Comparison
qdrant vs SeekStorm
Verdict
Pick qdrant if qdrant is a high-performance vector database and search engine built for AI applications, offering distributed deployment with sharding and replication; pick SeekStorm if rust-based library & server for real-time vector & lexical search.
Markdown twin · qdrant alternatives · SeekStorm alternatives
GraphCanon updated Sep 30, 2026
18views this month
Trust & integrity
| Signal | qdrant | SeekStorm |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 18, 2026 · github_public_v1 | Active (7d 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
- qdrant
- High-performance, massive-scale Vector Database and Vector Search Engine for AI
- SeekStorm
- Vector & Lexical Search Library and Multi-tenancy Server
Stars
- qdrant
- 35k
- SeekStorm
- 1.9k
Forks
- qdrant
- 2.7k
- SeekStorm
- 69
Open issues
- qdrant
- 733
- SeekStorm
- 15
Language
- qdrant
- Rust
- SeekStorm
- Rust
Adopt for
- qdrant
- Qdrant is a high-performance vector database and search engine built for AI applications, offering distributed deployment with sharding and replication.
- SeekStorm
- Rust-based library & server for real-time vector & lexical search
Persona
- qdrant
- -
- SeekStorm
- -
Runtime
- qdrant
- -
- SeekStorm
- -
License
- qdrant
- Qdrant is available under the Apache License, Version 2.0, which is a permissive free software license.
- SeekStorm
- Apache-2.0
Last pushed
- qdrant
- Sep 18, 2026
- SeekStorm
- Sep 12, 2026
Categories
- qdrant
- Vector Databases
- SeekStorm
- Data & Retrieval, Vector Databases
Trust and health
Maintenance
- qdrant
- Very active (96%)
- SeekStorm
- Active (82%)
Days since push
- qdrant
- 0d
- SeekStorm
- 7d
Open issues (now)
- qdrant
- 733
- SeekStorm
- 15
Stars delta
- qdrant
- +1.0k (30d)
- SeekStorm
- +12 (30d)
Open issues delta
- qdrant
- +81 (30d)
- SeekStorm
- -3 (30d)
Full report
- qdrant
- Trust report
- SeekStorm
- Trust report
Typed relationship
qdrant alternative SeekStormBoth Qdrant and SeekStorm provide vector search engines that can be used in machine learning contexts to handle large-scale, real-time queries.
Choose qdrant if…
- Pricing: Qdrant offers a free open-source version under the Apache License, with additional paid services available in the cloud..
- Requirements: Min 2 GB RAM; Qdrant supports distributed deployment, which may require additional resources depending on the scale of deployment..
- Both Qdrant and SeekStorm provide vector search engines that can be used in machine learning contexts to handle large-scale, real-time queries.
- Tags unique to qdrant: embeddings-similarity, hnsw, image-search, knn-algorithm.
- When you need a scalable solution for vector search that supports distributed deployment with sharding and replication.
When NOT to use qdrant
- If your project requires a database that is not built in Rust, and you have constraints against using Rust-based solutions.
- When you need a vector database that does not support distributed deployment, as Qdrant's strength lies in its scalability features.
Choose SeekStorm if…
- Both Qdrant and SeekStorm provide vector search engines that can be used in machine learning contexts to handle large-scale, real-time queries.
- Tags unique to SeekStorm: bm25, dense-retrieval, enterprise-search, faceting.
- Also covers Data & Retrieval.
- Need Rust integration for complex search tasks combining dense, sparse, and full-text capabilities
When NOT to use SeekStorm
- Prefer Python or JavaScript environments over Rust for development
- Seeking only simple keyword-based or narrow-vector searches without hybrid features
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (qdrant/qdrant) · observed Sep 20, 2026
- GitHub forks (qdrant/qdrant) · observed Sep 20, 2026
- Last push (qdrant/qdrant) · observed Sep 18, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (SeekStorm/SeekStorm) · observed Sep 20, 2026
- GitHub forks (SeekStorm/SeekStorm) · observed Sep 20, 2026
- Last push (SeekStorm/SeekStorm) · observed Sep 12, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: qdrant 35k · SeekStorm 1.9k (synced Sep 20, 2026).
Common questions
- What is the difference between qdrant and SeekStorm?
- qdrant: High-performance, massive-scale Vector Database and Vector Search Engine for AI. SeekStorm: Vector & Lexical Search Library and Multi-tenancy Server. See the comparison table for live GitHub stats and shared categories.
- When should I choose qdrant over SeekStorm?
- Choose qdrant over SeekStorm when Pricing: Qdrant offers a free open-source version under the Apache License, with additional paid services available in the cloud.; Requirements: Min 2 GB RAM; Qdrant supports distributed deployment, which may require additional resources depending on the scale of deployment.; Both Qdrant and SeekStorm provide vector search engines that can be used in machine learning contexts to handle large-scale, real-time queries; Tags unique to qdrant: embeddings-similarity, hnsw, image-search, knn-algorithm; When you need a scalable solution for vector search that supports distributed deployment with sharding and replication.
- When should I choose SeekStorm over qdrant?
- Choose SeekStorm over qdrant when Both Qdrant and SeekStorm provide vector search engines that can be used in machine learning contexts to handle large-scale, real-time queries; Tags unique to SeekStorm: bm25, dense-retrieval, enterprise-search, faceting; Also covers Data & Retrieval; Need Rust integration for complex search tasks combining dense, sparse, and full-text capabilities.
- When should I avoid qdrant?
- If your project requires a database that is not built in Rust, and you have constraints against using Rust-based solutions. When you need a vector database that does not support distributed deployment, as Qdrant's strength lies in its scalability features.
- When should I avoid SeekStorm?
- Prefer Python or JavaScript environments over Rust for development Seeking only simple keyword-based or narrow-vector searches without hybrid features
- Is qdrant or SeekStorm more popular on GitHub?
- qdrant has more GitHub stars (34,655 vs 1,913). Stars measure visibility, not whether either tool fits your constraints.
- Are qdrant and SeekStorm open source?
- Yes - both are open-source projects on GitHub (qdrant: Apache-2.0, SeekStorm: Apache-2.0).
- Where can I find alternatives to qdrant or SeekStorm?
- GraphCanon lists graph-backed alternatives at qdrant alternatives and SeekStorm alternatives (qdrant markdown twin, SeekStorm 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, qdrant or SeekStorm?
- qdrant: Very active. SeekStorm: 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 qdrant and SeekStorm?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: qdrant trust report; SeekStorm trust report.