RRepoGEO

REPOGEO REPORT · LITE

dstackai/dstack

Default branch master · commit 565b9a62 · scanned 5/12/2026, 5:32:11 AM

GitHub: 2,133 stars · 226 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface dstackai/dstack, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening statement

    Why:

    CURRENT
    `dstack` is a unified control plane for GPU provisioning and orchestration that works with any GPU cloud, Kubernetes, or on-prem clusters.
    COPY-PASTE FIX
    `dstack` is a vendor-agnostic control plane for GPU provisioning and orchestration, purpose-built for AI workloads like training, inference, and agentic tasks. It unifies diverse hardware (NVIDIA, AMD, TPU, Tenstorrent) and infrastructure (any cloud, Kubernetes, bare metal) under a single API, abstracting away underlying complexity for ML engineers.
  • mediumabout#2
    Refine the repository's 'About' description

    Why:

    CURRENT
    Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AMD, TPU, and Tenstorrent on clouds, Kubernetes, and bare metal.
    COPY-PASTE FIX
    Vendor-agnostic control plane for AI workloads, providing serverless-like GPU orchestration across NVIDIA, AMD, TPU, and Tenstorrent on any cloud, Kubernetes, or bare metal, abstracting infrastructure complexity for ML engineers.
  • lowreadme#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, for example, `## Comparison with Alternatives`, with content similar to: 'While tools like Kubernetes provide general container orchestration, `dstack` offers a specialized control plane for GPU provisioning and AI workload orchestration. Unlike broader MLOps platforms such as Kubeflow or MLflow, `dstack` focuses specifically on abstracting GPU infrastructure. Compared to distributed computing frameworks like Ray, `dstack` provides a higher-level, vendor-agnostic API for managing diverse accelerators and cloud resources.'

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface dstackai/dstack
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Kubernetes
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Kubernetes · recommended 2×
  2. Ray · recommended 2×
  3. Kubeflow · recommended 1×
  4. MLflow · recommended 1×
  5. AWS SageMaker · recommended 1×
  • CATEGORY QUERY
    How to orchestrate machine learning training and inference across various GPU types and clouds?
    you: not recommended
    AI recommended (in order):
    1. Kubernetes
    2. Kubeflow
    3. MLflow
    4. AWS SageMaker
    5. Google Cloud Vertex AI
    6. Azure Machine Learning
    7. Ray
    8. Valohai

    AI recommended 8 alternatives but never named dstackai/dstack. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a vendor-agnostic platform to manage GPU resources for LLM inference and agentic workloads.
    you: not recommended
    AI recommended (in order):
    1. Kubernetes
    2. KubeFlow
    3. Slurm Workload Manager
    4. Run:AI
    5. OpenShift
    6. Ray
    7. Anyscale Platform

    AI recommended 7 alternatives but never named dstackai/dstack. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of dstackai/dstack?
    pass
    AI named dstackai/dstack explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts dstackai/dstack in production, what risks or prerequisites should they evaluate first?
    pass
    AI named dstackai/dstack explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo dstackai/dstack solve, and who is the primary audience?
    pass
    AI named dstackai/dstack explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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