RRepoGEO

REPOGEO REPORT · LITE

beam-cloud/beta9

Default branch main · commit b8979ae3 · scanned 6/20/2026, 9:41:18 AM

GitHub: 1,673 stars · 144 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 beam-cloud/beta9, 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 README's opening to clarify core purpose and avoid Apache Beam confusion

    Why:

    CURRENT
    Beam is a fast, open-source runtime for serverless AI workloads. It gives you a Pythonic interface to deploy and scale AI applications with zero infrastructure overhead.
    COPY-PASTE FIX
    Beam is an ultrafast, open-source platform for serverless GPU inference, sandboxes, and background jobs, specifically designed for AI workloads. It provides a Pythonic interface to deploy and scale AI applications with zero infrastructure overhead.
  • mediumreadme#2
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Why Beam? (or Beam vs. Alternatives)

Beam stands out by offering [mention 2-3 key differentiators, e.g., self-hostable, ultrafast cold starts, specific GPU support, Pythonic interface] compared to other serverless AI platforms like Modal, RunPod, or Baseten. Our focus is on [specific benefit, e.g., providing a fully open-source, flexible solution for high-performance AI inference and fine-tuning].
  • lowabout#3
    Refine the repository description for clarity

    Why:

    CURRENT
    Ultrafast serverless GPU inference, sandboxes, and background jobs
    COPY-PASTE FIX
    Ultrafast, open-source platform for serverless GPU inference, sandboxes, and background jobs, optimized for AI/LLM workloads.

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 beam-cloud/beta9
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Cloud Vertex AI Endpoints
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud Vertex AI Endpoints · recommended 2×
  2. Azure Machine Learning Endpoints · recommended 2×
  3. Modal Labs · recommended 2×
  4. RunPod Serverless · recommended 2×
  5. Baseten · recommended 2×
  • CATEGORY QUERY
    How to deploy and scale serverless Python AI models with GPU acceleration and zero infrastructure?
    you: not recommended
    AI recommended (in order):
    1. AWS Lambda
    2. AWS SageMaker Endpoint
    3. AWS ECS (Elastic Container Service)
    4. AWS EKS (Elastic Kubernetes Service)
    5. Google Cloud Functions
    6. Google Cloud Vertex AI Endpoints
    7. Azure Functions
    8. Azure Machine Learning Endpoints
    9. Modal Labs
    10. RunPod Serverless
    11. Baseten
    12. Replicate

    AI recommended 12 alternatives but never named beam-cloud/beta9. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best serverless container platforms for ultrafast LLM inference and fine-tuning?
    you: not recommended
    AI recommended (in order):
    1. AWS SageMaker Serverless Inference
    2. AWS SageMaker Training
    3. Google Cloud Vertex AI Endpoints
    4. Google Cloud Vertex AI Training
    5. Azure Machine Learning Endpoints
    6. Azure Machine Learning Training
    7. RunPod Serverless
    8. Modal Labs
    9. Baseten

    AI recommended 9 alternatives but never named beam-cloud/beta9. 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 beam-cloud/beta9?
    pass
    AI named beam-cloud/beta9 explicitly

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

  • If a team adopts beam-cloud/beta9 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named beam-cloud/beta9 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 beam-cloud/beta9 solve, and who is the primary audience?
    pass
    AI named beam-cloud/beta9 explicitly

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

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite