--- title: "vespa vs deep-searcher" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/vespa-engine-vespa-vs-zilliztech-deep-searcher" tools: ["vespa-engine-vespa", "zilliztech-deep-searcher"] --- # vespa vs deep-searcher Neutral, constraint-first comparison with live GitHub stats. | | [vespa](/tools/vespa-engine-vespa.md) | [deep-searcher](/tools/zilliztech-deep-searcher.md) | | --- | --- | --- | | Tagline | The AI search platform | Open Source Deep Research Alternative to Reason and Search on Private Data | | Stars | 6,997 | 7,934 | | Forks | 723 | 767 | | Open issues | 244 | 53 | | Language | Java | Python | | Adopt for | Vespa is an AI search platform that performs high-availability and high-performance operations such as search, recommendation, and personalization. | DeepSearcher is an open-source tool that combines advanced large language models (LLMs) and vector databases to perform search, evaluation, and reasoning based on private data. It provides enterprise knowledge management | | Persona | - | - | | Runtime | - | - | | License | The Apache-2.0 license under which Vespa is distributed allows for free use in open source as well as commercial applications, with the condition that changes to the code be made available under the许可 | Apache-2.0 | | Categories | Data & Retrieval, Vector Databases | Data & Retrieval, Vector Databases, AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [vespa](/tools/vespa-engine-vespa.md) | [deep-searcher](/tools/zilliztech-deep-searcher.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 231d | | Open issues (now) | 244 | 53 | | Full report | [trust report](/tools/vespa-engine-vespa/trust.md) | [trust report](/tools/zilliztech-deep-searcher/trust.md) | **Typed relationship:** vespa _(alternative)_ deep-searcher Both Vespa and deep-searcher are used for performing advanced search operations on large data sets, often including private data, making them direct alternatives. ## Decision facts: vespa - **Hosting:** self hosted - Vespa can both run on-premises allowing full control over infrastructure, and is also available as a cloud service which handles deployment and scaling. - **Adopt for:** Vespa is an AI search platform that performs high-availability and high-performance operations such as search, recommendation, and personalization. - **License detail:** The Apache-2.0 license under which Vespa is distributed allows for free use in open source as well as commercial applications, with the condition that changes to the code be made available under the许可 ## Decision facts: deep-searcher - **Adopt for:** DeepSearcher is an open-source tool that combines advanced large language models (LLMs) and vector databases to perform search, evaluation, and reasoning based on private data. It provides enterprise knowledge management ## Choose when ### Choose vespa if… - vespa is primarily Java; deep-searcher is Python. - Vespa can both run on-premises allowing full control over infrastructure, and is also available as a cloud service which handles deployment and scaling. - Both Vespa and deep-searcher are used for performing advanced search operations on large data sets, often including private data, making them direct alternatives. - Tags unique to vespa: big-data, vector-database, ai, machine-learning. - - When your application requires a highly scalable solution for handling large datasets with millisecond response times. ### Choose deep-searcher if… - deep-searcher is primarily Python; vespa is Java. - Both Vespa and deep-searcher are used for performing advanced search operations on large data sets, often including private data, making them direct alternatives. - Tags unique to deep-searcher: llm, openai, claude, agentic-rag. - Also covers AI Agents. - deep-searcher ships Docker support for self-hosted deployment. - - **When you need a flexible embedding option**: DeepSearcher supports multiple embedding models like Milvus for optimal selection. ## When NOT to use vespa - - In environments where the primary requirement is a lightweight solution with minimal overhead; Vespa includes extensive feature sets which may not be necessary for smaller or less complex use cases. - - If you are looking for a tool primarily focused on generic database operations rather than specialized search and recommendation scenarios. ## When NOT to use deep-searcher - - **If you require real-time web content integration only**: DeepSearcher primarily focuses on local/private data. Online content integration is possible but not its core functionality. - - **When strict API dependency avoidance is needed**: DeepSearcher often relies on specific APIs (e.g., OpenAI) for LLM services, which might be a constraint in environments strictly avoiding third-党 ## Common questions ### What is the difference between vespa and deep-searcher? vespa: The AI search platform. deep-searcher: Open Source Deep Research Alternative to Reason and Search on Private Data. See the comparison table for live GitHub stats and shared categories. ### When should I choose vespa over deep-searcher? Choose vespa over deep-searcher when vespa is primarily Java; deep-searcher is Python; Vespa can both run on-premises allowing full control over infrastructure, and is also available as a cloud service which handles deployment and scaling; Both Vespa and deep-searcher are used for performing advanced search operations on large data sets, often including private data, making them direct alternatives; Tags unique to vespa: big-data, vector-database, ai, machine-learning; - When your application requires a highly scalable solution for handling large datasets with millisecond response times. ### When should I choose deep-searcher over vespa? Choose deep-searcher over vespa when deep-searcher is primarily Python; vespa is Java; Both Vespa and deep-searcher are used for performing advanced search operations on large data sets, often including private data, making them direct alternatives; Tags unique to deep-searcher: llm, openai, claude, agentic-rag; Also covers AI Agents; deep-searcher ships Docker support for self-hosted deployment; - **When you need a flexible embedding option**: DeepSearcher supports multiple embedding models like Milvus for optimal selection. ### When should I avoid vespa? - In environments where the primary requirement is a lightweight solution with minimal overhead; Vespa includes extensive feature sets which may not be necessary for smaller or less complex use cases. - If you are looking for a tool primarily focused on generic database operations rather than specialized search and recommendation scenarios. ### When should I avoid deep-searcher? - **If you require real-time web content integration only**: DeepSearcher primarily focuses on local/private data. Online content integration is possible but not its core functionality. - **When strict API dependency avoidance is needed**: DeepSearcher often relies on specific APIs (e.g., OpenAI) for LLM services, which might be a constraint in environments strictly avoiding third-党 ### Is vespa or deep-searcher more popular on GitHub? deep-searcher has more GitHub stars (7,934 vs 6,997). Stars measure visibility, not whether either tool fits your constraints. ### Are vespa and deep-searcher open source? Yes - both are open-source projects on GitHub (vespa: Apache-2.0, deep-searcher: Apache-2.0). ### Where can I find alternatives to vespa or deep-searcher? GraphCanon lists graph-backed alternatives at /tools/vespa-engine-vespa/alternatives and /tools/zilliztech-deep-searcher/alternatives (/tools/vespa-engine-vespa/alternatives.md, /tools/zilliztech-deep-searcher/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 /compare/vespa-engine-vespa-vs-zilliztech-deep-searcher.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, vespa or deep-searcher? vespa: Very active. deep-searcher: Slowing. 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 vespa and deep-searcher? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: vespa: /tools/vespa-engine-vespa/trust; deep-searcher: /tools/zilliztech-deep-searcher/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=vespa-engine-vespa`](/api/graphcanon/graph?tool=vespa-engine-vespa) - 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/_