--- title: "aikit vs seldon-core" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kaito-project-aikit-vs-seldonio-seldon-core" tools: ["kaito-project-aikit", "seldonio-seldon-core"] --- # aikit vs seldon-core *GraphCanon updated Aug 24, 2026* ## Verdict Pick aikit if aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies; pick seldon-core if seldon-core is an MLOps framework designed for managing machine learning models in Kubernetes environments. [aikit](https://kaito-project.github.io/aikit/) reports 537 GitHub stars, 57 forks, and 40 open issues, last pushed Aug 24, 2026. [seldon-core](https://www.seldon.io/solutions/core/) has 4.8k stars, 867 forks, and 396 open issues, last pushed Mar 23, 2026. Figures are from public GitHub metadata via [aikit's repository](https://github.com/kaito-project/aikit) and [seldon-core's repository](https://github.com/SeldonIO/seldon-core). | | [aikit](/tools/kaito-project-aikit.md) | [seldon-core](/tools/seldonio-seldon-core.md) | | --- | --- | --- | | Tagline | Fine-tune, build, and deploy open-source LLMs easily! | An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models | | Stars | 537 | 4,765 | | Forks | 57 | 867 | | Open issues | 40 | 396 | | Language | Go | Go | | Adopt for | Aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies. | seldon-core is an MLOps framework designed for managing machine learning models in Kubernetes environments. | | Persona | - | - | | Runtime | - | - | | License | MIT | SeldonIO/seldon-core uses The Business Source License for distribution | | Categories | Inference & Serving, LLM Frameworks, Model Training | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [aikit](/tools/kaito-project-aikit.md) | [seldon-core](/tools/seldonio-seldon-core.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 133d | | Open issues (now) | 40 | 396 | | Stars delta | +3 (30d) | Unknown | | Open issues delta | -3 (30d) | Unknown | | Full report | [trust report](/tools/kaito-project-aikit/trust.md) | [trust report](/tools/seldonio-seldon-core/trust.md) | ## Decision facts: aikit - **Adopt for:** Aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies. ## Decision facts: seldon-core - **Requirements:** Requires Docker; Requires Docker for deployment environments - **Adopt for:** seldon-core is an MLOps framework designed for managing machine learning models in Kubernetes environments. - **License detail:** SeldonIO/seldon-core uses The Business Source License for distribution ## Choose when ### Choose aikit if… - License: aikit is MIT, seldon-core is Other. - Tags unique to aikit: ai, buildkit, chatgpt, docker. - Also covers LLM Frameworks, Model Training. - aikit ships Docker support for self-hosted deployment. - - You need a flexible solution specifically built using Go and prefer its concurrency model. ### Choose seldon-core if… - License: seldon-core is Other, aikit is MIT. - Requirements: Requires Docker; Requires Docker for deployment environments. - Tags unique to seldon-core: aiops, deployment, kubernetes, machine-learning-operations. - If you are deploying and serving ML models on Kubernetes clusters, seldon-core provides specialized capabilities within its MLOps framework to facilitate this. ## When NOT to use aikit - - You have a preference or requirement for Python-based tools due to the lack of native support in Aikit. - - If your deployment setup strictly uses cloud-specific platforms and you do not use Kubernetes or Docker, as Aikit heavily integrates with containerized environments like these. ## When NOT to use seldon-core - Consider alternatives if you are not running your infrastructure on Kubernetes, since seldon-core is optimized for Kubernetes environments. - If compatibility or licensing concerns arise due to the Business Source License under which Seldon is distributed, explore other frameworks with more permissive licenses. ## Common questions ### What is the difference between aikit and seldon-core? aikit: Fine-tune, build, and deploy open-source LLMs easily!. seldon-core: An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models. See the comparison table for live GitHub stats and shared categories. ### When should I choose aikit over seldon-core? Choose aikit over seldon-core when License: aikit is MIT, seldon-core is Other; Tags unique to aikit: ai, buildkit, chatgpt, docker; Also covers LLM Frameworks, Model Training; aikit ships Docker support for self-hosted deployment; - You need a flexible solution specifically built using Go and prefer its concurrency model. ### When should I choose seldon-core over aikit? Choose seldon-core over aikit when License: seldon-core is Other, aikit is MIT; Requirements: Requires Docker; Requires Docker for deployment environments; Tags unique to seldon-core: aiops, deployment, kubernetes, machine-learning-operations; If you are deploying and serving ML models on Kubernetes clusters, seldon-core provides specialized capabilities within its MLOps framework to facilitate this. ### When should I avoid aikit? - You have a preference or requirement for Python-based tools due to the lack of native support in Aikit. - If your deployment setup strictly uses cloud-specific platforms and you do not use Kubernetes or Docker, as Aikit heavily integrates with containerized environments like these. ### When should I avoid seldon-core? Consider alternatives if you are not running your infrastructure on Kubernetes, since seldon-core is optimized for Kubernetes environments. If compatibility or licensing concerns arise due to the Business Source License under which Seldon is distributed, explore other frameworks with more permissive licenses. ### Is aikit or seldon-core more popular on GitHub? seldon-core has more GitHub stars (4,765 vs 537). Stars measure visibility, not whether either tool fits your constraints. ### Are aikit and seldon-core open source? Yes - both are open-source projects on GitHub (aikit: MIT, seldon-core: Other). ### Where can I find alternatives to aikit or seldon-core? GraphCanon lists graph-backed alternatives at [aikit alternatives](/tools/kaito-project-aikit/alternatives) and [seldon-core alternatives](/tools/seldonio-seldon-core/alternatives) ([aikit markdown twin](/tools/kaito-project-aikit/alternatives.md), [seldon-core markdown twin](/tools/seldonio-seldon-core/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/kaito-project-aikit-vs-seldonio-seldon-core.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, aikit or seldon-core? aikit: Very active. seldon-core: 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 aikit and seldon-core? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [aikit trust report](/tools/kaito-project-aikit/trust); [seldon-core trust report](/tools/seldonio-seldon-core/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kaito-project-aikit`](/api/graphcanon/graph?tool=kaito-project-aikit) - 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/_