Home/Compare/daytona vs dust

Comparison

daytona vs dust

Verdict

Pick daytona when requirements: While creating and managing sandboxes in Daytona, users require an API key to authenticate their requests.; pick dust when tags unique to dust: agents, llm, large-language-models, rust.

Markdown twin · daytona alternatives · dust alternatives

GraphCanon updated today

daytona logo

daytona

daytonaio/daytona

72kpushed Jul 9, 2026
vs
dust logo

dust

dust-tt/dust

1.4kpushed Jul 11, 2026

Trust & integrity

Signaldaytonadust
Maintenance
Very active (1d since push)
As of today · github_public_v1
Very active (0d since push)
As of today · github_public_v1
Provenance
Not a fork · Organization account
As of today · github_public_v1
Not a fork · Organization account
As of today · github_public_v1
Security (OSV)
No lockfile
As of today · none
No lockfile
As of today · none

Tagline

daytona
Secure and Elastic Infrastructure for Running AI-Generated Code
dust
Custom AI agent platform to speed up your work.

Stars

daytona
72k
dust
1.4k

Forks

daytona
5.7k
dust
302

Open issues

daytona
444
dust
224

Language

daytona
-
dust
TypeScript

Adopt for

daytona
Daytona, known for its secure and elastic infrastructure tailored specifically to run AI-generated code, stands distinct in the developer tools landscape.
dust
-

Persona

daytona
-
dust
-

Runtime

daytona
-
dust
-

License

daytona
The license details for Daytona are unknown at present.
dust
MIT

Last pushed

daytona
Jul 9, 2026
dust
Jul 11, 2026

Categories

daytona
AI Agents, Developer Tools
dust
AI Agents, LLM Frameworks, Developer Tools

Trust and health

Days since push

daytona
1d
dust
0d

Open issues (now)

daytona
444
dust
224

Full report

Choose daytona if…

  • Requirements: While creating and managing sandboxes in Daytona, users require an API key to authenticate their requests..
  • Tags unique to daytona: ai-runtime, code-execution, agentic-workflow, code-interpreter.
  • When your project requires running AI-generated code securely and you need an on-demand scalable environment that adjusts automatically based on demand.

When NOT to use daytona

  • Avoid using Daytona if you have strict requirements for the programming languages it supports, since this information is currently unknown.
  • Daytona might not be suitable if your development workflow does not require API access or an online dashboard, as its user interface and API integration are key components.

Choose dust if…

  • Tags unique to dust: agents, llm, large-language-models, rust.
  • Also covers LLM Frameworks.
  • dust ships Docker support for self-hosted deployment.

When NOT to use dust

  • AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
  • LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
  • Developer Tools: A gateway is overkill when you're pinned to a single provider and model.

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: daytona 72k · dust 1.4k (synced Jul 11, 2026).

Common questions

What is the difference between daytona and dust?
daytona: Secure and Elastic Infrastructure for Running AI-Generated Code. dust: Custom AI agent platform to speed up your work.. See the comparison table for live GitHub stats and shared categories.
When should I choose daytona over dust?
Choose daytona over dust when Requirements: While creating and managing sandboxes in Daytona, users require an API key to authenticate their requests.; Tags unique to daytona: ai-runtime, code-execution, agentic-workflow, code-interpreter; When your project requires running AI-generated code securely and you need an on-demand scalable environment that adjusts automatically based on demand.
When should I choose dust over daytona?
Choose dust over daytona when Tags unique to dust: agents, llm, large-language-models, rust; Also covers LLM Frameworks; dust ships Docker support for self-hosted deployment.
When should I avoid daytona?
Avoid using Daytona if you have strict requirements for the programming languages it supports, since this information is currently unknown. Daytona might not be suitable if your development workflow does not require API access or an online dashboard, as its user interface and API integration are key components.
When should I avoid dust?
AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Developer Tools: A gateway is overkill when you're pinned to a single provider and model.
Is daytona or dust more popular on GitHub?
daytona has more GitHub stars (72,233 vs 1,413). Stars measure visibility, not whether either tool fits your constraints.
Are daytona and dust open source?
Yes - both are open-source projects on GitHub.
Where can I find alternatives to daytona or dust?
GraphCanon lists graph-backed alternatives at daytona alternatives and dust alternatives (daytona markdown twin, dust markdown twin), ranked by typed relationship edges rather than popularity votes.
Is there a machine-readable version of this comparison?
Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, daytona or dust?
daytona: Very active. dust: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
Where are the full trust reports for daytona and dust?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: daytona trust report; dust trust report.