--- title: "search vs SPTAG" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kelindar-search-vs-microsoft-sptag" tools: ["kelindar-search", "microsoft-sptag"] --- # search vs SPTAG *GraphCanon updated Sep 20, 2026* ## Verdict Pick search if search is a Go library enabling embedded vector search and semantic embeddings via the llama.cpp framework; pick SPTAG if sPTAG is optimal for developers and enterprises handling large-scale vector searches who need high-quality indexing with efficient serving mechanisms. [search](https://github.com/kelindar/search) reports 559 GitHub stars, 24 forks, and 5 open issues, last pushed Sep 19, 2026. [SPTAG](https://github.com/microsoft/SPTAG) has 5.0k stars, 622 forks, and 143 open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [search's repository](https://github.com/kelindar/search) and [SPTAG's repository](https://github.com/microsoft/SPTAG). | | [search](/tools/kelindar-search.md) | [SPTAG](/tools/microsoft-sptag.md) | | --- | --- | --- | | Tagline | Go library for embedded vector search and semantic embeddings with llamacpp | Distributed ANN library for large-scale vector search | | Stars | 559 | 5,018 | | Forks | 24 | 622 | | Open issues | 5 | 143 | | Language | Go | C++ | | Adopt for | search is a Go library enabling embedded vector search and semantic embeddings via the llama.cpp framework. | SPTAG is optimal for developers and enterprises handling large-scale vector searches who need high-quality indexing with efficient serving mechanisms. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Data & Retrieval, Vector Databases | Inference & Serving, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [search](/tools/kelindar-search.md) | [SPTAG](/tools/microsoft-sptag.md) | | --- | --- | --- | | Days since push | 0d | 1d | | Open issues (now) | 5 | 143 | | Stars delta | +4 (30d) | +11 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/kelindar-search/trust.md) | [trust report](/tools/microsoft-sptag/trust.md) | ## Decision facts: search - **Adopt for:** search is a Go library enabling embedded vector search and semantic embeddings via the llama.cpp framework. ## Decision facts: SPTAG - **Pricing:** freemium - SPTAG is open source and free to use under the MIT license. Advanced support or integrations may bear extra costs depending on commercial context. - **Requirements:** Requires proficiency in C++ for optimal customization, though general usage can be managed with provided toolkits.; High infrastructure demands due to its distributed nature make efficient resource management a priority. - **Adopt for:** SPTAG is optimal for developers and enterprises handling large-scale vector searches who need high-quality indexing with efficient serving mechanisms. ## Choose when ### Choose search if… - search is primarily Go; SPTAG is C++. - Tags unique to search: ai, bert, embeddings, gguf. - Also covers Data & Retrieval. - Use for projects needing a lightweight, fast integration of semantic search capabilities within applications written in Go ### Choose SPTAG if… - SPTAG is primarily C++; search is Go. - Pricing: SPTAG is open source and free to use under the MIT license. Advanced support or integrations may bear extra costs depending on commercial context.. - Requirements: Requires proficiency in C++ for optimal customization, though general usage can be managed with provided toolkits.; High infrastructure demands due to its distributed nature make efficient resource management a priority.. - Tags unique to SPTAG: approximate-nearest-neighbor-search, distributed-serving, fresh-update, neighborhood-graph. - Also covers Inference & Serving. - SPTAG ships Docker support for self-hosted deployment. - If you are working on applications that require quick access to nearest neighborhood data in massive datasets, such as recommendation engines or image search systems. ## When NOT to use search - Avoid if relying on out-of-the-box support beyond Go or requiring heavy customization that is not supported directly by llama.cpp's capabilities - Not suitable when a more comprehensive database service with extensive querying and integration features is desired over an embedded solution ## When NOT to use SPTAG - Avoid using SPTAG if your application does not benefit from distributed infrastructure and prefers simpler, single-machine deployments with less operational complexity. - If the primary requirement of your project is a high level of exactness over speed in nearest neighbor detection, as SPTAG compromises on precision for faster query performance. ## Common questions ### What is the difference between search and SPTAG? search: Go library for embedded vector search and semantic embeddings with llamacpp. SPTAG: Distributed ANN library for large-scale vector search. See the comparison table for live GitHub stats and shared categories. ### When should I choose search over SPTAG? Choose search over SPTAG when search is primarily Go; SPTAG is C++; Tags unique to search: ai, bert, embeddings, gguf; Also covers Data & Retrieval; Use for projects needing a lightweight, fast integration of semantic search capabilities within applications written in Go. ### When should I choose SPTAG over search? Choose SPTAG over search when SPTAG is primarily C++; search is Go; Pricing: SPTAG is open source and free to use under the MIT license. Advanced support or integrations may bear extra costs depending on commercial context.; Requirements: Requires proficiency in C++ for optimal customization, though general usage can be managed with provided toolkits.; High infrastructure demands due to its distributed nature make efficient resource management a priority.; Tags unique to SPTAG: approximate-nearest-neighbor-search, distributed-serving, fresh-update, neighborhood-graph; Also covers Inference & Serving; SPTAG ships Docker support for self-hosted deployment; If you are working on applications that require quick access to nearest neighborhood data in massive datasets, such as recommendation engines or image search systems. ### When should I avoid search? Avoid if relying on out-of-the-box support beyond Go or requiring heavy customization that is not supported directly by llama.cpp's capabilities Not suitable when a more comprehensive database service with extensive querying and integration features is desired over an embedded solution ### When should I avoid SPTAG? Avoid using SPTAG if your application does not benefit from distributed infrastructure and prefers simpler, single-machine deployments with less operational complexity. If the primary requirement of your project is a high level of exactness over speed in nearest neighbor detection, as SPTAG compromises on precision for faster query performance. ### Is search or SPTAG more popular on GitHub? SPTAG has more GitHub stars (5,018 vs 559). Stars measure visibility, not whether either tool fits your constraints. ### Are search and SPTAG open source? Yes - both are open-source projects on GitHub (search: MIT, SPTAG: MIT). ### Where can I find alternatives to search or SPTAG? GraphCanon lists graph-backed alternatives at [search alternatives](/tools/kelindar-search/alternatives) and [SPTAG alternatives](/tools/microsoft-sptag/alternatives) ([search markdown twin](/tools/kelindar-search/alternatives.md), [SPTAG markdown twin](/tools/microsoft-sptag/alternatives.md)), 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](/compare/kelindar-search-vs-microsoft-sptag.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, search or SPTAG? search: Very active. SPTAG: 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 search and SPTAG? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [search trust report](/tools/kelindar-search/trust); [SPTAG trust report](/tools/microsoft-sptag/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kelindar-search`](/api/graphcanon/graph?tool=kelindar-search) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_