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Comparison · Analytics
DHARMa vs Omni
A side-by-side editorial comparison of DHARMa and Omni — release velocity, themes, recent moves, and the top alternatives to consider.
DHARMa vs Omni: at a glance
What is DHARMa?
DHARMa changed how GLMM residuals are simulated, so the same code now returns different numbers.
DHARMa generates scaled quantile residuals for fitted GLMMs and runs the dispersion, uniformity, and autocorrelation tests built on them. Version 0.5.0 changed the default simulation for hierarchical models from the model's own default, mostly unconditional, to conditional simulation, and states plainly that residuals will differ from those computed by older versions. The same release added brms to the supported model set and reworked how predictors are passed to plotting and testing functions.
What is Omni?
Omni ships weekly, and almost every week the headline item is an AI feature.
Omni publishes a dated weekly digest whose body is a single line listing that week's items, so each entry compresses several releases into a sentence. Across the window the pattern is unmistakable: AI-powered semantic model generation reaching general availability, AI Routines creatable from chat and deliverable to Slack, AI model suggestion endpoints, AI credit controls scoped to embed entity groups and individual users, AI Evals on Azure, and MCP surfaces appearing both in-app and as a searchDashboards tool. The most recent week breaks that streak — default filters on composite topics, stopping a running dashboard query, full-screen preview editing — the first digest in two months led by conventional BI work.
DHARMa vs Omni: editorial side-by-side
DHARMa
0.0ANALYTICS
DHARMa changed how GLMM residuals are simulated, so the same code now returns different numbers.
◆ Current state
DHARMa generates scaled quantile residuals for fitted GLMMs and runs the dispersion, uniformity, and autocorrelation tests built on them. Version 0.5.0 changed the default simulation for hierarchical models from the model's own default, mostly unconditional, to conditional simulation, and states plainly that residuals will differ from those computed by older versions. The same release added brms to the supported model set and reworked how predictors are passed to plotting and testing functions.
◆ Where it's heading
The package has spent several releases widening which model backends it can diagnose, from glmmTMB through mgcv, phylolm and now brms, while methodological work has gone into handling correlated residuals via the rotation argument. Version 0.5.0 shifts from adding coverage to changing defaults for statistical power. The formula interface arriving across plotResiduals, testCategorical, testQuantiles and the autocorrelation tests suggests the API is being unified rather than extended function by function.
◆ Prediction
The next releases will likely broaden brms support past the simple-model restriction and continue converting remaining functions to the formula interface.
Omni
6.3ANALYTICS
Omni ships weekly, and almost every week the headline item is an AI feature.
◆ Current state
Omni publishes a dated weekly digest whose body is a single line listing that week's items, so each entry compresses several releases into a sentence. Across the window the pattern is unmistakable: AI-powered semantic model generation reaching general availability, AI Routines creatable from chat and deliverable to Slack, AI model suggestion endpoints, AI credit controls scoped to embed entity groups and individual users, AI Evals on Azure, and MCP surfaces appearing both in-app and as a searchDashboards tool. The most recent week breaks that streak — default filters on composite topics, stopping a running dashboard query, full-screen preview editing — the first digest in two months led by conventional BI work.
◆ Where it's heading
Two things have been happening in parallel and they are related. Omni pushed AI into the modelling layer rather than only the query layer, which is what semantic model generation reaching GA signified, then built the commercial and access controls those features require — credit limits per user and per embed entity group arrived within weeks of the capabilities that consume them. The MCP work points at a third direction, exposing Omni's content to external agents rather than only serving its own chat. The latest week's return to filters and query controls suggests the AI surface has reached the point where the surrounding product has to catch up to it.
◆ Prediction
With searchDashboards already shipped as an MCP tool, more of Omni's catalog is the obvious next thing to expose that way, and credit controls should keep extending to cover newer AI surfaces. Whether the non-AI week is a pause or a genuine rebalancing is not something one digest can settle.
Alternatives to DHARMa and Omni
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 DHARMa or Omni.
silx
silx settles into maintenance a release after its PySide6 migration
Plotly
Plotly is turning its cloud into a metered compute platform with an enterprise on-ramp.
aniread
aniread stops asking you to know which tracker wrote the file
Rho
Rho's release machinery finally produced a stable build — and it shipped no new product.
Usermaven
Usermaven closed the loop: data comes in from anywhere, and now it goes back out.
Recent activity from DHARMa and Omni
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 17h agoOmniOmni adds default filters on composite topics and query stopping
- 8d agoOmniOmni adds presentation mode and a searchDashboards MCP tool
- 15d agoOmniOmni adds AI credit controls per user and embed entity group
- 22d agoOmniAI semantic model generation goes generally available in Omni ⚡
- 29d agoOmniOmni adds AI suggestion endpoints and OAuth for database connections
- 1mo agoOmniOmni brings AI routines to Slack and adds in-app MCP settings
- 2mo agoDHARMaConditional simulation becomes the GLMM default, changing residuals ⚡
- 1y agoDHARMaDHARMa 0.4.7
- 3y agoDHARMaDHARMa 0.4.6
- 4y agoDHARMaDHARMa 0.4.5
- 4y agoDHARMaDHARMa 0.4.4
- 5y agoDHARMaDHARMa 0.4.3
Frequently asked questions
What is the difference between DHARMa and Omni?
They serve adjacent needs but don't currently overlap on shipped themes. Omni is currently shipping more aggressively (velocity 6.3 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 DHARMa better than Omni?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. Omni is currently shipping more aggressively (velocity 6.3 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 DHARMa?
Top DHARMa alternatives in Analytics are ranked by recent ship velocity. Browse the "DHARMa alternatives" section above for the current picks, or visit /alternatives/dharma for the full list with editorial commentary on each.
What are the best alternatives to Omni?
Top Omni alternatives in Analytics are ranked by recent ship velocity. Browse the "Omni alternatives" section above for the current picks, or visit /alternatives/omni for the full list with editorial commentary on each.