Comparison
deeplake vs Daft
Verdict
Pick deeplake if deeplake is an AI Data Runtime for Agents that integrates a serverless PostgreSQL with a multimodal data lake, designed for scalable retrieval and training; 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 Sep 30, 2026
9views this month
vs
Trust & integrity
| Signal | deeplake | Daft |
|---|---|---|
| Maintenance | Slowing (119d since push) As of Sep 18, 2026 · github_public_v1 | Very active (2d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- Daft
- High-performance data engine for AI and multimodal workloads in Rust.
Stars
- deeplake
- 9.2k
- Daft
- 5.8k
Forks
- deeplake
- 722
- Daft
- 561
Open issues
- deeplake
- 67
- Daft
- 389
Language
- deeplake
- C++
- Daft
- Rust
Adopt for
- deeplake
- Deeplake is an AI Data Runtime for Agents that integrates a serverless PostgreSQL with a multimodal data lake, designed for scalable retrieval and training.
- 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 is available under the Apache-2.0 license, allowing for free use, modification, and distribution.
- Daft
- Apache-2.0
Last pushed
- deeplake
- May 21, 2026
- Daft
- Sep 17, 2026
Categories
- deeplake
- Data & Retrieval, Model Training, Vector Databases
- Daft
- Data & Retrieval, Model Training
Trust and health
Maintenance
- deeplake
- Slowing (36%)
- Daft
- Very active (96%)
Days since push
- deeplake
- 119d
- Daft
- 2d
Open issues (now)
- deeplake
- 67
- Daft
- 389
Stars delta
- deeplake
- +15 (30d)
- Daft
- +130 (30d)
Open issues delta
- deeplake
- +4 (30d)
- Daft
- +47 (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: Deeplake offers a free tier with options for premium services, though specific pricing details are not provided in the repository..
- Requirements: Min 4 GB RAM; Installation can be done via pip, making it accessible for Python environments..
- Tags unique to deeplake: agent, agentic-rag, ai, clawbot.
- Also covers Vector Databases.
- When you need a serverless PostgreSQL setup integrated with a multimodal data lake for efficient data retrieval and training.
When NOT to use deeplake
- If your project does not require serverless PostgreSQL or multimodal data handling capabilities.
- When you are looking for a solution that does not support C++ as the primary language, as Deeplake is built with C++.
- If your use case does not benefit from the specific integration of PostgreSQL with a data lake, and you prefer a more traditional database setup.
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 Sep 20, 2026
- GitHub forks (activeloopai/deeplake) · observed Sep 20, 2026
- Last push (activeloopai/deeplake) · observed May 21, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 30, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (Eventual-Inc/Daft) · observed Sep 20, 2026
- GitHub forks (Eventual-Inc/Daft) · observed Sep 20, 2026
- Last push (Eventual-Inc/Daft) · observed Sep 17, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: deeplake 9.2k · Daft 5.8k (synced Sep 20, 2026).
Common questions
- What is the difference between deeplake and Daft?
- deeplake: AI Data Runtime for Agents. 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: Deeplake offers a free tier with options for premium services, though specific pricing details are not provided in the repository.; Requirements: Min 4 GB RAM; Installation can be done via pip, making it accessible for Python environments.; Tags unique to deeplake: agent, agentic-rag, ai, clawbot; Also covers Vector Databases; When you need a serverless PostgreSQL setup integrated with a multimodal data lake for efficient data retrieval and training.
- 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 require serverless PostgreSQL or multimodal data handling capabilities. When you are looking for a solution that does not support C++ as the primary language, as Deeplake is built with C++. If your use case does not benefit from the specific integration of PostgreSQL with a data lake, and you prefer a more traditional database setup.
- 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 (9,239 vs 5,779). 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: Slowing. 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.