dstack vs Continue
Side-by-side comparison to help you choose the best tool.
dstack
freedstack 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.
Continue
freeContinue is an open-source AI coding assistant that connects any LLM to VS Code and JetBrains, enabling developers to customise their AI coding experience with any model - local or cloud. It provides autocomplete, chat, and edit modes, and supports integration with local models via Ollama. Continue is popular with developers who want full control over their AI coding setup without vendor lock-in.
| Feature | dstack | Continue |
|---|---|---|
| Pricing | free | free |
| Category | - | - |
| Rating | 4.1 | 4.4 |
| Best For | ML engineering teams that want a simple, cloud-agnostic way to define and run GPU workloads across multiple cloud providers. | Developers wanting full control over their AI coding assistant — choosing any LLM, including local models, with zero vendor lock-in |
| Views | 5 | 4 |
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
Pros
- Completely free and open-source
- Use any LLM including local models via Ollama
- Full customisation and no vendor lock-in
Cons
- More setup than Copilot or Codeium
- Quality depends on LLM choice
- 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
- Open-source AI coding assistant
- Any LLM support (cloud & local)
- Ollama local model integration
- VS Code & JetBrains plugins
- Customisable context & prompts