Comparison
deeplake vs Daft
Verdict
Pick deeplake if deeplake is an AI Data Runtime for Agents designed with serverless Postgres and multimodal data lake support, targeting scalable retrieval and training capabilities; pick Daft if daft is a Rust-based high-performance data engine for AI and multimodal workloads that supports processing various types of structured and unstructured data at scale.
Markdown twin · deeplake alternatives · Daft alternatives
GraphCanon updated 2d
vs
Trust & integrity
| Signal | deeplake | Daft |
|---|---|---|
| Maintenance | Steady (87d since push) As of 1w · github_public_v1 | Very active (0d since push) As of 2d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1w · github_public_v1 | Not a fork · Organization account As of 2d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- deeplake
- AI Data Runtime for Agents with scalable retrieval and training features
- Daft
- High-performance data engine for AI and multimodal workloads in Rust.
Stars
- deeplake
- 9.2k
- Daft
- 5.7k
Forks
- deeplake
- 721
- Daft
- 544
Open issues
- deeplake
- 63
- Daft
- 371
Language
- deeplake
- C++
- Daft
- Rust
Adopt for
- deeplake
- Deeplake is an AI Data Runtime for Agents designed with serverless Postgres and multimodal data lake support, targeting scalable retrieval and training capabilities.
- Daft
- Daft is a Rust-based high-performance data engine for AI and multimodal workloads that supports processing various types of structured and unstructured data at scale.
Persona
- deeplake
- -
- Daft
- -
Runtime
- deeplake
- -
- Daft
- -
License
- deeplake
- Deeplake uses the Apache-2.0 license, allowing free use in both open source and commercial projects with attribution.
- Daft
- Apache-2.0
Last pushed
- deeplake
- May 21, 2026
- Daft
- Aug 21, 2026
Categories
- deeplake
- Data & Retrieval, Model Training, Vector Databases
- Daft
- Data & Retrieval, Model Training
Trust and health
Maintenance
- deeplake
- Steady (60%)
- Daft
- Very active (96%)
Days since push
- deeplake
- 87d
- Daft
- 0d
Open issues (now)
- deeplake
- 63
- Daft
- 371
Stars delta
- deeplake
- +16 (30d)
- Daft
- +76 (30d)
Open issues delta
- deeplake
- -6 (30d)
- Daft
- +29 (30d)
Full report
- deeplake
- Trust report
- Daft
- Trust report
Shared compatibility
- Python · deeplake: Python runtime · Daft: Python runtime
Choose deeplake if…
- deeplake is primarily C++; Daft is Rust.
- Pricing: Pricing details are not specified for Deeplake's public repository..
- Requirements: Deeplake can be installed using pip, making it accessible via the command `pip install deeplake`..
- Tags unique to deeplake: agent, agentic-rag, ai, computer-vision.
- Also covers Vector Databases.
- When you are developing applications that require seamless integration with AI agents, as Deeplake supports agent-centric design.
When NOT to use deeplake
- If your project does not benefit from an agent-centric architecture and you primarily require traditional database operations without multimodal features.
- When cost control is critical and serverless PostgreSQL might introduce variable costs compared to on-premises solutions for data retrieval and training.
Choose Daft if…
- Daft is primarily Rust; deeplake is C++.
- Tags unique to Daft: ai-engineering, ai-pipeline, arrow, artificial-intelligence.
- - When you require high performance and efficiency in a multilingual environment, particularly if projects are primarily developed in Rust
When NOT to use Daft
- - Avoid using Daft for projects where Python dominates the tech stack or development ecosystem
- - When performance requirements are lower and ease of use is prioritized over speed
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (activeloopai/deeplake) · observed Aug 17, 2026
- GitHub forks (activeloopai/deeplake) · observed Aug 17, 2026
- Last push (activeloopai/deeplake) · observed May 21, 2026
- License file (Apache-2.0) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 9, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (Eventual-Inc/Daft) · observed Aug 22, 2026
- GitHub forks (Eventual-Inc/Daft) · observed Aug 22, 2026
- Last push (Eventual-Inc/Daft) · observed Aug 21, 2026
- License file (Apache-2.0) · observed Aug 22, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: deeplake 9.2k · Daft 5.7k (synced Aug 17, 2026).
Common questions
- What is the difference between deeplake and Daft?
- deeplake: AI Data Runtime for Agents with scalable retrieval and training features. Daft: High-performance data engine for AI and multimodal workloads in Rust.. See the comparison table for live GitHub stats and shared categories.
- When should I choose deeplake over Daft?
- Choose deeplake over Daft when deeplake is primarily C++; Daft is Rust; Pricing: Pricing details are not specified for Deeplake's public repository.; Requirements: Deeplake can be installed using pip, making it accessible via the command
pip install deeplake.; Tags unique to deeplake: agent, agentic-rag, ai, computer-vision; Also covers Vector Databases; When you are developing applications that require seamless integration with AI agents, as Deeplake supports agent-centric design. - When should I choose Daft over deeplake?
- Choose Daft over deeplake when Daft is primarily Rust; deeplake is C++; Tags unique to Daft: ai-engineering, ai-pipeline, arrow, artificial-intelligence; - When you require high performance and efficiency in a multilingual environment, particularly if projects are primarily developed in Rust.
- When should I avoid deeplake?
- If your project does not benefit from an agent-centric architecture and you primarily require traditional database operations without multimodal features. When cost control is critical and serverless PostgreSQL might introduce variable costs compared to on-premises solutions for data retrieval and training.
- When should I avoid Daft?
- - Avoid using Daft for projects where Python dominates the tech stack or development ecosystem - When performance requirements are lower and ease of use is prioritized over speed
- Is deeplake or Daft more popular on GitHub?
- deeplake has more GitHub stars (9,224 vs 5,725). Stars measure visibility, not whether either tool fits your constraints.
- Are deeplake and Daft open source?
- Yes - both are open-source projects on GitHub (deeplake: Apache-2.0, Daft: Apache-2.0).
- Where can I find alternatives to deeplake or Daft?
- GraphCanon lists graph-backed alternatives at deeplake alternatives and Daft alternatives (deeplake markdown twin, Daft markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, deeplake or Daft?
- deeplake: Steady. Daft: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for deeplake and Daft?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: deeplake trust report; Daft trust report.