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Alauda Container Platform is included as an Honorable Mention in the 2024 Gartner® Magic Quadrant™ for DevOps Platforms
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Alauda AI

Alauda AI is a Kubernetes-native, enterprise-grade MLOps platform built to power AI innovation across large-scale infrastructure. It streamlines the full AI lifecycle from development to deployment, combining advanced computing resource management, out-of-the-box support for LLMs, and modular integrations. With native support for multi-cloud and multi-datacenter environments, Alauda AI empowers enterprises to operationalize AI with speed, control, and scale.
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Unified Model Lifecycle

Seamlessly manage development, training, fine-tuning, and serving from one platform.

AI Infrastructure as a Service

Enable internal teams to self-serve compute resources and environments securely.

Enterprise LLM Applications

Quickly deploy generative AI and agent-based services with built-in support for LLM fine-tuning and inference.

Automated AI Pipelines
Use visual tools and reusable components to design and execute automated ML workflows.
Multi-Cloud AI Operations
Optimize and scale AI workloads across cloud, on-prem, and edge environments.

Benefits of Alauda AI

Accelerate AI Innovation

Quickly develop, fine-tune, and deploy LLMs and other models using integrated tools, prebuilt templates, and support for multimodal AI workloads.

Maximize Resource Efficiency

Optimize usage of GPUs, NPUs, and CPUs through intelligent scheduling, quota management, and dynamic workload scaling.

Simplify AI Operations

Automate model lifecycle management with visual pipelines, one-click deployment, observability, and DevSecOps-aligned controls.

Adapt with Flexibility

Extend capabilities with customizable runtimes, training templates, and toolchain integrations to support evolving enterprise needs.

Core Features of Alauda AI

Infrastructure Management
Unified management and scheduling for heterogeneous compute resources (GPU/NPU/CPU), with multi-cloud deployment support and built-in observability and quota enforcement.
Model Development
Integrated development environments (Jupyter, VSCode), visual ML pipelines (Kubeflow, Elyra), and support for major frameworks like PyTorch, TensorFlow, ONNX, HuggingFace, and SQLFlow.
Fine-Tuning & Training
Built-in support for LoRA, DPO, SFT, RLFH, and more for partial/full LLM tuning, along with distributed training capabilities (FSDP, DDP, pipeline/model parallelism) and custom training template support.
Model Deployment & Inference
One-click conversion from model to online service using vLLM, Triton, or Seldon runtimes, with REST/gRPC/OpenAI-compatible endpoints and features like autoscaling, canary release, and monitoring.
Monitoring & Operations
End-to-end tracking of training (MLFlow, TensorBoard), inference observability, performance alerts, and visual dashboards to ensure optimal AI workload performance.
Applications & Ecosystem

Build intelligent agents (e.g., RAG workflows), integrate application frameworks like Gradio and Streamlit, and connect with feature platforms like Feast for real-time data and model integration.