--- title: "ai-getting-started vs dstack" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/a16z-infra-ai-getting-started-vs-dstack-tee-dstack" tools: ["a16z-infra-ai-getting-started", "dstack-tee-dstack"] --- # ai-getting-started vs dstack *GraphCanon updated Aug 15, 2026* ## Verdict Pick ai-getting-started if ai-getting-started is a TypeScript-based JavaScript AI tool tailored for weekend projects, offering everything from model implementation to deployment configurations; pick dstack if dstack offers infrastructure for running AI workloads in Trusted Execution Environments ensuring privacy and security. [ai-getting-started](https://ai-getting-started.com/) reports 4.1k GitHub stars, 660 forks, and 16 open issues, last pushed Aug 21, 2024. [dstack](https://dstack.org) has 519 stars, 91 forks, and 178 open issues, last pushed Jul 31, 2026. Figures are from public GitHub metadata via [ai-getting-started's repository](https://github.com/a16z-infra/ai-getting-started) and [dstack's repository](https://github.com/Dstack-TEE/dstack). | | [ai-getting-started](/tools/a16z-infra-ai-getting-started.md) | [dstack](/tools/dstack-tee-dstack.md) | | --- | --- | --- | | Tagline | A Javascript AI getting started stack for weekend projects | Open framework for confidential AI | | Stars | 4,141 | 519 | | Forks | 660 | 91 | | Open issues | 16 | 178 | | Language | TypeScript | Rust | | Adopt for | ai-getting-started is a TypeScript-based JavaScript AI tool tailored for weekend projects, offering everything from model implementation to deployment configurations. | Dstack offers infrastructure for running AI workloads in Trusted Execution Environments ensuring privacy and security. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Developer Tools, Model Training, Vector Databases | Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ai-getting-started](/tools/a16z-infra-ai-getting-started.md) | [dstack](/tools/dstack-tee-dstack.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 723d | 1d | | Open issues (now) | 16 | 178 | | Stars delta | 0 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/a16z-infra-ai-getting-started/trust.md) | [trust report](/tools/dstack-tee-dstack/trust.md) | ## Decision facts: ai-getting-started - **Adopt for:** ai-getting-started is a TypeScript-based JavaScript AI tool tailored for weekend projects, offering everything from model implementation to deployment configurations. ## Decision facts: dstack - **Adopt for:** Dstack offers infrastructure for running AI workloads in Trusted Execution Environments ensuring privacy and security. ## Choose when ### Choose ai-getting-started if… - ai-getting-started is primarily TypeScript; dstack is Rust. - License: ai-getting-started is MIT, dstack is Apache-2.0. - Tags unique to ai-getting-started: deployment, image models, javascript, text models. - Also covers Model Training, Vector Databases. - ai-getting-started ships Docker support for self-hosted deployment. - * Use this tool if you are already familiar with or prefer working in TypeScript and want an easy entry point into AI project development. ### Choose dstack if… - dstack is primarily Rust; ai-getting-started is TypeScript. - License: dstack is Apache-2.0, ai-getting-started is MIT. - Tags unique to dstack: confidential-ai, intel-tdx, private-ai, safe-ai. - When you need to run confidential AI computations that require Intel TDX or NVIDIA GPUs to ensure data and code are protected from potential threats at runtime ## When NOT to use ai-getting-started - * If your focus is on developing large-scale, production-level applications, this tool may not offer the necessary scalability features. - * Not suitable if you require highly customized vector stores or specific AI model training environments beyond what the package provides as it focuses more on a general setup. ## When NOT to use dstack - Avoid if your AI workloads do not benefit from confidential computing features as the overhead of using Intel TDX may not provide any advantage and can add complexity - Not suitable for hardware setups that do not support Intel TDX or AMD SEV-SNP, limiting flexibility compared to broader hardware support offered by competitors ## Common questions ### What is the difference between ai-getting-started and dstack? ai-getting-started: A Javascript AI getting started stack for weekend projects. dstack: Open framework for confidential AI. See the comparison table for live GitHub stats and shared categories. ### When should I choose ai-getting-started over dstack? Choose ai-getting-started over dstack when ai-getting-started is primarily TypeScript; dstack is Rust; License: ai-getting-started is MIT, dstack is Apache-2.0; Tags unique to ai-getting-started: deployment, image models, javascript, text models; Also covers Model Training, Vector Databases; ai-getting-started ships Docker support for self-hosted deployment; * Use this tool if you are already familiar with or prefer working in TypeScript and want an easy entry point into AI project development. ### When should I choose dstack over ai-getting-started? Choose dstack over ai-getting-started when dstack is primarily Rust; ai-getting-started is TypeScript; License: dstack is Apache-2.0, ai-getting-started is MIT; Tags unique to dstack: confidential-ai, intel-tdx, private-ai, safe-ai; When you need to run confidential AI computations that require Intel TDX or NVIDIA GPUs to ensure data and code are protected from potential threats at runtime. ### When should I avoid ai-getting-started? * If your focus is on developing large-scale, production-level applications, this tool may not offer the necessary scalability features. * Not suitable if you require highly customized vector stores or specific AI model training environments beyond what the package provides as it focuses more on a general setup. ### When should I avoid dstack? Avoid if your AI workloads do not benefit from confidential computing features as the overhead of using Intel TDX may not provide any advantage and can add complexity Not suitable for hardware setups that do not support Intel TDX or AMD SEV-SNP, limiting flexibility compared to broader hardware support offered by competitors ### Is ai-getting-started or dstack more popular on GitHub? ai-getting-started has more GitHub stars (4,141 vs 519). Stars measure visibility, not whether either tool fits your constraints. ### Are ai-getting-started and dstack open source? Yes - both are open-source projects on GitHub (ai-getting-started: MIT, dstack: Apache-2.0). ### Where can I find alternatives to ai-getting-started or dstack? GraphCanon lists graph-backed alternatives at [ai-getting-started alternatives](/tools/a16z-infra-ai-getting-started/alternatives) and [dstack alternatives](/tools/dstack-tee-dstack/alternatives) ([ai-getting-started markdown twin](/tools/a16z-infra-ai-getting-started/alternatives.md), [dstack markdown twin](/tools/dstack-tee-dstack/alternatives.md)), 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](/compare/a16z-infra-ai-getting-started-vs-dstack-tee-dstack.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ai-getting-started or dstack? ai-getting-started: Dormant. dstack: 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 ai-getting-started and dstack? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ai-getting-started trust report](/tools/a16z-infra-ai-getting-started/trust); [dstack trust report](/tools/dstack-tee-dstack/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=a16z-infra-ai-getting-started`](/api/graphcanon/graph?tool=a16z-infra-ai-getting-started) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_