[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]  Ehraz Ahmed [@ehrazahmedd](/creator/twitter/ehrazahmedd) on x 1861 followers Created: 2025-07-10 09:59:17 UTC $NBIS is a full-stack AI Cloud and it is building an ecosystem which truly separates itself from commodity GPU lessors like $IREN, $CORZ or even $CRWV. It's important to keep in mind that Nebius isn’t a GPU landlord. It’s a full-stack AI cloud, from orchestration to deployment already used by 60k+ devs. Their real moat is "stickiness". Most customers run Nebius software on top of Nebius hardware. Let's get into it... X. Infrastructure-as-a-Service (IaaS): Tech stack: Slurm, Kubernetes, orchestration tools. a. What this means: Nebius offers foundational infrastructure orchestration tools, not just raw GPU compute. b. Slurm: Popular in HPC for managing large-scale clusters, used in scientific workloads, biotech & simulations. c. Kubernetes: Container orchestration for ML workloads, making Nebius cloud-native and scalable. d. Value: This gives customers flexible control over job scheduling, multi-tenant workloads, autoscaling which is the building blocks of a programmable cloud, not just rented metal. Main idea: This is what separates hyperscalers from colocation farms. X. MLOps Suite for teams without In-House infra: What this means: For startups or small AI teams without dedicated infra engineers, Nebius provides out-of-the-box tools to handle a. Data versioning. b. Model training pipelines. c. Monitoring, rollback, reproducibility. Why this matters: MLOps is often the bottleneck for companies adopting AI having to hire a DevOps + ML infra team can slow you down by months. Strategy: Nebius abstracts this away, making AI accessible for non-cloud-native companies. X. AI Studio: Fine-Tuning, Inference, Deployment: Think: Nebius’s version of Vertex AI ( $GOOG ) or SageMaker $AMZN (AWS) but much leaner and dev-friendly. Capabilities: a. Upload any model (open weights). b. Finetune on your dataset. c. Perform inference (batch or real-time). d. Export for deployment (on Nebius or elsewhere). Why it matters: AI Studio allows customers to get value out of models without ever touching infra, that's a massive barrier removed. Ease of use: It’s a SaaS-like layer that rides on top of GPU infrastructure = recurring, high-margin revenue. X. 60,000+ AI Studio Users; 50+ New features in Q1. Signal of traction: 60k+ users is not a pilot anymore, IT'S A PLATFORM. Velocity of execution: 50+ new features shipped in one quarter shows: a. Active roadmap. b. High dev velocity. c. Rapid feedback loops from users. Adoption: This is a living platform, not a static product. That’s hyperscaler DNA in a smallcap. X. SDKs for Go and Python; Integrations with Hugging Face, Metaflow, LlamaIndex, Postman, and more. Go + Python SDKs: Let developers embed Nebius cloud functions into their apps or pipelines easily. Integrations: a. Hugging Face: Model zoo integration + finetuning = plug-and-play LLMs. b. Metaflow (Netflix): Workflow management for ML, useful for versioning and reproducibility. c. LlamaIndex: Retrieval-augmented generation (RAG) tool for enterprise AI agents. d. Postman: APIs for inference or model deployment testing, suggesting REST-first architecture. Vertical integration: These integrations prove Nebius isn’t building in a silo, they’re playing nice with the ecosystem. X. Most managed customers use Nebius software stack, not just GPU metal. This is the most important point from a business perspective. Why it matters: Selling just compute = low margin, commoditized. Selling compute + software = sticky revenue, higher margins, low churn. Examples of "stickiness": Fine-tuned models stored on Nebius infra. Pipelines wired into Nebius MLOps. Inference endpoints exposed via Nebius SDKs. This is how Nebius avoids being “just another AI GPU cloud" and on path to become the ultimate full stack AI cloud. That’s how AWS started.  XXXXX engagements  **Related Topics** [iren](/topic/iren) [devs](/topic/devs) [$corz](/topic/$corz) [gpu](/topic/gpu) [coins ai](/topic/coins-ai) [ahmed](/topic/ahmed) [$nbis](/topic/$nbis) [$iren](/topic/$iren) [Post Link](https://x.com/ehrazahmedd/status/1943248655076724975)
[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]
Ehraz Ahmed @ehrazahmedd on x 1861 followers
Created: 2025-07-10 09:59:17 UTC
$NBIS is a full-stack AI Cloud and it is building an ecosystem which truly separates itself from commodity GPU lessors like $IREN, $CORZ or even $CRWV.
It's important to keep in mind that Nebius isn’t a GPU landlord. It’s a full-stack AI cloud, from orchestration to deployment already used by 60k+ devs. Their real moat is "stickiness". Most customers run Nebius software on top of Nebius hardware.
Let's get into it...
X. Infrastructure-as-a-Service (IaaS): Tech stack: Slurm, Kubernetes, orchestration tools.
a. What this means: Nebius offers foundational infrastructure orchestration tools, not just raw GPU compute.
b. Slurm: Popular in HPC for managing large-scale clusters, used in scientific workloads, biotech & simulations.
c. Kubernetes: Container orchestration for ML workloads, making Nebius cloud-native and scalable.
d. Value: This gives customers flexible control over job scheduling, multi-tenant workloads, autoscaling which is the building blocks of a programmable cloud, not just rented metal.
Main idea: This is what separates hyperscalers from colocation farms.
X. MLOps Suite for teams without In-House infra:
What this means: For startups or small AI teams without dedicated infra engineers, Nebius provides out-of-the-box tools to handle a. Data versioning. b. Model training pipelines. c. Monitoring, rollback, reproducibility.
Why this matters: MLOps is often the bottleneck for companies adopting AI having to hire a DevOps + ML infra team can slow you down by months.
Strategy: Nebius abstracts this away, making AI accessible for non-cloud-native companies.
X. AI Studio: Fine-Tuning, Inference, Deployment: Think: Nebius’s version of Vertex AI ( $GOOG ) or SageMaker $AMZN (AWS) but much leaner and dev-friendly.
Capabilities: a. Upload any model (open weights). b. Finetune on your dataset. c. Perform inference (batch or real-time). d. Export for deployment (on Nebius or elsewhere).
Why it matters: AI Studio allows customers to get value out of models without ever touching infra, that's a massive barrier removed.
Ease of use: It’s a SaaS-like layer that rides on top of GPU infrastructure = recurring, high-margin revenue.
X. 60,000+ AI Studio Users; 50+ New features in Q1. Signal of traction: 60k+ users is not a pilot anymore, IT'S A PLATFORM.
Velocity of execution: 50+ new features shipped in one quarter shows: a. Active roadmap. b. High dev velocity. c. Rapid feedback loops from users.
Adoption: This is a living platform, not a static product. That’s hyperscaler DNA in a smallcap.
X. SDKs for Go and Python; Integrations with Hugging Face, Metaflow, LlamaIndex, Postman, and more. Go + Python SDKs: Let developers embed Nebius cloud functions into their apps or pipelines easily.
Integrations: a. Hugging Face: Model zoo integration + finetuning = plug-and-play LLMs. b. Metaflow (Netflix): Workflow management for ML, useful for versioning and reproducibility. c. LlamaIndex: Retrieval-augmented generation (RAG) tool for enterprise AI agents. d. Postman: APIs for inference or model deployment testing, suggesting REST-first architecture.
Vertical integration: These integrations prove Nebius isn’t building in a silo, they’re playing nice with the ecosystem.
X. Most managed customers use Nebius software stack, not just GPU metal. This is the most important point from a business perspective.
Why it matters: Selling just compute = low margin, commoditized. Selling compute + software = sticky revenue, higher margins, low churn.
Examples of "stickiness": Fine-tuned models stored on Nebius infra. Pipelines wired into Nebius MLOps. Inference endpoints exposed via Nebius SDKs.
This is how Nebius avoids being “just another AI GPU cloud" and on path to become the ultimate full stack AI cloud. That’s how AWS started.
XXXXX engagements
Related Topics iren devs $corz gpu coins ai ahmed $nbis $iren