--- title: "ai-getting-started vs fiftyone" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/a16z-infra-ai-getting-started-vs-voxel51-fiftyone" tools: ["a16z-infra-ai-getting-started", "voxel51-fiftyone"] --- # ai-getting-started vs fiftyone *GraphCanon updated Jul 12, 2026* ## Verdict Pick ai-getting-started when license: ai-getting-started is MIT, fiftyone is Apache-2.0; pick fiftyone when license: fiftyone is Apache-2.0, ai-getting-started is MIT. [ai-getting-started](https://ai-getting-started.com/) reports 4.1k GitHub stars, 663 forks, and 16 open issues, last pushed Aug 21, 2024. [fiftyone](https://fiftyone.ai) has 11k stars, 793 forks, and 672 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [ai-getting-started's repository](https://github.com/a16z-infra/ai-getting-started) and [fiftyone's repository](https://github.com/voxel51/fiftyone). | | [ai-getting-started](/tools/a16z-infra-ai-getting-started.md) | [fiftyone](/tools/voxel51-fiftyone.md) | | --- | --- | --- | | Tagline | A Javascript AI getting started stack for weekend projects, including image/text models, vector stores, auth, and deployment configs | Refine high-quality datasets and visual AI models | | Stars | 4,141 | 10,891 | | Forks | 663 | 793 | | Open issues | 16 | 672 | | Language | TypeScript | TypeScript | | Adopt for | - | Fiftyone is a specialized tool that leverages TypeScript and is licensed under Apache-2.0 for refining high-quality datasets and visual AI models in the context of computer vision tasks. It covers areas such as data curo | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Computer Vision, Inference & Serving, Vector Databases | Computer Vision, Data & Retrieval, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ai-getting-started](/tools/a16z-infra-ai-getting-started.md) | [fiftyone](/tools/voxel51-fiftyone.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 688d | 0d | | Open issues (now) | 16 | 672 | | Security scan | 31 low (31 low) | No lockfile | | Full report | [trust report](/tools/a16z-infra-ai-getting-started/trust.md) | [trust report](/tools/voxel51-fiftyone/trust.md) | ## Decision facts: fiftyone - **Adopt for:** Fiftyone is a specialized tool that leverages TypeScript and is licensed under Apache-2.0 for refining high-quality datasets and visual AI models in the context of computer vision tasks. It covers areas such as data curo - **License detail:** Apache-2.0 ## Choose when ### Choose ai-getting-started if… - License: ai-getting-started is MIT, fiftyone is Apache-2.0. - Tags unique to ai-getting-started: typescript. - Also covers Inference & Serving, Vector Databases. ### Choose fiftyone if… - License: fiftyone is Apache-2.0, ai-getting-started is MIT. - Tags unique to fiftyone: active-learning, artificial-intelligence, computer-vision, data-centric-ai. - Also covers Data & Retrieval, Developer Tools. - When you need a comprehensive solution for both dataset refinement and visualization tailored for computer vision projects, Fiftyone stands out. ## When NOT to use ai-getting-started - Last GitHub push was 689 days ago (dormant maintenance, Aug 21, 2024). Validate activity before betting a new project on ai-getting-started. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## When NOT to use fiftyone - If your primary focus is not within the realm of computer vision or unstructured data handling, Fiftyone may not align with your needs. - Consider alternatives if your project does not require TypeScript; Fiftyone’s choice of language might create a compatibility barrier for projects preferring other languages. ## Common questions ### What is the difference between ai-getting-started and fiftyone? ai-getting-started: A Javascript AI getting started stack for weekend projects, including image/text models, vector stores, auth, and deployment configs. fiftyone: Refine high-quality datasets and visual AI models. See the comparison table for live GitHub stats and shared categories. ### When should I choose ai-getting-started over fiftyone? Choose ai-getting-started over fiftyone when License: ai-getting-started is MIT, fiftyone is Apache-2.0; Tags unique to ai-getting-started: typescript; Also covers Inference & Serving, Vector Databases. ### When should I choose fiftyone over ai-getting-started? Choose fiftyone over ai-getting-started when License: fiftyone is Apache-2.0, ai-getting-started is MIT; Tags unique to fiftyone: active-learning, artificial-intelligence, computer-vision, data-centric-ai; Also covers Data & Retrieval, Developer Tools; When you need a comprehensive solution for both dataset refinement and visualization tailored for computer vision projects, Fiftyone stands out. ### When should I avoid ai-getting-started? Last GitHub push was 689 days ago (dormant maintenance, Aug 21, 2024). Validate activity before betting a new project on ai-getting-started. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### When should I avoid fiftyone? If your primary focus is not within the realm of computer vision or unstructured data handling, Fiftyone may not align with your needs. Consider alternatives if your project does not require TypeScript; Fiftyone’s choice of language might create a compatibility barrier for projects preferring other languages. ### Is ai-getting-started or fiftyone more popular on GitHub? fiftyone has more GitHub stars (10,891 vs 4,141). Stars measure visibility, not whether either tool fits your constraints. ### Are ai-getting-started and fiftyone open source? Yes - both are open-source projects on GitHub (ai-getting-started: MIT, fiftyone: Apache-2.0). ### Where can I find alternatives to ai-getting-started or fiftyone? GraphCanon lists graph-backed alternatives at [ai-getting-started alternatives](/tools/a16z-infra-ai-getting-started/alternatives) and [fiftyone alternatives](/tools/voxel51-fiftyone/alternatives) ([ai-getting-started markdown twin](/tools/a16z-infra-ai-getting-started/alternatives.md), [fiftyone markdown twin](/tools/voxel51-fiftyone/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/a16z-infra-ai-getting-started-vs-voxel51-fiftyone.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ai-getting-started or fiftyone? ai-getting-started: Dormant. fiftyone: 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 ai-getting-started and fiftyone? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ai-getting-started trust report](/tools/a16z-infra-ai-getting-started/trust); [fiftyone trust report](/tools/voxel51-fiftyone/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=a16z-infra-ai-getting-started`](/api/graphcanon/graph?tool=a16z-infra-ai-getting-started) - 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/_