Kaskada
Real-time feature engineering platform for ML using event-based data and temporal query language.
Our Verdict
Powerful when time-series features are the bottleneck; otherwise a simpler feature store wins.
Pros
- Purpose-built for real-time ML features
- Temporal query language handles time joins cleanly
- Open-source core lowers lock-in risk
Cons
- Niche tech — small community and talent pool
- Learning curve on temporal DSL
- Roadmap uncertain after DataStax acquisition
Best for: ML teams with heavy event-based streaming feature requirements
Not for: Batch-heavy ML or teams without real-time feature pain
When to Use Kaskada
Good fit if you need
- Computing time-travel features from event streams for ML
- Building real-time feature pipelines with temporal semantics
- Generating training examples with point-in-time correct joins
- Accelerating feature engineering for recommendation systems
Lock-in Assessment
High 2/5
Lock-in Score 2/5
Pricing
Price wrong?Kaskada Pricing
- Pricing Model
- custom
- Free Tier
- No
- Entry Price
- —
- Enterprise Available
- No
- Transparency Score
- —
Beta — estimates may differ from actual pricing
1,000
1001K10K100K1M
Estimated Monthly Cost
$25
Estimated Annual Cost
$300
Estimates are approximate and may not reflect current pricing. Always check the official pricing page.
Project Health
F
Health Score
363 17
Bus Factor
8
Last Commit
2.4 years
Release Freq
12d
Open Issues
111
Issue Response
N/A
License
Apache-2.0
Last checked: 2026-04-21
Community Discussion
Comments powered by Giscus (GitHub Discussions). You need a GitHub account to comment.