REPOGEO REPORT · LITE

dstackai/dstack

Default branch master · commit 67d49cbd · scanned 6/22/2026, 11:42:11 AM

GitHub: 2,164 stars · 234 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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

Refine README opening to clarify unique positioning

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 unified control plane for GPU provisioning and orchestration, abstracting away the complexities of diverse hardware (NVIDIA, AMD, Google TPU, Tenstorrent) and environments (cloud, Kubernetes, on-prem clusters) to streamline ML training, inference, and agentic workloads.

mediumreadme#2

Add a 'Why dstack?' or 'Comparison' section to README

Why:

COPY-PASTE FIX

## Why dstack?

dstack provides a unified abstraction layer for GPU orchestration that complements existing tools like Kubernetes, MLflow, and Slurm. Unlike generic container orchestrators, dstack focuses specifically on the lifecycle of ML workloads across heterogeneous GPU hardware and cloud/on-prem environments. It's not a replacement for Kubernetes, but rather a specialized control plane that works on top of it (or other infrastructure) to simplify GPU provisioning and workload management for AI/ML.

lowhomepage#3
Update homepage URL to main product site
Why:
CURRENT
```
https://dstack.ai/docs
```
COPY-PASTE FIX
```
https://dstack.ai
```

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

Of all named tools, what % are you?

Top rival

Kubernetes

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Kubernetes · recommended 2×
NVIDIA GPU Operator · recommended 1×
MLflow · recommended 1×
Slurm · recommended 1×
Ray · recommended 1×

CATEGORY QUERY
How to orchestrate machine learning training and inference across diverse GPU hardware?
you: not recommended
AI recommended (in order):
1. Kubernetes
2. NVIDIA GPU Operator
3. MLflow
4. Slurm
5. Ray
6. PyTorch
7. TensorFlow
8. Ray Tune
9. Ray Serve
10. OpenShift
11. Open Data Hub
12. JupyterHub
13. Seldon Core
14. Kubeflow
15. Katib
16. KFServing/KServe
AI recommended 16 alternatives but never named dstackai/dstack. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Need a unified control plane for LLM agentic workloads on cloud and on-prem clusters.
you: not recommended
AI recommended (in order):
1. Kubernetes
2. KubeFlow
3. OpenShift AI
4. MLRun
5. Azure Machine Learning
6. Azure Arc
7. Google Cloud Vertex AI
8. Anthos
9. AWS SageMaker
10. Amazon EKS Anywhere
11. AWS Outposts
12. Domino Data Lab
AI recommended 12 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?

Embed your GEO score

Drop this badge into the README of dstackai/dstack. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/dstackai/dstack.svg)](https://repogeo.com/en/r/dstackai/dstack)

HTML

<a href="https://repogeo.com/en/r/dstackai/dstack"><img src="https://repogeo.com/badge/dstackai/dstack.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

dstackai/dstack — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite