indie iOS developers in 2026:

My recommendation: Start with TelemetryDeck if you are an indie developer who values simplicity and privacy. It takes 5 minutes to integrate, requires no ATT prompt, and the free tier covers most indie apps. Move to PostHog when you need feature flags, A/B testing, or deeper product analytics. Many developers run both — TelemetryDeck for lightweight event tracking and PostHog for experimentation.

Setting Up TelemetryDeck in Swift

TelemetryDeck is purpose-built for Swift developers. The SDK is lightweight, the API is clean, and you can be sending events in under 5 minutes.

Step 1: Create a TelemetryDeck Account

Sign up at dashboard.telemetrydeck.com, create a new app, and copy your App ID (a UUID string). That is the only credential you need.

Step 2: Add the SDK via Swift Package Manager

In Xcode, go to File → Add Package Dependencies and enter:

https://github.com/TelemetryDeck/SwiftSDK

Select the latest version and add TelemetryDeck to your app target.

Step 3: Initialize in Your App Entry Point

import SwiftUI
import TelemetryDeck

@main
struct MyApp: App {
    init() {
        let config = TelemetryDeck.Config(
            appID: "YOUR-APP-ID-HERE"
        )
        TelemetryDeck.initialize(config: config)
    }

    var body: some Scene {
        WindowGroup {
            ContentView()
        }
    }
}

That is it for basic setup. TelemetryDeck will automatically track app launches and sessions with zero additional code.

Step 4: Send Custom Events

// Simple event
TelemetryDeck.signal("onboarding_completed")

// Event with metadata parameters
TelemetryDeck.signal(
    "paywall_viewed",
    parameters: [
        "source": "settings_screen",
        "plan_shown": "annual",
        "trial_eligible": "true"
    ]
)

// Track feature usage
TelemetryDeck.signal(
    "feature_used",
    parameters: [
        "feature": "export_pdf",
        "item_count": "\(items.count)"
    ]
)

// Track errors without PII
TelemetryDeck.signal(
    "error_occurred",
    parameters: [
        "type": "network_timeout",
        "endpoint": "sync_data",
        "retry_count": "\(retryCount)"
    ]
)

Step 5: Build an Analytics Service Layer

Do not scatter TelemetryDeck.signal() calls throughout your codebase. Wrap everything in a service:

import TelemetryDeck

enum AnalyticsEvent: String {
    // Onboarding
    case onboardingStarted = "onboarding_started"
    case onboardingStepViewed = "onboarding_step_viewed"
    case onboardingCompleted = "onboarding_completed"
    case onboardingSkipped = "onboarding_skipped"

    // Paywall
    case paywallViewed = "paywall_viewed"
    case paywallDismissed = "paywall_dismissed"
    case purchaseStarted = "purchase_started"
    case purchaseCompleted = "purchase_completed"
    case purchaseFailed = "purchase_failed"
    case trialStarted = "trial_started"

    // Core features
    case featureUsed = "feature_used"
    case itemCreated = "item_created"
    case itemDeleted = "item_deleted"
    case searchPerformed = "search_performed"
    case exportCompleted = "export_completed"

    // Engagement
    case appOpened = "app_opened"
    case sessionDuration = "session_duration"
    case settingsChanged = "settings_changed"
    case shareCompleted = "share_completed"
    case ratingPromptShown = "rating_prompt_shown"
    case ratingSubmitted = "rating_submitted"

    // Errors
    case errorOccurred = "error_occurred"
    case syncFailed = "sync_failed"
}

final class AnalyticsService {
    static let shared = AnalyticsService()

    func track(
        _ event: AnalyticsEvent,
        parameters: [String: String] = [:]
    ) {
        TelemetryDeck.signal(
            event.rawValue,
            parameters: parameters
        )
    }
}

// Usage throughout the app:
AnalyticsService.shared.track(.paywallViewed, parameters: [
    "source": "onboarding",
    "plan": "annual"
])

This pattern gives you autocomplete on event names, prevents typos, and makes it trivial to swap analytics providers later without touching every view.

Setting Up PostHog in Swift

PostHog gives you a full product analytics suite: events, funnels, retention, feature flags, A/B testing, and even session replay. It is more complex than TelemetryDeck but far more powerful if you need experimentation capabilities.

Step 1: Create a PostHog Project

Sign up at posthog.com (or self-host with Docker). Create a project and grab your API key and host URL. If you are using the cloud version, the host is https://us.i.posthog.com or https://eu.i.posthog.com depending on your region.

Step 2: Add the SDK via SPM

https://github.com/PostHog/posthog-ios

Add PostHog to your app target.

Step 3: Initialize PostHog

import SwiftUI
import PostHog

@main
struct MyApp: App {
    init() {
        let config = PostHogConfig(
            apiKey: "phc_YOUR_API_KEY_HERE",
            host: "https://us.i.posthog.com"
        )

        // Privacy-first configuration
        config.captureApplicationLifecycleEvents = true
        config.captureScreenViews = true
        config.sessionReplay = false  // Disable unless needed
        config.personProfiles = .identifiedOnly

        PostHogSDK.shared.setup(config)
    }

    var body: some Scene {
        WindowGroup {
            ContentView()
        }
    }
}

Step 4: Track Events and Identify Users

// Track a simple event
PostHogSDK.shared.capture("onboarding_completed")

// Track with properties
PostHogSDK.shared.capture(
    "paywall_viewed",
    properties: [
        "source": "settings_screen",
        "plan_shown": "annual",
        "trial_eligible": true,
        "days_since_install": 3
    ]
)

// Identify user after authentication (optional)
PostHogSDK.shared.identify(
    userId,  // Use your internal user ID, not email
    userProperties: [
        "subscription_tier": "premium",
        "app_version": Bundle.main.appVersion
    ]
)

// Track when a user upgrades their subscription
PostHogSDK.shared.capture(
    "purchase_completed",
    properties: [
        "product_id": "annual_premium",
        "price": 49.99,
        "currency": "USD",
        "trial_converted": true
    ]
)

// Reset on sign out (clears anonymous ID)
PostHogSDK.shared.reset()

Step 5: Feature Flags and A/B Testing

This is where PostHog pulls ahead of TelemetryDeck. You can run experiments directly from the PostHog dashboard without shipping app updates:

// Check a boolean feature flag
if PostHogSDK.shared.isFeatureEnabled("new_onboarding_flow") {
    showNewOnboarding()
} else {
    showClassicOnboarding()
}

// Get a multivariate flag value
let variant = PostHogSDK.shared.getFeatureFlag(
    "paywall_experiment"
) as? String

switch variant {
case "control":
    showOriginalPaywall()
case "variant_a":
    showDiscountPaywall()
case "variant_b":
    showTestimonialPaywall()
default:
    showOriginalPaywall()
}

// Reload flags after user state changes
PostHogSDK.shared.reloadFeatureFlags()

PostHog automatically tracks which variant each user saw and lets you compare conversion rates, retention, and any custom metric between groups. This is incredibly powerful for optimizing paywalls, onboarding flows, and feature rollouts.

Essential Events Every App Should Track

Do not track everything. Track what changes your decisions. Here is the essential event taxonomy that applies to virtually every iOS app:

Start with 10-15 events. You can always add more later, but removing events you thought you needed is a sign of poor upfront planning. Every event should map to a question you are trying to answer or a metric you are trying to move.

Funnel Analysis: Finding Where Users Drop Off

A funnel is a sequence of events where you measure how many users progress from one step to the next. The two most important funnels for any iOS app are:

Onboarding Funnel

1. app_opened (first launch)
2. onboarding_started
3. onboarding_step_viewed (step 1)
4. onboarding_step_viewed (step 2)
5. onboarding_step_viewed (step 3)
6. onboarding_completed

A healthy onboarding funnel has 70-85% completion. If you are below 60%, something is wrong — either the flow is too long, a permission prompt is scaring users, or the value proposition is unclear. In TelemetryDeck, you build this funnel in the dashboard by selecting events in sequence. In PostHog, use the Funnels insight type and drop in the events.

Purchase Funnel

1. paywall_viewed
2. purchase_started
3. purchase_completed

Benchmark conversion rates: 2-5% of paywall views should convert. If paywall_viewed to purchase_started is low, the paywall design or pricing needs work. If purchase_started to purchase_completed is low, there is a technical issue (failed StoreKit transaction, network error) or the Apple payment sheet is confusing users.

Both TelemetryDeck and PostHog can visualize these funnels. PostHog adds the ability to break down funnels by properties (e.g., "show me this funnel only for users who came from onboarding vs. settings") and run A/B tests on different funnel variations.

Retention Metrics and Cohort Analysis

Downloads mean nothing if users leave after day one. Retention is the metric that determines whether your app will survive. Here are the retention benchmarks for iOS apps in 2026:

Setting Up Retention Tracking

In TelemetryDeck, retention is calculated automatically based on returning signals from the same anonymous user hash. The dashboard gives you a retention matrix out of the box — no additional events needed beyond your standard tracking.

In PostHog, use the Retention insight type. Define your "start event" (usually first app_opened) and your "return event" (usually any subsequent app_opened). PostHog generates a cohort matrix showing what percentage of users who started on each date came back on subsequent days.

Cohort Analysis: Finding Your Best Users

Cohort analysis groups users by a shared characteristic and compares their behavior. The most useful cohorts for indie apps:

• Install date cohort: Compare retention for users who installed during v1.2 vs. v1.3. Did your latest release actually improve retention?
• Onboarding completion cohort: Users who completed onboarding vs. those who skipped. If the gap is huge, invest in onboarding improvements.
• Feature activation cohort: Users who used feature X within 7 days vs. those who did not. This reveals your "aha moment."
• Subscription cohort: Compare behavior of free users, trial users, and paying subscribers. What do paying users do differently in their first session?

PostHog excels at cohort analysis with its behavioral cohort builder. TelemetryDeck supports basic cohorts through signal filtering and group-by queries. For most indie apps, TelemetryDeck's cohort capabilities are sufficient. If you need to build complex behavioral cohorts (e.g., "users who completed onboarding AND used feature X within 3 days AND viewed the paywall but did not purchase"), PostHog is the stronger choice.

A/B Testing with Feature Flags

A/B testing is the single most impactful optimization technique available to you. Instead of guessing whether a new paywall design will convert better, you ship both versions, split traffic, and let the data decide. Here are the highest-impact experiments for indie iOS apps:

• Paywall design: Test different layouts, copy, pricing emphasis. This is consistently the highest-ROI experiment.
• Onboarding length: 3 screens vs. 5 screens. Shorter is not always better — sometimes more context improves activation.
• Free trial duration: 3-day vs. 7-day vs. 14-day. Longer trials can increase conversion but also increase churn if the product is not sticky enough.
• Feature gating: Which features should be free vs. premium? Get this wrong and you either give away too much or gate too aggressively.
• Push notification timing: When should you send the first retention push? Day 1 vs. Day 3 can make a meaningful difference.

PostHog handles all of this natively. Create an experiment in the dashboard, define your variants, select your target metric, and PostHog handles random assignment, sample size calculation, and statistical significance. Here is the implementation pattern:

import PostHog

final class ExperimentService {
    static let shared = ExperimentService()

    /// Check paywall experiment variant
    var paywallVariant: PaywallVariant {
        let flag = PostHogSDK.shared.getFeatureFlag(
            "paywall_experiment_v2"
        ) as? String

        switch flag {
        case "discount_first":
            return .discountFirst
        case "social_proof":
            return .socialProof
        default:
            return .control
        }
    }

    /// Check if new onboarding is enabled
    var isNewOnboardingEnabled: Bool {
        PostHogSDK.shared.isFeatureEnabled(
            "new_onboarding_flow_v3"
        )
    }

    /// Get trial duration from experiment
    var trialDays: Int {
        let flag = PostHogSDK.shared.getFeatureFlag(
            "trial_duration_experiment"
        ) as? String

        switch flag {
        case "seven_days": return 7
        case "fourteen_days": return 14
        default: return 3
        }
    }
}

enum PaywallVariant {
    case control
    case discountFirst
    case socialProof
}

If you are using TelemetryDeck (which does not have built-in A/B testing), you can implement basic feature flags with a simple local configuration and track the variant as an event parameter. For proper multivariate testing with statistical significance, you will want PostHog or a dedicated experimentation platform.

Error and Crash Tracking Through Analytics

While dedicated crash reporters like Sentry or Firebase Crashlytics are more powerful for stack traces, your analytics layer can catch errors that crash reporters miss — non-fatal errors, degraded experiences, and silent failures.

// Track non-fatal errors via analytics
func trackError(
    _ error: Error,
    context: String,
    isFatal: Bool = false
) {
    let params: [String: String] = [
        "error_type": String(describing: type(of: error)),
        "error_message": error.localizedDescription,
        "context": context,
        "is_fatal": "\(isFatal)",
        "app_version": Bundle.main.appVersion,
        "os_version": UIDevice.current.systemVersion
    ]

    // Send to TelemetryDeck
    TelemetryDeck.signal("error_occurred", parameters: params)

    // Or send to PostHog
    PostHogSDK.shared.capture(
        "error_occurred",
        properties: params
    )
}

// Usage in a network call
func fetchUserData() async {
    do {
        let data = try await apiClient.getUserProfile()
        processData(data)
    } catch {
        trackError(error, context: "fetch_user_profile")
        showFallbackUI()
    }
}

// Usage in a purchase flow
func handlePurchase(productID: String) async {
    do {
        let result = try await purchaseManager.buy(productID)
        AnalyticsService.shared.track(.purchaseCompleted, parameters: [
            "product_id": productID,
            "price": "\(result.price)"
        ])
    } catch {
        trackError(error, context: "purchase_flow")
        AnalyticsService.shared.track(.purchaseFailed, parameters: [
            "product_id": productID,
            "error_code": "\((error as NSError).code)"
        ])
    }
}

The analytics approach to error tracking is complementary to Sentry or Crashlytics. Use the crash reporter for stack traces and debugging. Use analytics errors to understand the business impact — how many users hit this error, on which screen, and did they churn afterward?

Revenue Analytics with RevenueCat Integration

If you are using RevenueCat for subscriptions (and you should be — see our monetization guide), you can pipe purchase events directly to your analytics platform for unified reporting.

RevenueCat + TelemetryDeck

import RevenueCat
import TelemetryDeck

final class PurchaseAnalytics: NSObject, PurchasesDelegate {

    func purchases(
        _ purchases: Purchases,
        receivedUpdated customerInfo: CustomerInfo
    ) {
        let isActive = customerInfo
            .entitlements["premium"]?
            .isActive == true

        TelemetryDeck.signal(
            "subscription_status_changed",
            parameters: [
                "is_premium": "\(isActive)",
                "active_subscriptions": "\(customerInfo.activeSubscriptions.count)"
            ]
        )
    }
}

// Track purchase events in your paywall view
func onPurchaseTapped(package: Package) async {
    TelemetryDeck.signal("purchase_started", parameters: [
        "product_id": package.storeProduct.productIdentifier,
        "price": "\(package.storeProduct.price)",
        "period": package.packageType.description
    ])

    do {
        let result = try await Purchases.shared.purchase(package: package)
        if !result.userCancelled {
            TelemetryDeck.signal("purchase_completed", parameters: [
                "product_id": package.storeProduct.productIdentifier,
                "price": "\(package.storeProduct.price)"
            ])
        } else {
            TelemetryDeck.signal("purchase_cancelled")
        }
    } catch {
        TelemetryDeck.signal("purchase_failed", parameters: [
            "error": error.localizedDescription
        ])
    }
}

RevenueCat + PostHog

RevenueCat has a native PostHog integration. Enable it in the RevenueCat dashboard under Integrations → PostHog, enter your PostHog API key and host, and RevenueCat will automatically send purchase, renewal, cancellation, and billing events to PostHog. No client-side code needed beyond the initial setup.

This gives you a unified view: you can see the entire user journey from install → onboarding → feature usage → paywall view → purchase → renewal (or churn) in a single PostHog dashboard.

App Store Privacy Nutrition Labels: What to Declare

When submitting your app, App Store Connect asks you to declare your data collection practices. Here is what to declare for each analytics provider:

The privacy nutrition label matters because users see it before downloading your app. An app that shows "Data Not Collected" has a meaningful advantage over one with a long list of data categories. This is another reason TelemetryDeck is compelling for indie apps — your privacy label stays clean.

Building a Dual Analytics Architecture

The most effective setup for indie developers who want both simplicity and power is running TelemetryDeck and PostHog side by side. TelemetryDeck handles day-to-day event monitoring with zero privacy overhead. PostHog handles experimentation, feature flags, and deep product analysis. Here is how to structure this cleanly:

import TelemetryDeck
import PostHog

protocol AnalyticsProvider {
    func track(_ event: String, properties: [String: String])
    func identify(userId: String)
    func reset()
}

final class TelemetryDeckProvider: AnalyticsProvider {
    func track(_ event: String, properties: [String: String]) {
        TelemetryDeck.signal(event, parameters: properties)
    }
    func identify(userId: String) {
        // TelemetryDeck uses anonymous hashed IDs automatically
    }
    func reset() {
        // No user state to reset
    }
}

final class PostHogProvider: AnalyticsProvider {
    func track(_ event: String, properties: [String: String]) {
        PostHogSDK.shared.capture(event, properties: properties)
    }
    func identify(userId: String) {
        PostHogSDK.shared.identify(userId)
    }
    func reset() {
        PostHogSDK.shared.reset()
    }
}

final class AnalyticsManager {
    static let shared = AnalyticsManager()
    private var providers: [AnalyticsProvider] = []

    func configure() {
        // Initialize TelemetryDeck
        let tdConfig = TelemetryDeck.Config(
            appID: "YOUR-TD-APP-ID"
        )
        TelemetryDeck.initialize(config: tdConfig)
        providers.append(TelemetryDeckProvider())

        // Initialize PostHog
        let phConfig = PostHogConfig(
            apiKey: "phc_YOUR_KEY",
            host: "https://us.i.posthog.com"
        )
        phConfig.captureApplicationLifecycleEvents = true
        PostHogSDK.shared.setup(phConfig)
        providers.append(PostHogProvider())
    }

    func track(_ event: String, properties: [String: String] = [:]) {
        providers.forEach { $0.track(event, properties: properties) }
    }

    func identify(userId: String) {
        providers.forEach { $0.identify(userId: userId) }
    }

    func reset() {
        providers.forEach { $0.reset() }
    }
}

This pattern has three benefits. First, every event fires to both platforms automatically. Second, swapping or removing a provider requires changing one file instead of hundreds. Third, you can add new providers (Amplitude, Sentry, your own endpoint) without touching any view code.

Common Mistakes to Avoid

After integrating analytics into a dozen apps (some well, some poorly), here are the mistakes I see most often:

1. Tracking too many events from day one. You end up with 200 events, 180 of which you never look at. Start with 10-15 and add more when you have a specific question you cannot answer.
2. Inconsistent naming conventions. paywall_viewed, PaywallViewed, paywall-viewed, and viewedPaywall in the same codebase will make your funnels useless. Pick snake_case and enforce it with an enum.
3. Not tracking the negative path. Everyone tracks purchase_completed. Few track purchase_failed, paywall_dismissed, or onboarding_skipped. The negative events are where the insights live.
4. Sending PII by accident. Double-check that you never include email addresses, names, or device identifiers in event properties. Use an internal user ID if you must identify users.
5. Not testing analytics in development. Fire every event in debug mode and verify it shows up in the dashboard before shipping. A misconfigured SDK that silently drops events is worse than no analytics at all.
6. Ignoring the data. Analytics are useless if you never open the dashboard. Set a weekly reminder to review your key funnels and retention cohorts. Make it a habit, not an afterthought.

Putting It All Together: A Complete Implementation Checklist

Here is the step-by-step checklist for adding analytics to your iOS app the right way:

1. Choose your provider(s). TelemetryDeck for privacy-first simplicity. PostHog if you need feature flags and A/B testing. Both if you want the best of both worlds.
2. Define your event taxonomy. Write down every event name, its category, and its properties before writing any code. Use the table above as a starting point.
3. Build an analytics service layer. Use the enum + service pattern from this guide. Never scatter raw SDK calls throughout your views.
4. Implement core events. Onboarding funnel, paywall funnel, feature usage, and error tracking. These cover 80% of the insights you need.
5. Set up dashboards. Create an onboarding funnel, a purchase funnel, a retention chart, and an error rate chart. These four dashboards should be your weekly review.
6. Integrate with RevenueCat. Pipe purchase events into your analytics for unified revenue reporting.
7. Configure App Store privacy labels. Declare your data collection accurately. TelemetryDeck makes this easy — PostHog requires more careful configuration.
8. Test in development. Verify every event fires correctly in debug mode before shipping.
9. Review weekly. Analytics only create value when you act on them. Block 30 minutes every week to review funnels and retention.

Getting Started with Pre-Integrated Analytics

If you are building a new iOS app from scratch, wiring up analytics alongside auth, onboarding, paywalls, and your backend takes days of integration work. Every service needs to be initialized in the right order, event names need to be consistent, and the service layer needs to be architected cleanly from the start.

The Swift Kit ships with TelemetryDeck and PostHog pre-integrated. The analytics service layer, event taxonomy, onboarding funnel tracking, paywall conversion tracking, and error monitoring are all wired up and ready to go. You configure your API keys in one file, and the entire analytics pipeline starts working. No setup friction, no scattered SDK calls, no naming inconsistencies.

Check the features page to see the full analytics integration, review the documentation for configuration details, or head straight to pricing to grab the kit and start tracking what matters. For a broader view of the full indie tech stack, our 2026 tech stack guide covers every tool you need beyond analytics.