Anaconda AI Catalyst launches, powered by AWS, with models to speed development while delivering cost efficiencies and policy controls for building and managing AI systems
Anaconda Inc., the leader in advancing AI with open source, launched AI Catalyst, an enterprise AI development suite within the Anaconda Platform, powered by AWS, that will deliver an end-to-end ecosystem for building, deploying, and governing AI applications. Available now for AWS customers, enterprises benefit from transparent, governed, and optimized AI development at scale. AI Catalyst launches with a curated model catalog of secure, vetted AI models to enable organizations to discover, select, test, compare, and run models in their own environment, reducing unknown risks and improving governance and cost efficiency.
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With nearly all modern applications now built on open source, there is a growing gap between open-source innovation and enterprise security requirements. While developers want to build quickly, lengthy security, compliance, and legal reviews delay adoption. Even when those reviews are completed, security vulnerabilities, licensing issues, and performance problems inevitably emerge. As a result, development teams are at the mercy of security and compliance reviews and end up sinking time and resources into infrastructure setup and model optimization instead of building applications, stretching AI deployment timelines to weeks or months.
Anaconda AI Catalyst unlocks enterprise AI development, starting with models – curated, open-source models that come with a robust AI Bill of Materials and comprehensive risk profiles for transparency and audit-ready oversight. Its controlled inference stack reduces third-party vulnerabilities, while dynamic evaluations identify model-specific risks like prompt injection attacks before they impact production. Model functionality supports local, cloud development or production integrations that can run on CPU or GPU, ensuring developers have flexibility when it comes to model deployment within their organization’s secure infrastructure. Optimized and benchmarked for enterprise use cases, these models save weeks of manual research and testing, enabling teams to move faster from prototype to production. Together, these capabilities allow enterprises to innovate with confidence, control, and speed.
“Enterprises don’t want just AI models – they want an end-to-end platform where they can confidently build, deploy, and govern AI applications,” said Laura Sellers, Chief Product and Technology Officer at Anaconda. “With AI Catalyst, we’re committed to setting a new standard for enterprise AI development by bringing Anaconda’s curated, secure open-source ecosystem together with the scale and governance of our customer’s own Amazon VPC. This is designed to eliminate weeks of manual model evaluation and dependency management, helping to ensure consistent security from experimentation through production and empowering teams to turn open source models into breakthrough AI applications and business outcomes faster.”
With AI Catalyst, enterprises can take advantage of:
- Security from the bottom up – Extensive risk profiling capabilities and a secure inference server built using Anaconda’s package distribution provides greater control over the inference stack from quantization to runtime. This significantly reduces third-party security vulnerabilities and supports verified model execution.
- Customized governance – Custom policy controls allow organizations to establish governance rules based on individual model criteria, from security vulnerabilities and licensing terms to compute requirements and performance benchmarks. This ensures safeguards for projects to move efficiently without creating bottlenecks for practitioners.
- AI transparency with comprehensive AI Bill of Materials – Ensures enterprises have a clear picture and risk profile for all of the top open source LLMs on the market.
- Better performance at lower cost – Quantized models reduce compute resources while maintaining exceptional solution performance, enabling deployment on GPUs or CPUs depending on an organization’s needs.
- Flexible deployment – Models can be deployed across local development environments via command line interface (CLI) and Anaconda Desktop, or in cloud environments, giving teams multiple access methods to fit their workflows.
Along with AI Catalyst, Anaconda announced two additional new capabilities within its Platform. Offered through Amazon Virtual Private Cloud, customers can now choose a self-hosted cloud implementation of the Anaconda Platform, which enables enterprises to operate a complete platform within their own secure, controlled environments on Amazon Web Services (AWS), preserving existing security postures and network boundaries. Anaconda also announced unified search functionality and expanded model access capabilities. The unified search provides users with a fast, intuitive discovery experience across all Anaconda products, eliminating context switching so developers can spend more time building and less time searching for resources. Additionally, users can now access AI Catalyst models through multiple deployment options: deploy to AWS cloud for GPU-enabled autoscaling endpoints, access via a CLI, or download locally with Anaconda Desktop for on-device inference, giving teams the flexibility to run models wherever their workflows demand.
“At Sutherland Global, we’re transforming how we serve clients across financial services, healthcare, and telecommunications by integrating AI into our operations,” said Dr. Iman Karimi, Global Head of Data Science at Sutherland Global. “Anaconda gives our data teams a trusted foundation to develop and deploy AI responsibly. We get the speed and flexibility to innovate while maintaining the enterprise security our business demands, allowing us to move confidently from experimentation to production.”
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