SaaS

supporting

time to get started

benefits

limitations

pricing structure

Best for


Get started with LLMs instantly through Cohere's Software as a Service (SaaS) platform, all without managing backend infrastructure.


Private Deployments

Compute and infrastructure management required

Organizations with strict data residency requirements

Need for maximum control and customization

Organizations dealing with sensitive or customer data

Bring Cohere's models into your own infrastructure, whether in the cloud or on-premises. Maintain a high level of security and control over sensitive information.

For ultimate customization and control over security

Cloud AI Platforms

Deployment depends on cloud provider features

Companies seeking easy integration with cloud service

Use Cohere's models through a variety of platforms such as Amazon SageMaker, Amazon Bedrock, OCI Generative AI, etc.



For running Cohere’s models across cloud AI platforms

Let’s work together to redefine the future using language AI. Apply now.

We charge differently for input and output tokens. You are charged based on the sum of tokens processed. Fine-tuning inference costs will be the same as our base models.

Each text (input) classified counts as one classification. You are not charged for examples provided.

Embeddings perform best when the text to be embedded is less than 512 tokens. You can create up to 96 embeddings per API call.

We charge differently for input and output tokens. You are charged based on the sum of tokens processed. Only Command Light is available for fine-tuning at this moment. Fine-tuning inference costs will be the same as our base models.

We count a single search unit as a query with up to 100 documents to be ranked. Documents longer than 510 tokens when including the length of the search query will be split up into multiple chunks, where each chunk counts as a singular document. Fine-tuning inference costs will be the same as our base models.

We charge differently for input and output tokens. You are charged based on the sum of tokens processed.

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