battles / Analytics
FullStory vs Rockerbox
FullStory ($850/mo/mo, vibe code 3/10) vs Rockerbox ($3,500/mo/mo, vibe code 3/10). FullStory is the easier one to rebuild yourself — here is what you lose either way.
Analytics
$850/mo/mo
- MVP
- 1 week
- Full replacement
- 12-24 months, due to massive DOM storage scale, real-time query engines, and client-side PII masking.
easier to rebuild
get the build prompt →Analytics
$3,500/mo/mo
- MVP
- 3-4 weeks
- Full replacement
- 12-18 months, due to complex identity resolution, continuous ad platform API updates, and advanced statistical MMM development
price gap / year
$31,800/mo
running both / year
$52,200/mo
our call
Start with FullStory — highest vibe code, weakest moat.
FullStory
Wrapping rrweb into a web app takes a few days, but storing millions of DOM diffs without killing client performance or ballooning cloud database bills requires serious infrastructure engineering. Client-side PII masking, SOC2 compliance, and fast event indexing cannot be prompt-engineered.
you can rebuild
- Client-side DOM event and mutation recording
- Basic session replay video playback
- Console logs and network request capturing
- Simple click and scroll heatmaps
- Basic funnel step tracking
what you lose
- Automated client-side PII and sensitive input field masking before data transmission
- Instant retroactive search over unindexed DOM text and element CSS selectors
- Frustration heuristics like rage clicks, dead clicks, and error clicks
- Enterprise compliance certifications including SOC2 Type II, HIPAA, and GDPR tooling
- High-throughput event ingestion infrastructure optimized for gigabytes of raw DOM diffs
real moats
- Patented client-side DOM capture and event compression engine
- Proprietary retroactive search index over raw DOM mutation streams
- SOC2 Type II, HIPAA, and strict client-side PII masking guarantees
open source escape hatches
- PostHog MIT
- OpenReplay Apache-2.0
- rrweb MIT
Rockerbox
Building a custom dashboard with basic UTM tracking is trivial, but Rockerbox combines deterministic identity resolution across fragmented ad channels with Bayesian Media Mix Modeling (MMM). Maintaining 20+ ad platform API integrations, handling ITP browser restrictions, and running reliable statistical models requires a full data engineering team.
you can rebuild
- Rule-based attribution models (First Touch, Last Touch, Linear)
- Ad spend aggregation across Meta, Google, and TikTok APIs
- Unified dashboard displaying MER (Marketing Efficiency Ratio) and CAC
- Basic UTM tracking pixel and server-side webhook collection
- Exporting aggregated revenue data to Snowflake or BigQuery
what you lose
- Pre-built probabilistic identity resolution and cross-device graph mapping
- Turnkey Media Mix Modeling (MMM) with automated carryover and saturation curves
- Pre-built connectors for linear TV, OTT, podcasts, and direct mail channels
- Managed maintenance of ad platform API breakages and rate limit updates
- Historical baseline data and automated incrementality testing frameworks
real moats
- Deep API integration density across dozens of legacy and modern ad networks
- Standardized data transformations for messy multi-channel ad spend payloads
- Proprietary cross-merchant tracking heuristics resilient to privacy updates
Questions people ask
Which is easier to rebuild with AI, FullStory or Rockerbox?
FullStory. It scores 3/10 on vibe code with a moat of 5/10, so an AI-assisted MVP takes about 1 week and a full replacement about 12-24 months, due to massive DOM storage scale, real-time query engines, and client-side PII masking..
Which one costs less, FullStory or Rockerbox?
FullStory at $850/mo/mo for a typical mid-market store. The gap between the two is about $31,800/mo a year.
What do I lose if I replace FullStory?
Automated client-side PII and sensitive input field masking before data transmission Instant retroactive search over unindexed DOM text and element CSS selectors Frustration heuristics like rage clicks, dead clicks, and error clicks
What do I lose if I replace Rockerbox?
Pre-built probabilistic identity resolution and cross-device graph mapping Turnkey Media Mix Modeling (MMM) with automated carryover and saturation curves Pre-built connectors for linear TV, OTT, podcasts, and direct mail channels
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