--- title: "ColossalAI vs pipelines" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hpcaitech-colossalai-vs-kubeflow-pipelines" tools: ["hpcaitech-colossalai", "kubeflow-pipelines"] --- # ColossalAI vs pipelines *GraphCanon updated Jul 11, 2026* ## Verdict Pick ColossalAI when tags unique to ColossalAI: ai, big-model, data-parallelism, deep-learning; pick pipelines when tags unique to pipelines: data-science, kubeflow, kubeflow-pipelines, kubernetes. [ColossalAI](https://www.colossalai.org) reports 41k GitHub stars, 4.5k forks, and 501 open issues, last pushed May 25, 2026. [pipelines](https://www.kubeflow.org/docs/components/pipelines/) has 4.2k stars, 2.0k forks, and 419 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [ColossalAI's repository](https://github.com/hpcaitech/ColossalAI) and [pipelines's repository](https://github.com/kubeflow/pipelines). | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [pipelines](/tools/kubeflow-pipelines.md) | | --- | --- | --- | | Tagline | Making large AI models cheaper, faster and more accessible | Machine Learning Pipelines for Kubeflow | | Stars | 41,408 | 4,169 | | Forks | 4,504 | 2,030 | | Open issues | 501 | 419 | | Language | Python | Python | | Adopt for | ColossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models. | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Inference & Serving, Model Training | Data & Retrieval, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [pipelines](/tools/kubeflow-pipelines.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 46d | 0d | | Open issues (now) | 501 | 419 | | Security scan | No lockfile | 2 low (2 low) | | Full report | [trust report](/tools/hpcaitech-colossalai/trust.md) | [trust report](/tools/kubeflow-pipelines/trust.md) | ## Decision facts: ColossalAI - **Adopt for:** ColossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models. ## Choose when ### Choose ColossalAI if… - Tags unique to ColossalAI: ai, big-model, data-parallelism, deep-learning. - Also covers Model Training. - You require handling extremely large AI models with massive context windows, such as over 2M tokens. ### Choose pipelines if… - Tags unique to pipelines: data-science, kubeflow, kubeflow-pipelines, kubernetes. - Also covers Data & Retrieval. - More recently updated (last pushed Jul 11, 2026). ## When NOT to use ColossalAI - You are working in an environment that does not support Linux OS, as ColossalAI currently offers no support for other operating systems. - Your current CUDA version is less than 11.0 or your GPU compute capability is below 7.0 (pre-V100/RTX20 series). - You cannot satisfy the minimum hardware and software requirements specified, such as PyTorch >= 2.2 and Python >= 3.7. ## When NOT to use pipelines - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## Common questions ### What is the difference between ColossalAI and pipelines? ColossalAI: Making large AI models cheaper, faster and more accessible. pipelines: Machine Learning Pipelines for Kubeflow. See the comparison table for live GitHub stats and shared categories. ### When should I choose ColossalAI over pipelines? Choose ColossalAI over pipelines when Tags unique to ColossalAI: ai, big-model, data-parallelism, deep-learning; Also covers Model Training; You require handling extremely large AI models with massive context windows, such as over 2M tokens. ### When should I choose pipelines over ColossalAI? Choose pipelines over ColossalAI when Tags unique to pipelines: data-science, kubeflow, kubeflow-pipelines, kubernetes; Also covers Data & Retrieval; More recently updated (last pushed Jul 11, 2026). ### When should I avoid ColossalAI? You are working in an environment that does not support Linux OS, as ColossalAI currently offers no support for other operating systems. Your current CUDA version is less than 11.0 or your GPU compute capability is below 7.0 (pre-V100/RTX20 series). You cannot satisfy the minimum hardware and software requirements specified, such as PyTorch >= 2.2 and Python >= 3.7. ### When should I avoid pipelines? Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### Is ColossalAI or pipelines more popular on GitHub? ColossalAI has more GitHub stars (41,408 vs 4,169). Stars measure visibility, not whether either tool fits your constraints. ### Are ColossalAI and pipelines open source? Yes - both are open-source projects on GitHub (ColossalAI: Apache-2.0, pipelines: Apache-2.0). ### Where can I find alternatives to ColossalAI or pipelines? GraphCanon lists graph-backed alternatives at [ColossalAI alternatives](/tools/hpcaitech-colossalai/alternatives) and [pipelines alternatives](/tools/kubeflow-pipelines/alternatives) ([ColossalAI markdown twin](/tools/hpcaitech-colossalai/alternatives.md), [pipelines markdown twin](/tools/kubeflow-pipelines/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/hpcaitech-colossalai-vs-kubeflow-pipelines.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ColossalAI or pipelines? ColossalAI: Steady. pipelines: 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 ColossalAI and pipelines? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ColossalAI trust report](/tools/hpcaitech-colossalai/trust); [pipelines trust report](/tools/kubeflow-pipelines/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hpcaitech-colossalai`](/api/graphcanon/graph?tool=hpcaitech-colossalai) - 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/_