tidyr
tidyr replaced separate() with a family that says what it does.
Comparison · Analytics
modeltime vs Parseable
A side-by-side editorial comparison of modeltime and Parseable — release velocity, themes, recent moves, and the top alternatives to consider.
modeltime vs Parseable: at a glance
What is modeltime?
modeltime built conformal intervals in, then went quiet on features.
modeltime is at 1.3.3, a single change making the package robust to xgboost version shifts. The feature weight sits in 1.3.2, which added a future-based parallel backend, the maape() accuracy metric and dials helpers for ADAM engine tuning, and further back in the 1.2.8 and 1.3.0 pair that introduced conformal prediction intervals and then carried them through the nested forecasting workflow.
What is Parseable?
Parseable's 3.0 turns a log store into a logs, metrics, traces and APM console.
Parseable has spent the 2.9 line hardening a multi-tenant ingestion engine — API keys, OAuth sync, tenant quotas, credential masking, and a run of injection and path-traversal fixes contributed from outside the core team. Version 3.0.0 collects that groundwork into a platform release: PromQL-based alerts, dashboard templates, dataset tagging, trace and ingestion endpoints, service maps and APM in the Prism UI, and a custom-provider option in the LLM flow. The ingestion story also changed shape, with fluent-bit dropped from the scripts in favour of an OpenTelemetry collector.
modeltime vs Parseable: editorial side-by-side
modeltime
0.0ANALYTICS
modeltime built conformal intervals in, then went quiet on features.
◆ Current state
modeltime is at 1.3.3, a single change making the package robust to xgboost version shifts. The feature weight sits in 1.3.2, which added a future-based parallel backend, the maape() accuracy metric and dials helpers for ADAM engine tuning, and further back in the 1.2.8 and 1.3.0 pair that introduced conformal prediction intervals and then carried them through the nested forecasting workflow.
◆ Where it's heading
The arc runs from uncertainty quantification to execution. Conformal intervals arrived first and were then threaded through nested fitting, refitting and the printed forecast tables so users can see which confidence method produced an interval. The later work moves down a layer to how forecasts are computed — a portable future backend replacing foreach tuning — rather than what they express.
◆ Prediction
With only an xgboost compatibility fix since the 1.3.2 feature release, the entries do not support a confident prediction about what comes next beyond continued dependency maintenance.
Parseable
6.3ANALYTICS
Parseable's 3.0 turns a log store into a logs, metrics, traces and APM console.
◆ Current state
Parseable has spent the 2.9 line hardening a multi-tenant ingestion engine — API keys, OAuth sync, tenant quotas, credential masking, and a run of injection and path-traversal fixes contributed from outside the core team. Version 3.0.0 collects that groundwork into a platform release: PromQL-based alerts, dashboard templates, dataset tagging, trace and ingestion endpoints, service maps and APM in the Prism UI, and a custom-provider option in the LLM flow. The ingestion story also changed shape, with fluent-bit dropped from the scripts in favour of an OpenTelemetry collector.
◆ Where it's heading
The direction is consolidation: rather than being the cheap object-store log backend that something else queries, Parseable is absorbing the query, alerting and dashboard layers that normally sit above it. PromQL support is the clearest tell — it targets teams whose alert rules are already written for a Prometheus-shaped world. Performance work is tracking that ambition too, with zstd manifests, configurable concurrent object-store calls and faster field-stats sitting alongside the feature list.
◆ Prediction
The next releases most likely fill in the metrics side to match the logs side — deeper PromQL coverage and more dashboard and alert templates — while the 3.0 UI migrations settle through point releases. Whether the LLM provider hook grows into anything beyond configuration isn't visible from these entries.
Alternatives to modeltime and Parseable
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 modeltime or Parseable.
performance
performance keeps adding ways to check a model you have already fitted.
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 modeltime alternatives → · See all Parseable alternatives →
Recent activity from modeltime and Parseable
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1d agoParseableParseable 3.0 adds PromQL alerts, APM and dashboard templates ⚡
- 21d agoParseableRelease v2.9.5
- 1mo agoParseableBugfix release v2.9.4
- 1mo agoParseableFeature release v2.9.3
- 1mo agoParseableFeature release v2.9.2
- 1mo agoParseableBug fix release v2.9.1
- 7mo agomodeltimeRobustness to xgboost version changes
- 11mo agomodeltimefuture parallel backend, maape() metric and ADAM tuning helpers
- 2y agomodeltimeConformal intervals reach the nested forecasting workflow
- 2y agomodeltimeConformal prediction intervals introduced
- 3y agomodeltimeFixes the Smooth es() model
- 3y agomodeltimeFixes failing developer-tools tests
Frequently asked questions
What is the difference between modeltime and Parseable?
They serve adjacent needs but don't currently overlap on shipped themes. Parseable 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 modeltime better than Parseable?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. Parseable 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 modeltime?
Top modeltime alternatives in Analytics are ranked by recent ship velocity. Browse the "modeltime alternatives" section above for the current picks, or visit /alternatives/modeltime for the full list with editorial commentary on each.
What are the best alternatives to Parseable?
Top Parseable alternatives in Analytics are ranked by recent ship velocity. Browse the "Parseable alternatives" section above for the current picks, or visit /alternatives/parseable for the full list with editorial commentary on each.