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Comparison · Analytics
Appfigures vs performance
A side-by-side editorial comparison of Appfigures and performance — release velocity, themes, recent moves, and the top alternatives to consider.
Appfigures vs performance: at a glance
| Feature | Appfigures | performance |
|---|---|---|
| Sector | Analytics | Analytics |
| Velocity score | 3.8 | 0.0 |
| Sparks · 30d | 1 | 0 |
| Top themes | app-analytics, agentic, aso, competitive-intelligence | r-language, model-diagnostics, bayesian, easystats |
| Last editorial update | 1d ago | 1h ago |
| Website | — | Visit → |
What is Appfigures?
Appfigures just made its app-market data something an AI agent can query, not something you screenshot.
Appfigures has spent the last year widening what its estimates cover — iPad data folded into every download and revenue figure, state-level financials in the API, a 15-report App Intelligence suite for competitor research, and Leaderboards that rank apps by explicit metrics instead of opaque store charts. The August release changes who consumes all of that: a CLI built specifically for AI agents, with a hinting system to keep them from misreading the data. The product is no longer only a dashboard.
What is performance?
performance keeps adding ways to check a model you have already fitted.
performance is at 0.17.1, which added check_priors() for prior predictive checks on Bayesian models and gave check_overdispersion(), check_model() and check_predictions() arguments to control residual type and plot range. The releases before it are a similar mix: a -2LL criterion column in test_likelihoodratio(), Bayesian predictive checks routed through modelbased, and in 0.16.0 a set of breaking renames including RMSA to the correct RMSR.
Appfigures vs performance: editorial side-by-side
Appfigures
3.8ANALYTICS
Appfigures just made its app-market data something an AI agent can query, not something you screenshot.
◆ Current state
Appfigures has spent the last year widening what its estimates cover — iPad data folded into every download and revenue figure, state-level financials in the API, a 15-report App Intelligence suite for competitor research, and Leaderboards that rank apps by explicit metrics instead of opaque store charts. The August release changes who consumes all of that: a CLI built specifically for AI agents, with a hinting system to keep them from misreading the data. The product is no longer only a dashboard.
◆ Where it's heading
The arc runs from data completeness to data access. First they closed gaps in the underlying estimates, then they built more ways to slice them, and now they are exposing the whole surface to agents that can investigate, compare, monitor, and act — including replying to reviews and adjusting Apple Ads campaigns. Each layer assumes the one below it is trustworthy, which is why the accuracy fixes (iPad coverage, keyword popularity, Google Play delay removal) came first.
◆ Prediction
Expect the agent surface to deepen before it widens — more write actions exposed through the CLI, and Leaderboards and App Intelligence reports made directly queryable by agents rather than only through the web reports.
performance
0.0ANALYTICS
performance keeps adding ways to check a model you have already fitted.
◆ Current state
performance is at 0.17.1, which added check_priors() for prior predictive checks on Bayesian models and gave check_overdispersion(), check_model() and check_predictions() arguments to control residual type and plot range. The releases before it are a similar mix: a -2LL criterion column in test_likelihoodratio(), Bayesian predictive checks routed through modelbased, and in 0.16.0 a set of breaking renames including RMSA to the correct RMSR.
◆ Where it's heading
Two consistent habits. Diagnostics keep gaining arguments to narrow what is examined — ppc_range, x_limits, maximum_dots, show_ci — which reads as a package being used on models large and awkward enough that the defaults stopped working. And simulated residuals via DHARMa keep displacing standard ones as the basis for the checks themselves.
◆ Prediction
With check_priors() newly added and Bayesian predictive checks now routed through modelbased, the next release most likely extends the Bayesian diagnostic set rather than reworking the frequentist checks.
Alternatives to Appfigures and performance
Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either Appfigures or performance.
modeltime
modeltime built conformal intervals in, then went quiet on features.
cmdstanpy
CmdStanPy is clearing deprecations ahead of a 2.0 it keeps announcing.
dowhy
DoWhy adds one estimation method a year and keeps its identification edge.
OpenHouse
OpenHouse is hardening the seams where table policies and jobs quietly fail.
silx
silx 3.0 moved its default Qt binding to PySide6 — a migration for everyone embedding it.
See all Appfigures alternatives → · See all performance alternatives →
Recent activity from Appfigures and performance
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1d agoAppfiguresIntroducing the Appfigures CLI ⚡
- 1mo agoperformancecheck_priors() added; overdispersion plots use simulated residuals
- 1mo agoAppfiguresIntroducing Appfigures Leaderboards
- 2mo agoperformance-2LL criterion column and unified Bayesian predictive checks
- 3mo agoAppfiguresApp Intelligence for iPad
- 6mo agoperformanceBreaking renames plus point-count and CI controls in check_model()
- 8mo agoAppfiguresSupport for By State Financial Reports via the API
- 8mo agoperformancecheck_autocorrelation() methods for DHARMa objects
- 8mo agoAppfiguresDaily Averages Across Reports
- 8mo agoAppfiguresIntroducing the New App Intelligence ⚡
- 10mo agoperformanceFixes CRAN checks after an rstanarm update
- 11mo agoperformancetinytable output format in display()
Frequently asked questions
What is the difference between Appfigures and performance?
They serve adjacent needs but don't currently overlap on shipped themes. Appfigures is currently shipping more aggressively (velocity 3.8 vs 0.0), with 1 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is Appfigures better than performance?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. Appfigures is currently shipping more aggressively (velocity 3.8 vs 0.0), with 1 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
What are the best alternatives to Appfigures?
Top Appfigures alternatives in Analytics are ranked by recent ship velocity. Browse the "Appfigures alternatives" section above for the current picks, or visit /alternatives/appfigures for the full list with editorial commentary on each.
What are the best alternatives to performance?
Top performance alternatives in Analytics are ranked by recent ship velocity. Browse the "performance alternatives" section above for the current picks, or visit /alternatives/easystats-performance for the full list with editorial commentary on each.