Enterprise AI platform offering language models, search tools, and workplace AI solutions with private, secure, and customizable deployment options.
Cohere is an enterprise-focused AI platform founded in 2019 that develops and deploys large language models, retrieval systems, and workplace AI tools designed specifically for business environments where data privacy, security, and customization are paramount. Unlike consumer-facing AI assistants, Cohere targets organizations that need to deploy generative AI within their own infrastructure—whether on-premises, in a virtual private cloud, or via Cohere's managed Model Vault—ensuring proprietary data never leaves the customer's controlled environment. The company's product portfolio is organized around several core offerings. North is its flagship enterprise AI platform that unifies workplace productivity tools, while Compass provides intelligent search and discovery to surface business insights from unstructured data. On the model side, the Command family delivers high-performance generative language models supporting 23 languages, Transcribe (a newer addition) handles speech-to-text in 14 languages, and the Aya family covers research-oriented multilingual capabilities across 70+ languages. For retrieval-augmented generation workflows, Cohere offers Embed for multimodal semantic search and Rerank for relevance-based result refinement—both widely adopted in production RAG pipelines. Cohere serves regulated industries including financial services, healthcare, public sector, telecommunications, energy, manufacturing, and technology, with enterprise customers including Fujitsu, Oracle, and major banks. The company emphasizes fine-tuning models on customer proprietary data, partnership-style customization engagements, and compliance with industry security certifications. Cohere Labs, its research arm, contributes open-science initiatives like the Aya project and runs scholar and grant programs. Pricing is enterprise-oriented with API access for developers and demo-driven sales motion for larger deployments. While Cohere lacks a popular consumer chatbot, its focus on private deployment, agentic capabilities, and retrieval quality has made it a credible alternative to OpenAI and Anthropic for organizations prioritizing data sovereignty and integration with existing business systems over raw frontier-model benchmark scores.
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Cohere differentiates itself by allowing customers to deploy models entirely within their own infrastructure. Options include virtual private cloud (VPC) deployment on AWS, Azure, OCI, or Google Cloud, fully on-premises installations for air-gapped environments, or the Cohere-managed Model Vault for dedicated single-tenant inference. This architecture ensures sensitive data and prompts never traverse shared multi-tenant systems, making Cohere viable for banks, healthcare organizations, and government agencies with strict data residency requirements.
Cohere's Embed model converts text and multimodal inputs into dense vector representations optimized for semantic search, while Rerank applies a second-pass relevance scoring to refine results from any retrieval system. Together they form a production-grade RAG foundation widely adopted across vector databases like Pinecone, Weaviate, and Elasticsearch. Many teams use Cohere's retrieval models alongside other companies' generative LLMs because of their strong retrieval quality benchmarks.
Cohere supports fine-tuning Command models on proprietary customer data and offers partnership-style engagements where its team co-develops bespoke AI solutions. This goes beyond simple API access—customers can train models on internal terminology, documents, and workflows, creating differentiated AI capabilities aligned with their specific business processes, security requirements, and infrastructure constraints.
North is Cohere's turnkey workplace AI platform that bundles generative, retrieval, and agentic capabilities into a single deployable system. It connects to enterprise data sources and applications to power productivity workflows, knowledge discovery, and automated actions, positioning itself as an alternative to building custom RAG infrastructure from scratch or relying on consumer-grade assistants like ChatGPT Enterprise or Microsoft Copilot.
$0
$0.15 per 1M input tokens / $0.60 per 1M output tokens
$2.50 per 1M input tokens / $10.00 per 1M output tokens
$2.50 per 1M input tokens / $10.00 per 1M output tokens
$0.10 per 1M tokens
$2.00 per 1K searches
Training: ~$3.00 per 1M tokens; Inference: 2x base model rate
Custom (typically $100K–$1M+ annually)
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