--- title: "mlflow vs aqueduct" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/mlflow-mlflow-vs-runllm-aqueduct" tools: ["mlflow-mlflow", "runllm-aqueduct"] --- # mlflow vs aqueduct *GraphCanon updated Aug 20, 2026* ## Verdict Pick mlflow if mLflow is an open-source platform that offers comprehensive capabilities for managing, deploying, and monitoring machine learning models as well as large language models (LLMs) and AI agents. MLflow supports various use,; pick aqueduct if aqueduct is a deprecated Go-based tool for orchestrating LLM and ML workloads across various cloud infrastructures with Kubernetes support. [mlflow](https://mlflow.org) reports 28k GitHub stars, 6.2k forks, and 2.1k open issues, last pushed Aug 20, 2026. [aqueduct](https://aqueducthq.com) has 517 stars, 20 forks, and 11 open issues, last pushed Jun 7, 2023. Figures are from public GitHub metadata via [mlflow's repository](https://github.com/mlflow/mlflow) and [aqueduct's repository](https://github.com/RunLLM/aqueduct). | | [mlflow](/tools/mlflow-mlflow.md) | [aqueduct](/tools/runllm-aqueduct.md) | | --- | --- | --- | | Tagline | AI engineering platform for debugging, evaluating, monitoring, and optimizing AI applications | Orchestrate LLM and ML workloads on any cloud infrastructure using Go. | | Stars | 27,591 | 517 | | Forks | 6,189 | 20 | | Open issues | 2,054 | 11 | | Language | Python | Go | | Adopt for | MLflow is an open-source platform that offers comprehensive capabilities for managing, deploying, and monitoring machine learning models as well as large language models (LLMs) and AI agents. MLflow supports various use, | Aqueduct is a deprecated Go-based tool for orchestrating LLM and ML workloads across various cloud infrastructures with Kubernetes support. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Evaluation & Observability, Inference & Serving, Model Training | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [mlflow](/tools/mlflow-mlflow.md) | [aqueduct](/tools/runllm-aqueduct.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 1152d | | Open issues (now) | 2.1k | 11 | | Stars delta | +476 (30d) | Unknown | | Open issues delta | -22 (30d) | Unknown | | Full report | [trust report](/tools/mlflow-mlflow/trust.md) | [trust report](/tools/runllm-aqueduct/trust.md) | ## Decision facts: mlflow - **Adopt for:** MLflow is an open-source platform that offers comprehensive capabilities for managing, deploying, and monitoring machine learning models as well as large language models (LLMs) and AI agents. MLflow supports various use, ## Decision facts: aqueduct - **Adopt for:** Aqueduct is a deprecated Go-based tool for orchestrating LLM and ML workloads across various cloud infrastructures with Kubernetes support. ## Choose when ### Choose mlflow if… - mlflow is primarily Python; aqueduct is Go. - Tags unique to mlflow: agentops, agents, ai-governance, evaluation. - Also covers Evaluation & Observability. - - Use when you're working with a diverse range of environments like local or cloud platforms because MLflow is **vendor-neutral**. ### Choose aqueduct if… - aqueduct is primarily Go; mlflow is Python. - Tags unique to aqueduct: ai, data, data-science, kubernetes. - Also covers LLM Frameworks. - When you need to deploy legacy workflows that depend on Aqueduct's specific implementation of custom ops for resource allocation and training. ## When NOT to use mlflow - - Avoid if your organization has strong preferences for proprietary solutions with advanced features not available in the open-source domain. - - Not recommended for users who prefer a fully managed service without self-hosting options, as competitors like Databricks or Azure ML offer integrated services tailored for their cloud environments. ## When NOT to use aqueduct - Avoid if active project maintenance or community support is required as Aqueduct is no longer maintained. - Skip this tool for new projects focusing on state-of-the-art ML orchestration, opting instead for actively supported alternatives. ## Common questions ### What is the difference between mlflow and aqueduct? mlflow: AI engineering platform for debugging, evaluating, monitoring, and optimizing AI applications. aqueduct: Orchestrate LLM and ML workloads on any cloud infrastructure using Go.. See the comparison table for live GitHub stats and shared categories. ### When should I choose mlflow over aqueduct? Choose mlflow over aqueduct when mlflow is primarily Python; aqueduct is Go; Tags unique to mlflow: agentops, agents, ai-governance, evaluation; Also covers Evaluation & Observability; - Use when you're working with a diverse range of environments like local or cloud platforms because MLflow is **vendor-neutral**. ### When should I choose aqueduct over mlflow? Choose aqueduct over mlflow when aqueduct is primarily Go; mlflow is Python; Tags unique to aqueduct: ai, data, data-science, kubernetes; Also covers LLM Frameworks; When you need to deploy legacy workflows that depend on Aqueduct's specific implementation of custom ops for resource allocation and training. ### When should I avoid mlflow? - Avoid if your organization has strong preferences for proprietary solutions with advanced features not available in the open-source domain. - Not recommended for users who prefer a fully managed service without self-hosting options, as competitors like Databricks or Azure ML offer integrated services tailored for their cloud environments. ### When should I avoid aqueduct? Avoid if active project maintenance or community support is required as Aqueduct is no longer maintained. Skip this tool for new projects focusing on state-of-the-art ML orchestration, opting instead for actively supported alternatives. ### Is mlflow or aqueduct more popular on GitHub? mlflow has more GitHub stars (27,591 vs 517). Stars measure visibility, not whether either tool fits your constraints. ### Are mlflow and aqueduct open source? Yes - both are open-source projects on GitHub (mlflow: Apache-2.0, aqueduct: Apache-2.0). ### Where can I find alternatives to mlflow or aqueduct? GraphCanon lists graph-backed alternatives at [mlflow alternatives](/tools/mlflow-mlflow/alternatives) and [aqueduct alternatives](/tools/runllm-aqueduct/alternatives) ([mlflow markdown twin](/tools/mlflow-mlflow/alternatives.md), [aqueduct markdown twin](/tools/runllm-aqueduct/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/mlflow-mlflow-vs-runllm-aqueduct.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, mlflow or aqueduct? mlflow: Very active. aqueduct: Dormant. 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 mlflow and aqueduct? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [mlflow trust report](/tools/mlflow-mlflow/trust); [aqueduct trust report](/tools/runllm-aqueduct/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=mlflow-mlflow`](/api/graphcanon/graph?tool=mlflow-mlflow) - 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/_