Free
$0Free plan available.
Lamini is an enterprise LLM platform designed for software teams to develop and control their own LLMs. It offers built-in best practices for specializing LLMs on proprietary documents, improving performance, reducing hallucinations, offering citations, and ensuring safety. Lamini can be installed on-premise or on clouds securely and is the only platform for running LLMs on AMD GPUs and scaling to thousands with confidence.
Use the Lamini library to train high-performing LLMs on large datasets. Install Lamini on-premise or within your cloud environment. Utilize built-in best practices to specialize LLMs on your proprietary data to improve performance and accuracy.
Lamini utilizes built-in best practices for specializing LLMs on billions of proprietary documents, which improves performance and reduces hallucinations by up to 95%.
Lamini can be deployed in secure environments, including on-premise (including air-gapped setups) or within a VPC, to ensure your data remains private.
Lamini provides support through a dedicated contact form for reporting bugs, requesting features, or sharing feedback.
Lamini provides access to leading open-source models, including Llama 3.1, Mistral v0.3, and Phi 3.
Free plan available.
Use these comparison pages to understand the trade-offs between the models most relevant to Lamini.
Compare Amazon Nova Lite and Mistral Medium 3 across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for tool-augmented workflows versus tool-augmented workflows.
Compare Amazon Nova Pro and Mistral Medium 3 across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for tool-augmented workflows versus tool-augmented workflows.
Compare DeepSeek V4 Flash and Mistral Medium 3 across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus tool-augmented workflows.
Compare DeepSeek V4 Pro and Mistral Medium 3 across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus tool-augmented workflows.
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