Anthropic has introduced the Claude Partner Network, a new initiative designed to support organizations helping enterprises deploy its Claude AI models. The company said it will commit an initial $100 million to the program in 2026, funding partner training, technical assistance, and joint market development.

The program targets consulting firms, professional services companies, and AI specialists that guide enterprises through the adoption of AI systems. These partners typically help businesses identify high-value use cases for AI and implement production-ready applications within complex corporate environments.

Anthropic said the initiative reflects growing enterprise demand for structured support in adopting generative AI tools.

“Anthropic is the most committed AI company in the world to the partner ecosystem—and we’re putting $100 million behind that this year to prove it,” said Steve Corfield, Anthropic’s head of global business development and partnerships. “Our partners are instrumental in getting enterprises from proof of concept to production with Claude.”

Claude is currently available across all three major cloud providers: Amazon Web Services, Google Cloud, and Microsoft Azure, allowing partners to deploy the model across multiple enterprise environments.

Training, Certification, and Implementation Support

The Claude Partner Network will provide a range of resources designed to help organizations build services around Anthropic’s AI platform.

Partners will receive access to technical training through Anthropic Academy, along with dedicated engineering support for enterprise deployments. The company plans to expand its partner-facing team significantly, providing applied AI engineers and technical architects to assist with complex customer implementations.

Anthropic is also introducing its first technical certification, called Claude Certified Architect, Foundations. The certification is designed for solution architects developing production applications using Claude models.

Additional certifications for developers, architects, and sales professionals are expected to launch later this year. Partners joining the network will receive priority access to these programs.

In addition, Anthropic has released a code modernization starter kit intended to help enterprises migrate legacy software systems using Claude’s agentic coding capabilities. The company said modernizing older codebases and reducing technical debt is one of the most common enterprise AI use cases.

Growing Demand for Enterprise AI Deployment

Anthropic said the partner network will help enterprises move from experimental AI deployments to large-scale production systems. Many organizations have begun testing AI models but require technical and operational guidance to integrate them into existing workflows.

Members of the program will gain access to Anthropic’s partner portal, which includes sales playbooks, training materials, and co-marketing resources. Qualified partners will also appear in a public services directory where enterprises can find firms experienced in implementing Claude-based solutions.

The company said the Claude Partner Network is open to any organization involved in deploying Claude AI systems, and membership will be free of charge. Applications for the program opened today.

The partner program also comes as Anthropic expands its broader research and policy efforts around advanced AI. The company recently launched the Anthropic Institute, an initiative that brings together engineers, economists, and social scientists to study the societal, economic, and governance implications of increasingly powerful AI systems.

Amazon and Cerebras Systems have announced a partnership to integrate their computing chips into a new artificial intelligence inference service hosted on Amazon Web Services (AWS). The service aims to accelerate AI workloads such as chatbots, coding assistants, and other generative AI applications.

Under the agreement, Cerebras processors will be installed inside AWS data centers and connected with Amazon’s Trainium3 custom AI chips through proprietary networking technology. The companies did not disclose the financial terms of the partnership.

Cerebras, valued at roughly $23.1 billion, has developed a unique AI chip architecture designed to compete with Nvidia’s processors. Unlike many AI accelerators, Cerebras chips do not rely on high-bandwidth memory, a costly component used in Nvidia’s flagship GPUs.

Cerebras Chief Executive Andrew Feldman said the partnership could expand access to the company’s technology through AWS’s large customer base.

“Every customer large or small is on AWS, from individual developers to the largest banks in the world,” Feldman said. “This will make it easy as a click to get on Cerebras.”

Divide-and-Conquer Inference Architecture

The joint service will focus on inference, the stage where trained AI models generate responses to user prompts. Instead of running the entire inference workload on a single chip architecture, Amazon and Cerebras plan to split the process between two specialized systems.

Amazon’s Trainium3 chips will handle the “prefill” stage, which converts user input into tokens that AI systems process. Cerebras processors will manage the “decode” stage, where the model generates output tokens to produce responses.

According to Feldman, this “divide and conquer” approach is intended to improve speed and efficiency for AI inference workloads.

The strategy reflects a broader industry trend toward specialized AI infrastructure where different processors handle distinct parts of AI pipelines to improve performance and cost efficiency.

Competition in AI Infrastructure

The partnership comes amid intensifying competition in AI hardware and infrastructure. Nvidia currently dominates the AI accelerator market, but several companies are attempting to develop alternatives to its GPU-based architecture.

Analysts expect Nvidia to unveil a similar multi-chip strategy soon. Reports indicate the company may combine its GPUs with chips from startup Groq, which Nvidia acquired in a deal valued at $17 billion late last year.

Amazon said its service, expected to launch in the second half of the year, could offer better price-performance compared with GPU-based solutions.

The company added that its Trainium chip roadmap, including the upcoming Trainium4 processor, is designed to provide an alternative to third-party AI hardware by optimizing cost and efficiency for AWS customers.

Palantir Technologies has announced a new sovereign AI operating system reference architecture developed in partnership with Nvidia. The system is designed to provide organizations with a turnkey AI data center environment that integrates hardware infrastructure, data management, and AI application deployment.

The platform, called the Palantir AI OS Reference Architecture (AIOS-RA), combines Nvidia’s AI hardware and software stack with Palantir’s enterprise data and AI platforms. The companies said the architecture delivers a production-ready system capable of running advanced AI workloads while maintaining strict data sovereignty and operational control.

The reference architecture is built on Nvidia’s Enterprise Reference Architectures and is optimized to run Palantir’s full software ecosystem. This includes the company’s Artificial Intelligence Platform (AIP), Foundry data platform, Apollo deployment system, Rubix security framework, and AIP Hub.

Full-Stack AI Data Center Design

The system integrates Nvidia’s Blackwell Ultra AI infrastructure, including servers equipped with eight Blackwell Ultra GPUs and Spectrum-X Ethernet networking designed for AI training and inference workloads.

Alongside the hardware layer, the architecture includes a hardened Kubernetes-based compute environment for running Foundry services such as data cataloging, pipeline development, and analytics workloads.

Palantir’s Rubix platform provides a zero-trust security model for Kubernetes infrastructure, while the Apollo system automates deployment and lifecycle management across distributed environments.

The architecture also incorporates Nvidia’s AI Enterprise software stack, CUDA-X libraries, Nemotron open AI models, and Magnum IO data acceleration tools to improve training and inference performance.

Supporting Sovereign and Edge AI Deployments

The companies said the architecture is particularly suited for organizations operating in sensitive environments where data control and regulatory compliance are critical. These include government agencies, defense organizations, and industries with strict data residency requirements.

By enabling on-premise and edge deployments, the sovereign AI architecture allows organizations to run AI workloads locally without relying on public cloud infrastructure. This approach can also reduce latency for mission-critical applications and enable AI deployments in geographically distributed environments.

“From our first deployment with the United States government and in every deployment since, our software has had to meet the moment in the most complex and sensitive environments where customers must maintain control,” said Palantir Chief Architect Akshay Krishnaswamy.

Nvidia’s enterprise AI platforms team said the collaboration reflects growing demand for integrated AI infrastructure solutions.

“AI is redefining the infrastructure stack — demanding, latency-sensitive and data-sovereign environments require a full-stack architecture,” said Nvidia Vice President Justin Boitano.

The companies said the joint architecture is designed to help organizations convert large volumes of operational data into actionable intelligence while maintaining control over infrastructure, models, and applications.