Can I vibe code Kochava?

kochava.com·mobile-attribution·$100/mo·usage

KEEP — THE UI ISN'T THE MOAT

Kochava functions as an independent referee between your app, web shop, and paid ad channels. While building an in-house ClickHouse attribution database and simple last-touch matching engine is a weekend project, the core value of an MMP lies outside the software itself. You are paying for gated network access, including privileged Meta MAM integrations, Google Install Referrer verification, and Apple SKAdNetwork postback aggregation. Unless you have standard MMP partner status with ad platforms, an AI-built clone will receive redacted install data. Additionally, maintaining native mobile SDKs for iOS, Android, React Native, and Unity across app store rule updates presents a heavy ongoing tax.

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The verdict

KEEP

Replaces

$1,500/mo

Vibe code score

3/10

MVP build time

3 weeks

Full replacement

18+ months, limited by MMP network certifications

Editorial opinion, produced with a published methodology from public information. Not a statement of fact about the vendor. How we score · Report an error · Pricing checked 2026-09-11

01

Why this verdict

Kochava's underlying event tracking and attribution logic are straightforward to code, but you cannot replicate its Mobile Measurement Partner (MMP) status. Major ad networks like Meta, Google, and Apple only transmit fine-grained install attribution data to certified MMPs.

Verdict

KEEP

Vibe code score

3/10

Moat strength

7/10

02

What it really costs

Sticker price versus what a real store ends up paying.

Entry$100/moTypical store$1,500/mo≈ estimated · 2026-09-11
Free / Pay-As-You-Gofree / quotePay per conversion event with basic reporting features
Growth$100/moBase fee + usage per tracked conversion event
Enterprise$2,500/moCustom contract with high volume limits, dedicated support, and raw data access

Charges based on monthly active users (MAU) or conversions tracked, with enterprise contracts scaling into thousands.

Where this number comes from
Captured
2026-09-11 (13 days ago)
Verified by
crawler

Assumptions: Charges based on monthly active users (MAU) or conversions tracked, with enterprise contracts scaling into thousands.

03

The one-shot build prompt

Paste it into your agent of choice. Nothing else needed.

The one-shot build promptbuild it on Lovable
Build a custom web and server-side attribution engine using Node.js, Fastify, PostgreSQL, and ClickHouse. 1. DATA MODEL: Create tables for 'campaigns' (id, network, utm_source, utm_medium, utm_campaign), 'clicks' (id, click_id, device_ip, user_agent, created_at, campaign_id), 'events' (id, user_id, event_name, event_value, timestamp), and 'attributions' (id, event_id, click_id, model, matched_at). 2. INGESTION ENDPOINTS: Expose GET /track/click to log ad clicks, capture IP/User-Agent, and generate a signed first-party click_id cookie. Expose POST /track/event to accept client/server conversion payloads containing user_id and purchase details. 3. ATTRIBUTION ENGINE: Implement a background worker using BullMQ. For every incoming purchase event, query ClickHouse for the user's click history within a 7-day lookback window. Implement last-touch attribution logic: assign 100% of revenue to the most recent valid click_id. Calculate and store touchpoint decay for multi-touch reporting. 4. REPORTING API: Provide endpoints returning ROAS, Cost Per Acquisition (CPA), and campaign-level LTV cohorts. 5. FAILURE MODES: Handle duplicate webhooks using event ID deduplication in Redis. Fall back to deterministic IP + User Agent matching if the click_id cookie is missing. Out of scope: SKAdNetwork cryptographic validation, mobile native SDK development, and SAN API authentications.

$ each button prefixes agent-specific run instructions · build your own product, never copy proprietary code, trademarks or designs

04

Scorecard

Deterministic scoring, same method for every product.

Vibe code score

3/10

Moat strength

7/10

Technical difficulty7/10
Operational burden9/10
Integration depth9/10
Data advantage7/10
Network effects8/10
Compliance load7/10

05

What you keep, what you lose

The honest trade of rebuilding it yourself.

What you can actually replace

  • Custom rule-based attribution modeling (last-touch, first-touch, multi-touch)
  • Raw event ingestion and client-side event payload validation
  • Cohort retention and customer lifetime value (LTV) dashboarding
  • UTM and custom referral link parameter parser engine
  • Data warehouse exporter to Snowflake or BigQuery

What you lose

  • ×Official MMP status with Meta, Google, TikTok, and Snap for SAN data access
  • ×Cryptographically validated Apple SKAdNetwork / AdAttributionKit decoding pipeline
  • ×SmartLinks cross-platform dynamic deep linking framework for iOS Universal Links and Android App Links
  • ×Real-time SDK spoofing and ad click-flooding fraud detection algorithms
  • ×Pre-configured server-to-server postbacks to thousands of ad networks

06

Why people still pay — the real moats

Moats

  • Official MMP partner accreditation with Self-Attributing Networks (SANs)
  • Device footprint and proprietary identity resolution graphs
  • Widespread mobile SDK deployment across thousands of high-profile apps

Hard parts

  • Handling Apple ATT consent states and windowed SKAdNetwork conversion value updates
  • Maintaining stable, low-latency mobile client SDKs across iOS and Android OS releases
  • Ingesting millions of click/impression events per minute with reliable deduplication
  • Resolving probabilistic identity matches without violating platform privacy policies
  • Continually updating ad partner postback schemas when ad networks tweak APIs
  • Managing domain infrastructure and SSL certificates for universal deep links at scale
  • Defending ad spend disputes between media buyers and ad networks using custom logs
  • Staying compliant with evolving COPPA, CCPA, and GDPR privacy mandates

Network effects you cannot generate

  • Pre-integrated network directory where ad networks auto-populate Kochava-compatible postbacks
  • Global fraud signal pooling derived from device behavior across client applications

Build this instead

Build this instead

Build this instead

07

Prior art — do not start from zero

Existing projects and paid alternatives worth pricing first.

08

Open source alternatives to Kochava

Self-hostable projects that cover most of the same ground. Free licence, your infrastructure, your on-call.

09

Have you actually replaced it?

One click, no account. It moves the ranking.

Community verdict

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Spend killed
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10

Compare

Same category, different trade-offs.

11

FAQ

+Can I really replace Kochava with an AI-generated app?

NO — YOU CANNOT SELF-HOST AN OFFICIAL AD NETWORK PARTNER AND SKAN HANDSHAKE. Kochava's underlying event tracking and attribution logic are straightforward to code, but you cannot replicate its Mobile Measurement Partner (MMP) status. Major ad networks like Meta, Google, and Apple only transmit fine-grained install attribution data to certified MMPs. An MVP takes roughly 3 weeks; matching the product properly is closer to 18+ months, limited by MMP network certifications.

+How long does it take to rebuild Kochava?

A usable internal version: 3 weeks. A version you would sell or bet a business on: 18+ months, limited by MMP network certifications, mostly spent on handling apple att consent states and windowed skadnetwork conversion value updates.

+What do you actually lose by leaving Kochava?

Official MMP status with Meta, Google, TikTok, and Snap for SAN data access Cryptographically validated Apple SKAdNetwork / AdAttributionKit decoding pipeline SmartLinks cross-platform dynamic deep linking framework for iOS Universal Links and Android App Links

+Is it legal to build a Kochava alternative?

Building a competing product with your own code is normal competition. Copying their code, trademarks, brand assets or scraping their platform is not. Use the prompt to build your own implementation of common features.

Written by EcomReStack research agent18 years in the Magento ecosystem. Last reviewed 2026-09-11.

Sources consulted

Scores are computed, not typed. Read the methodology.

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