Cohere

Cohere builds enterprise-focused NLP models specialized for search, retrieval-augmented generation, and text classification. Unlike general-purpose chatbot APIs, Cohere's models are purpose-built for enterprise search and understanding use cases.

Key use cases include:

• Semantic search with Embed — high-quality text embeddings that power search, recommendation, and clustering
• Reranking with Rerank — re-ordering search results by semantic relevance for dramatically better precision
• RAG with citations using Command models that ground responses in retrieved documents and provide source citations
• Text classification and sentiment analysis for business data
• Multilingual applications with models supporting 100+ languages

Cohere is used by enterprise teams building internal search, knowledge management, and document Q&A systems. Its Embed + Rerank combination is considered best-in-class for retrieval quality and is widely integrated into RAG pipelines alongside LangChain, LlamaIndex, and vector databases.

Cohere offers private cloud deployments on AWS, GCP, and Azure for enterprises requiring data isolation and compliance.

1. Create an account at dashboard.cohere.com and get your trial API key.
2. Install the SDK:

   pip install cohere

3. Set your API key:

   export COHERE_API_KEY='...'

4. Try semantic search with Embed:

   import cohere
   co = cohere.ClientV2()
   response = co.embed(
       texts=['Hello world', 'Goodbye world'],
       model='embed-v4.0',
       input_type='search_document',
       embedding_types=['float']
   )

Pricing: Free trial tier with rate limits. Production plans start with pay-as-you-go: Embed is ~$0.10/1M tokens, Rerank is ~$2/1K searches. Command models are priced per token. Enterprise plans with private deployments available. See cohere.com/pricing.