--- title: "paperless-ai vs DeepSpeed" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/clusterzx-paperless-ai-vs-deepspeedai-deepspeed" tools: ["clusterzx-paperless-ai", "deepspeedai-deepspeed"] --- # paperless-ai vs DeepSpeed *GraphCanon updated Jul 15, 2026* ## Verdict Pick paperless-ai when paperless-ai is primarily JavaScript; DeepSpeed is Python; pick DeepSpeed when deepSpeed is primarily Python; paperless-ai is JavaScript. [paperless-ai](https://clusterzx.github.io/paperless-ai/) reports 5.8k GitHub stars, 318 forks, and 56 open issues, last pushed Jul 5, 2026. [DeepSpeed](https://www.deepspeed.ai/) has 43k stars, 4.9k forks, and 1.3k open issues, last pushed Jul 13, 2026. Figures are from public GitHub metadata via [paperless-ai's repository](https://github.com/clusterzx/paperless-ai) and [DeepSpeed's repository](https://github.com/deepspeedai/DeepSpeed). | | [paperless-ai](/tools/clusterzx-paperless-ai.md) | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | | --- | --- | --- | | Tagline | An automated document analyzer for Paperless-ngx using OpenAI API, Ollama, Deepseek-r1, Azure and all OpenAI API compatible Services to automatically analyze and tag your documents. | Deep learning optimization library for efficient distributed training and inference | | Stars | 5,819 | 42,700 | | Forks | 318 | 4,881 | | Open issues | 56 | 1,299 | | Language | JavaScript | Python | | Adopt for | - | Decisions for DeepSpeed use are driven by its capacity to handle large models efficiently using techniques such as data parallelism, model parallelism, pipeline parallelism, and compression. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Inference & Serving | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [paperless-ai](/tools/clusterzx-paperless-ai.md) | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 10d | 0d | | Open issues (now) | 56 | 1.3k | | Owner type | User | Organization | | Full report | [trust report](/tools/clusterzx-paperless-ai/trust.md) | [trust report](/tools/deepspeedai-deepspeed/trust.md) | ## Decision facts: DeepSpeed - **Adopt for:** Decisions for DeepSpeed use are driven by its capacity to handle large models efficiently using techniques such as data parallelism, model parallelism, pipeline parallelism, and compression. ## Choose when ### Choose paperless-ai if… - paperless-ai is primarily JavaScript; DeepSpeed is Python. - License: paperless-ai is MIT, DeepSpeed is Apache-2.0. - Tags unique to paperless-ai: ai, automation, gemma, llama. ### Choose DeepSpeed if… - DeepSpeed is primarily Python; paperless-ai is JavaScript. - License: DeepSpeed is Apache-2.0, paperless-ai is MIT. - Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, deep-learning. - Also covers Model Training. - - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters) ## When NOT to use paperless-ai - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## When NOT to use DeepSpeed - - When you are working in an environment that only supports CPU-based training without access to CUDA or ROCm compatible GPUs - - If your project's PyTorch version is less than 2.0, DeepSpeed may not support all of its features and optimizations effectively ## Common questions ### What is the difference between paperless-ai and DeepSpeed? paperless-ai: An automated document analyzer for Paperless-ngx using OpenAI API, Ollama, Deepseek-r1, Azure and all OpenAI API compatible Services to automatically analyze and tag your documents.. DeepSpeed: Deep learning optimization library for efficient distributed training and inference. See the comparison table for live GitHub stats and shared categories. ### When should I choose paperless-ai over DeepSpeed? Choose paperless-ai over DeepSpeed when paperless-ai is primarily JavaScript; DeepSpeed is Python; License: paperless-ai is MIT, DeepSpeed is Apache-2.0; Tags unique to paperless-ai: ai, automation, gemma, llama. ### When should I choose DeepSpeed over paperless-ai? Choose DeepSpeed over paperless-ai when DeepSpeed is primarily Python; paperless-ai is JavaScript; License: DeepSpeed is Apache-2.0, paperless-ai is MIT; Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, deep-learning; Also covers Model Training; - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters). ### When should I avoid paperless-ai? Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### When should I avoid DeepSpeed? - When you are working in an environment that only supports CPU-based training without access to CUDA or ROCm compatible GPUs - If your project's PyTorch version is less than 2.0, DeepSpeed may not support all of its features and optimizations effectively ### Is paperless-ai or DeepSpeed more popular on GitHub? DeepSpeed has more GitHub stars (42,700 vs 5,819). Stars measure visibility, not whether either tool fits your constraints. ### Are paperless-ai and DeepSpeed open source? Yes - both are open-source projects on GitHub (paperless-ai: MIT, DeepSpeed: Apache-2.0). ### Where can I find alternatives to paperless-ai or DeepSpeed? GraphCanon lists graph-backed alternatives at [paperless-ai alternatives](/tools/clusterzx-paperless-ai/alternatives) and [DeepSpeed alternatives](/tools/deepspeedai-deepspeed/alternatives) ([paperless-ai markdown twin](/tools/clusterzx-paperless-ai/alternatives.md), [DeepSpeed markdown twin](/tools/deepspeedai-deepspeed/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/clusterzx-paperless-ai-vs-deepspeedai-deepspeed.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, paperless-ai or DeepSpeed? paperless-ai: Active. DeepSpeed: 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 paperless-ai and DeepSpeed? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [paperless-ai trust report](/tools/clusterzx-paperless-ai/trust); [DeepSpeed trust report](/tools/deepspeedai-deepspeed/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=clusterzx-paperless-ai`](/api/graphcanon/graph?tool=clusterzx-paperless-ai) - 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/_