One of the hallmarks of Llama 4 is its open-access foundations: Meta has released Scout and Maverick under a permissive community license, so there are no direct fees to use the core model weights. This means developers can download and run Llama 4 locally on personal servers or cloud GPUs without upfront per-token billing from a vendor, giving total flexibility over infrastructure and deployment costs.

When using managed inference platforms or cloud APIs that host Llama 4, pricing varies widely by provider and configuration. Multiple benchmark cost comparisons show Llama 4 Maverick’s inference can run at about $0.19 - $0.49 per million tokens, a fraction of many proprietary leaders, while delivering competitive performance on multimodal and reasoning benchmarks. This cost efficiency makes Llama 4 appealing for large-scale deployments where both quality and budget matter.

For self-hosting, the primary costs come from compute infrastructure, GPUs, energy, and maintenance rather than licensing or token fees. Scout’s 10 M token context can run efficiently on a single high-end GPU, making local deployment accessible, while Maverick’s MoE design scales well across distributed resources. Whether deployed via API or self-hosted systems, Llama 4 offers flexible pricing approaches that let teams balance performance, scale, and cost based on their specific needs.