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Integrate Cohere Models into Game Warden-Deployed Applications

Cohere is a leading artificial intelligence company specializing in the development and deployment of large language models (LLMs) and foundational models designed for enterprise applications. Through a strategic partnership with Cohere, Second Front (2F) is able to deploy Cohere models on the Game Warden platform, empowering customers with cutting-edge AI capabilities.

Cohere’s product suite delivers advanced natural language processing (NLP) and image processing solutions that enable enterprises to harness models capable of comprehending, generating, searching, and interacting with human language-and, increasingly, with images. These models support a wide range of use cases, including content creation, semantic search, conversational AI, and knowledge management, all with robust enterprise-grade security and scalability.

The following Cohere models can be deployed on Game Warden:

Model Usage
Command Cohere’s state-of-the-art family of generative models optimized for security and mission needs. With strong multilingual support, multi-modal capability, high performance, and advanced reasoning capabilities, the Command suit excels at retrieval-augmented generation (RAG), agentic workflows, and can process large context windows for complex tasks.
Embed Cohere’s Embed models, including multi-lingual support, turn text and images into embeddings to enable semantic retrieval in search systems, RAG architectures, and agentic applications - powering answers, insights, and action across the enterprise.
Rerank Rerank passes only the most relevant documents into your RAG pipeline and agentic workflows - reducing token use, minimizing latency, and boosting accuracy.

This guide describes when to use Cohere models on Game Warden, requirements and restrictions, integration steps, and compliance and monitoring requirements. Following these recommended practices will ensure a secure integration of Cohere into applications deployed on the Game Warden platform.

When to use Cohere on Game Warden

Deploying Cohere’s models within Game Warden on a cloud service–provided Kubernetes cluster, or on-premise, to include classified environments, enables Game Warden users to securely integrate generative AI capabilities into their applications while maintaining full control over their data and systems. This approach addresses privacy, compliance, and operational requirements that are often critical for mission use cases.

Key reasons to deploy within a cloud service–provided Kubernetes cluster, or on-premise:

  • Data Privacy and Security: Sensitive or regulated data never leaves the Game Warden secure environment, reducing risk and exposure to third-party cloud providers.
  • Compliance: Meet strict industry and government compliance standards (such as HIPAA, GDPR, FINRA, etc.), which may prohibit certain data from leaving controlled infrastructure.
  • Customization and Integration: Fine-tune models, integrate with proprietary datasets, and tightly couple AI with existing local workflows and databases.
  • Performance and Latency: Reduce latency and ensure reliable, high-speed response times, particularly important for mission-critical applications.
  • Cost Predictability: Provide more predictable operational costs, especially for high-volume usage, avoiding variable cloud charges.
  • Isolation: Full isolation from the public internet and shared cloud resources, meeting the needs of organizations with highly sensitive or classified workloads.
  • Control: Retain complete control over access, updates, and AI governance policies within their infrastructure.

Deployment requirements

To ensure optimal performance and reliable operation of Cohere models on Game Warden, deployments must meet the following support and infrastructure requirements. In addition to below information, please see Cohere documentation on Deploying Cohere Models in Private Environments. Your 2F Technical Implementation Manager will work closely with you to ensure requirements are met for:

  • GPU Acceleration: Cohere’s models require state-of-the-art GPU hardware for production-grade inference and training. NVIDIA A100 GPUs (or equivalent, such as H100) are recommended to support performance and scalability standards.
  • On-Premise & Private Cloud/Kubernetes: For on-premise or self-managed Kubernetes clusters (running on any cloud or private infrastructure), direct access to supported NVIDIA A100+ GPU resources is required. Ensure hardware compatibility and sufficient resource allocation prior to deployment.
  • Support Scope: Technical support is available for deployments that meet these hardware and infrastructure standards. Deployments on unsupported hardware, or in regions without guaranteed GPU availability, may not be eligible for full support or performance guarantees.

Note

Limited GPU availability may adversely impact deployments. Deployments in unsupported or under-resourced regions may experience delays or lack of support.

  • Required Instance Types: Deployments must use P4d or P4de (P4DN) EC2 instance types, which provide the necessary NVIDIA A100 GPUs.
  • Regional Availability: P4d/P4de instances are primarily available in select regions, with the highest availability in US West (Oregon). Availability in other regions is limited; please work with your Game Warden Technical Implementation Manager to verify instance stock before planning your deployment.
  • Required Machine Types: Use A2 (A2-highgpu or A2-megagpu) instances equipped with NVIDIA A100 GPUs.
  • Regional Availability: Supported A2 machine types are available in limited GCP regions. us-central1 (Iowa) and europe-west4 (Netherlands) typically have the best availability, but verify current capacity with GCP before deployment.
  • Required VM Types: Use ND A100 v4-series virtual machines, which feature NVIDIA A100 GPUs.
  • Regional Availability: ND A100 v4-series VMs currently have the highest availability in select regions such as East US, South Central US, and select European data centers. Please consult Azure’s product documentation or portal to confirm current regional availability.

Integration steps

Define your use case

Before integrating Cohere models on Game Warden, determine how your application will use the Cohere:

  • Which foundation models will you call?
  • What is the justification for Cohere integration?
  • What data will be sent to and returned from Cohere?
  • Will any Controlled Unclassified Information (CUI) or sensitive data be processed?

Clear definition of these parameters will inform and guide 2F’s evaluation of your use of artificial intelligence. Each deployment of Cohere on Game Warden ensures LLM interactions, data, and usage metrics are logically and often physically separated at the cloud service provider account level, or at the hardware level for on-premise deployments, aligning with enhanced security and regulatory requirements of serving national security missions.

Create a ticket in the Game Warden app

In the ticket, include the following information:

  • Specify the AI model(s) intended for use.
  • Confirm that the AI Attestation section in the Body of Evidence (BoE) is complete.
  • Provide the business justification for integrating with Cohere.
  • Describe the data that will be sent to and returned from Cohere.
  • Indicate whether any Controlled Unclassified Information (CUI) or other sensitive data will be processed.

Request Cohere configuration from 2F

Once approved, open a 2nd Support Ticket in the Game Warden app to have 2F Engineering configure Cohere.

Note: If Cohere is being configured for the first time, a new Certificate to Field (CtF) is required.

Integrate Cohere Model(s) API to your application

Review Cohere documentation for getting started with the Cohere models’ API and review Cohere Cookbooks for sample code for a range of use-cases. Integrate needed API interactions into your application.

The “single-serving-cohere-all” container is deployed with an accompanying service that listens on TCP port 8080 and forwards incoming traffic to port 8080 of the deployed “single-serving-cohere-all” pod.

Commonly used open source libraries for generative AI applications, such as LangChain and LangGraph, also have integrations and abstractions for Cohere models.

Implement appropriate retry logic, rate limiting, and output validation, especially for workloads processing unstructured or dynamic input.

Helpful resources

Questions?

If you’re unsure about your Cohere integration or deployment impact level, contact your Second Front implementation engineer.