REPOGEO REPORT · LITE

beam-cloud/beta9

Default branch main · commit f46c96b4 · scanned 5/10/2026, 11:01:21 AM

GitHub: 1,644 stars · 142 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 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 the README's opening statement to clearly define the project

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. It provides a Pythonic interface to deploy and scale AI applications with zero infrastructure overhead, replacing complex setups for LLM inference and other GPU-accelerated tasks.

mediumreadme#2

Add a direct comparison or problem statement to the README

Why:

COPY-PASTE FIX

Unlike traditional cloud services or complex Kubernetes deployments, Beam offers a streamlined, Python-first approach to running and scaling generative AI, LLM inference, and fine-tuning workloads on GPUs.

lowtopics#3

Expand relevant topics to reinforce core identity

Why:

CURRENT

autoscaler, cloudrun, cuda, developer-productivity, distributed-computing, faas, fine-tuning, functions-as-a-service, generative-ai, gpu, large-language-models, llm, llm-inference, ml-platform, paas, self-hosted, serverless, serverless-containers

COPY-PASTE FIX

autoscaler, cloudrun, cuda, developer-productivity, distributed-computing, faas, fine-tuning, functions-as-a-service, generative-ai, gpu, large-language-models, llm, llm-inference, ml-platform, mlops-platform, ai-platform, paas, self-hosted, serverless, serverless-containers

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

Of all named tools, what % are you?

Top rival

AWS SageMaker Serverless Inference

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

AWS SageMaker Serverless Inference · recommended 1×
Google Cloud Vertex AI Endpoints · recommended 1×
Azure Machine Learning Endpoints · recommended 1×
RunPod Serverless · recommended 1×
Banana · recommended 1×

CATEGORY QUERY
How to deploy and scale large language model inference on serverless GPUs?
you: not recommended
AI recommended (in order):
1. AWS SageMaker Serverless Inference
2. Google Cloud Vertex AI Endpoints
3. Azure Machine Learning Endpoints
4. RunPod Serverless
5. Banana
6. Replicate
7. Modal Labs
8. Kubernetes (kubernetes/kubernetes)
9. KServe (kserve/kserve)
10. Kubeflow (kubeflow/kubeflow)
11. Ray Serve (ray-project/ray)
12. Amazon EKS
13. Google Kubernetes Engine (GKE)
14. Azure Kubernetes Service (AKS)
AI recommended 14 alternatives but never named beam-cloud/beta9. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are options for running serverless GPU-accelerated AI workloads with Python?
you: not recommended
AI recommended (in order):
1. AWS Lambda
2. AWS Fargate
3. Amazon ECS
4. EKS
5. Google Cloud Run
6. Azure Container Apps
7. Modal (modal-labs/modal-client)
8. RunPod Serverless (runpod/runpod-python)
9. Baseten (basetenlabs/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?

Embed your GEO score

Drop this badge into the README of beam-cloud/beta9. 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/beam-cloud/beta9.svg)](https://repogeo.com/en/r/beam-cloud/beta9)

HTML

<a href="https://repogeo.com/en/r/beam-cloud/beta9"><img src="https://repogeo.com/badge/beam-cloud/beta9.svg" alt="RepoGEO" /></a>

Pro

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