--- title: "aquila vs typesense" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aquila-network-aquila-vs-typesense-typesense" tools: ["aquila-network-aquila", "typesense-typesense"] --- # aquila vs typesense *GraphCanon updated Aug 23, 2026* ## Verdict Pick aquila if aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches; pick typesense if typesense is an open-source and type-tolerant fuzzy search engine written in C++, primarily suitable for applications requiring speedy search responses with high tolerance to typos. [aquila](https://aquila.network) reports 379 GitHub stars, 26 forks, and 13 open issues, last pushed May 6, 2024. [typesense](https://typesense.org) has 26k stars, 963 forks, and 872 open issues, last pushed Aug 18, 2026. Figures are from public GitHub metadata via [aquila's repository](https://github.com/Aquila-Network/aquila) and [typesense's repository](https://github.com/typesense/typesense). | | [aquila](/tools/aquila-network-aquila.md) | [typesense](/tools/typesense-typesense.md) | | --- | --- | --- | | Tagline | Efficient Neural Search Engine | Fast, typo tolerant, in-memory fuzzy Search Engine | | Stars | 379 | 26,475 | | Forks | 26 | 963 | | Open issues | 13 | 872 | | Language | HTML | C++ | | Adopt for | Aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches. | Typesense is an open-source and type-tolerant fuzzy search engine written in C++, primarily suitable for applications requiring speedy search responses with high tolerance to typos. | | Persona | - | - | | Runtime | - | - | | License | - | GPL-3.0 License ensures typesense is free to use, modify and distribute as long as those changes are made available under the same licensing terms. | | Categories | Data & Retrieval, Vector Databases | Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [aquila](/tools/aquila-network-aquila.md) | [typesense](/tools/typesense-typesense.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 817d | 4d | | Open issues (now) | 13 | 872 | | Stars delta | Unknown | +128 (30d) | | Open issues delta | Unknown | +20 (30d) | | Full report | [trust report](/tools/aquila-network-aquila/trust.md) | [trust report](/tools/typesense-typesense/trust.md) | ## Decision facts: aquila - **Adopt for:** Aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches. ## Decision facts: typesense - **Hosting:** self hosted - Self-hosting on-premises or in-cloud environments, enabling full control over data and infrastructure. - **Adopt for:** Typesense is an open-source and type-tolerant fuzzy search engine written in C++, primarily suitable for applications requiring speedy search responses with high tolerance to typos. - **License detail:** GPL-3.0 License ensures typesense is free to use, modify and distribute as long as those changes are made available under the same licensing terms. ## Choose when ### Choose aquila if… - aquila is primarily HTML; typesense is C++. - Tags unique to aquila: approximate-nearest-neighbor-search, embedding, faiss, feature-vectors. - Also covers Vector Databases. - When deploying a solution that requires the processing of feature vectors in image or video search contexts, where efficiency in approximate nearest neighbor search is necessary ### Choose typesense if… - typesense is primarily C++; aquila is HTML. - Self-hosting on-premises or in-cloud environments, enabling full control over data and infrastructure. - Tags unique to typesense: algolia, datastore, elastic-search, faceting. - When seeking a drop-in replacement or alternative for Algolia, especially if considering an open-source solution. ## When NOT to use aquila - If the development team lacks experience with Docker, as Aquila's setup heavily relies on Docker images to run locally or in a big data configuration - In scenarios where strict control over metadata and vector indexing is required beyond what JSON and latent vectors can provide ## When NOT to use typesense - If the project is working with a smaller dataset where setting up an additional service could be overkill and simplicity outweighs high performance. - When the team prefers not to use GPL-3.0 licensed software, as this may pose limitations or requirements on how the code can be used or distributed. - In projects requiring complex vector search functionalities that might need more than what Typesense offers in its current feature set. ## Common questions ### What is the difference between aquila and typesense? aquila: Efficient Neural Search Engine. typesense: Fast, typo tolerant, in-memory fuzzy Search Engine. See the comparison table for live GitHub stats and shared categories. ### When should I choose aquila over typesense? Choose aquila over typesense when aquila is primarily HTML; typesense is C++; Tags unique to aquila: approximate-nearest-neighbor-search, embedding, faiss, feature-vectors; Also covers Vector Databases; When deploying a solution that requires the processing of feature vectors in image or video search contexts, where efficiency in approximate nearest neighbor search is necessary. ### When should I choose typesense over aquila? Choose typesense over aquila when typesense is primarily C++; aquila is HTML; Self-hosting on-premises or in-cloud environments, enabling full control over data and infrastructure; Tags unique to typesense: algolia, datastore, elastic-search, faceting; When seeking a drop-in replacement or alternative for Algolia, especially if considering an open-source solution. ### When should I avoid aquila? If the development team lacks experience with Docker, as Aquila's setup heavily relies on Docker images to run locally or in a big data configuration In scenarios where strict control over metadata and vector indexing is required beyond what JSON and latent vectors can provide ### When should I avoid typesense? If the project is working with a smaller dataset where setting up an additional service could be overkill and simplicity outweighs high performance. When the team prefers not to use GPL-3.0 licensed software, as this may pose limitations or requirements on how the code can be used or distributed. In projects requiring complex vector search functionalities that might need more than what Typesense offers in its current feature set. ### Is aquila or typesense more popular on GitHub? typesense has more GitHub stars (26,475 vs 379). Stars measure visibility, not whether either tool fits your constraints. ### Are aquila and typesense open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to aquila or typesense? GraphCanon lists graph-backed alternatives at [aquila alternatives](/tools/aquila-network-aquila/alternatives) and [typesense alternatives](/tools/typesense-typesense/alternatives) ([aquila markdown twin](/tools/aquila-network-aquila/alternatives.md), [typesense markdown twin](/tools/typesense-typesense/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/aquila-network-aquila-vs-typesense-typesense.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, aquila or typesense? aquila: Dormant. typesense: 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 aquila and typesense? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [aquila trust report](/tools/aquila-network-aquila/trust); [typesense trust report](/tools/typesense-typesense/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=aquila-network-aquila`](/api/graphcanon/graph?tool=aquila-network-aquila) - 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/_