Complete pricing guide for Llama Stack. Compare all plans, analyze costs, and find the perfect tier for your needs.
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Pricing sourced from Llama Stack · Last verified March 2026
Detailed feature comparison coming soon. Visit Llama Stack's website for complete plan details.
View Full Features →Yes. The listed URL is https://github.com/meta-llama/llama-stack, the official public GitHub repository for Llama Stack. This revised listing is based on the Llama Stack identity rather than unrelated Open GenAI Stack repository data.
Llama Stack provides standardized APIs and composable building blocks for Llama application development, including inference, agents, tools, safety, retrieval, evaluation, and provider-based distributions. It is intended for developers building AI applications that need consistent behavior across local, hosted, and production environments.
Yes. The public repository has a $0 listed software price, self-hosted use has a $0/month Llama Stack fee, and no fixed SaaS subscription tiers are listed in the repository. Deployment costs may still apply for compute, GPUs, hosting, model providers, vector databases, storage, observability, and engineering operations.
Llama Stack is best suited for developers, AI engineers, and platform teams that want standardized infrastructure for building Llama-based AI applications and agents. It is less appropriate for business users who need a finished no-code product with packaged onboarding, billing, and support.
Teams should evaluate Llama Stack as an open-source framework and API layer rather than a hosted agent workspace. Compare its provider matrix, distribution model, SDK support, documentation, license terms, deployment requirements, and operational complexity against alternatives such as LangChain, Ollama, Together AI, and OpenAI Agents SDK.
AI builders and operators use Llama Stack to streamline their workflow.
Try Llama Stack Now →The industry-standard framework for building production-ready LLM applications with comprehensive tool integration, agent orchestration, and enterprise observability through LangSmith.
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