About dstack
dstack is an open-source AI container orchestration tool that allows ML teams to define and run GPU workloads across any cloud provider - including AWS, GCP, Azure, and Lambda Labs - using simple YAML configuration files, similar to how Docker Compose simplifies container management. It abstracts away cloud-specific differences, enabling teams to switch providers or run hybrid workloads without changing their workflow definitions. dstack supports fine-tuning runs, training jobs, development environments, and model serving with automatic GPU provisioning.
Best for:
ML engineering teams that want a simple, cloud-agnostic way to define and run GPU workloads across multiple cloud providers.
Key Features
- Cloud-agnostic GPU workload orchestration
- YAML-based workflow definition for simplicity
- Support for AWS, GCP, Azure, Lambda, and more
- Development environments, training, and serving configurations
- Open-source with self-hosted deployment option
Pros & Cons
Pros
- Cloud-agnostic design prevents vendor lock-in
- Simple YAML configuration lowers the barrier to GPU orchestration
- Fully open-source and self-hostable for maximum control
Cons
- Requires existing cloud provider accounts and credentials setup
- Smaller community and ecosystem compared to Kubernetes-based solutions
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AI Glossary
The simulation of human intelligence in machines programmed to think, learn, and problem-solve. AI encompasses machine learning, natural language processing, computer vision, and more.
A deep learning model trained on vast text datasets to understand and generate human-like language. Examples include GPT-4, Claude, and Gemini.
AI systems that create new content - text, images, audio, video, or code - based on patterns learned during training rather than retrieving existing data.
The practice of designing and refining input text (prompts) to guide an AI model toward producing more accurate, relevant, or creative outputs.
A computing architecture inspired by the human brain, consisting of interconnected layers of nodes that learn to recognise patterns in data.
A branch of AI where algorithms improve automatically through experience and exposure to data, without being explicitly programmed for each task.
AI technology that enables computers to understand, interpret, and generate human language - the foundation of chatbots, translation tools, and voice assistants.
The process of further training a pre-trained AI model on a specific, smaller dataset to specialise it for a particular task or domain.
When an AI model generates plausible-sounding but factually incorrect or entirely fabricated information, often presenting it with false confidence.
The units of text (roughly 4 characters or ¾ of a word in English) that LLMs process. Model costs and context limits are measured in tokens.
The maximum amount of text (measured in tokens) an LLM can process in a single interaction - both input and output combined.
A technique that enhances LLM outputs by fetching relevant external documents at query time, grounding responses in up-to-date or proprietary data.
Numerical vector representations of text, images, or other data that capture semantic meaning, enabling AI to measure similarity and retrieve relevant content.
The process of running a trained AI model to produce predictions or outputs from new input data - distinct from the training phase.
An AI system that autonomously plans and executes multi-step tasks by combining reasoning, tool use (web search, code execution, APIs), and memory.
AI models that can process and generate multiple types of data - such as text, images, audio, and video - within a single unified system.
A large-scale AI model trained on broad data that serves as a base for many downstream applications via fine-tuning or prompting.
A pricing model where core features are available for free, with premium features, higher usage limits, or advanced capabilities offered via paid plans.
A set of protocols that allows developers to integrate an AI tool's capabilities directly into their own applications and workflows.
Zero-shot means the model handles a task without any examples; few-shot means it is given a small number of examples in the prompt to guide its response.
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