r2rtf
The clinical-report table engine learned Chinese, then learned to leave RTF entirely
Comparison · Analytics
bundle vs orbital
A side-by-side editorial comparison of bundle and orbital — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:tidymodelsmodel-deployment
bundle vs orbital: at a glance
What is bundle?
Four releases in three years, each one teaching the serializer about a model type it couldn't carry
bundle solves a narrow, real problem: many R model objects hold pointers to external state — compiled boosters, Java handles, torch tensors — that do not survive being saved and reloaded in another session. It wraps them so they do. The package has shipped four releases since 2022, and the shape of each is the same: extend coverage to another model class, or repair coverage that an upstream release broke.
What is orbital?
Turning fitted tidymodels into SQL, one model family at a time — and the boosting engines just landed.
orbital converts a fitted tidymodels workflow into a database expression so prediction runs where the data lives, no R session in the loop. Its value is entirely a function of coverage, and 0.5.0 was the largest coverage release yet: catboost and lightgbm boosted trees, rpart decision trees, earth-backed MARS, glmnet multinomial regression, and both randomForest and ranger random forests, all for numeric, class, and probability predictions. The 0.5.1 follow-up is corrective, fixing SQL that Snowflake and other engines rejected because it cast booleans directly to numeric.
bundle vs orbital: editorial side-by-side
bundle
0.0ANALYTICS
Four releases in three years, each one teaching the serializer about a model type it couldn't carry
◆ Current state
bundle solves a narrow, real problem: many R model objects hold pointers to external state — compiled boosters, Java handles, torch tensors — that do not survive being saved and reloaded in another session. It wraps them so they do. The package has shipped four releases since 2022, and the shape of each is the same: extend coverage to another model class, or repair coverage that an upstream release broke.
◆ Where it's heading
Coverage is the product, so the release cadence is set by the ecosystem rather than by a roadmap. dbarts arrived in 0.1.2, along with extra work to preserve xgboost's nfeatures and feature_names through a round trip; 0.1.3 exists because xgboost changed its model format again. The 0.1.1 fix — recipes steps nested inside workflows — points at the same underlying issue one level up, where the object needing bundling is buried inside a tidymodels pipeline rather than passed directly.
◆ Prediction
Expect the next release to follow the same trigger: either a new parsnip engine that carries external pointers, or another upstream format change in one of the engines already covered. xgboost has now forced two of the four releases.
orbital
0.0ANALYTICS
Turning fitted tidymodels into SQL, one model family at a time — and the boosting engines just landed.
◆ Current state
orbital converts a fitted tidymodels workflow into a database expression so prediction runs where the data lives, no R session in the loop. Its value is entirely a function of coverage, and 0.5.0 was the largest coverage release yet: catboost and lightgbm boosted trees, rpart decision trees, earth-backed MARS, glmnet multinomial regression, and both randomForest and ranger random forests, all for numeric, class, and probability predictions. The 0.5.1 follow-up is corrective, fixing SQL that Snowflake and other engines rejected because it cast booleans directly to numeric.
◆ Where it's heading
The package has been working outward in rings: recipe preprocessing steps first, then model types, then post-processing via the tailor package in 0.4.0, with show_query() added so users can inspect what actually gets sent. Recent releases show the constraint shifting from R-side translation to SQL dialect compatibility — the bugs now are about what a specific database will accept, not whether a model can be expressed. estimate_orbital_size() in 0.5.1 acknowledges the other practical limit, since generated expressions can grow large enough to matter before you generate them.
◆ Prediction
With the major boosting and ensemble engines covered, expect the next releases to keep chasing dialect-specific SQL correctness across warehouses rather than adding model families.
Alternatives to bundle and orbital
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 bundle or orbital.
DoseFinding
New stewardship at openpharma, then two releases adding the methods MCP-Mod was missing
webmockr
The stubbing library added httr2 support, then spent a year cutting itself free of everything else
crul
crul took mocking back from webmockr and made it a property of the client itself
dendroNetwork
Six releases, six identical bodies — the feed carries the package abstract instead of release notes
chattr
chattr deleted every LLM integration it had written and outsourced the lot to ellmer
See all bundle alternatives → · See all orbital alternatives →
Recent activity from bundle and orbital
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agoorbitalSnowflake-compatible SQL for dummy and NA steps
- 5mo agoorbitalcatboost, lightgbm, ranger and four more model families translate to SQL ⚡
- 8mo agoorbitalCompatibility with new xgboost versions
- 8mo agobundlexgboost bundling updated for newer model versions
- 8mo agoorbitalPost-processing adjustments from tailor become translatable
- 11mo agoorbitalPCA step translation bugs cleared
- 1y agoorbitalClass and probability predictions arrive, with glm and xgboost
- 1y agobundledbarts BART models become bundleable
- 2y agobundleRecipes steps inside workflows now bundle correctly
- 3y agobundleFirst CRAN release
Frequently asked questions
What is the difference between bundle and orbital?
Both compete on the same themes — tidymodels, model-deployment — within Analytics. bundle and orbital are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is bundle better than orbital?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. bundle and orbital are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
What are the best alternatives to bundle?
Top bundle alternatives in Analytics are ranked by recent ship velocity. Browse the "bundle alternatives" section above for the current picks, or visit /alternatives/bundle for the full list with editorial commentary on each.
What are the best alternatives to orbital?
Top orbital alternatives in Analytics are ranked by recent ship velocity. Browse the "orbital alternatives" section above for the current picks, or visit /alternatives/orbital for the full list with editorial commentary on each.