DESIGN.md

Generated as educational analysis. Inferences are hypotheses, not source-code claims.

Observation

• Observed colors: #151532, #fff, #f6fafd, #e5f3ff, rgba(255, 255, 255, 0.85), #151531, rgba(21, 21, 49, 0.90), rgba(0, 0, 0, 0.7), rgba(0, 0, 0, 0.6), #ef4444, rgba(0, 0, 0, 0.08), #9bcdf500
• Observed font families: inherit, inherit !important, PP Neue Montreal Mono, Georgia, sans-serif !important

Inference

• Repeated tokens may indicate a shared design system; external stylesheets were not executed or downloaded.

Recommendation

• Define semantic color and type tokens before copying visual treatments.
• Validate contrast and responsive behavior in the target product context.

Observation

Together AI positions itself as "The AI Native Cloud" and prominently features support for "open-source models." They offer a comprehensive range of inference options (serverless, batch, dedicated) and compute resources (GPU clusters, AI Factory, Sandbox). The company also highlights significant investment in "Foundational research for production AI" and provides extensive developer resources like documentation, demos, and cookbooks.

Inference

Together AI has made a strategic decision to target the "AI Native" developer, providing a full-stack platform that spans from raw GPU compute to managed inference and model shaping. The emphasis on open-source models suggests a commitment to flexibility, avoiding vendor lock-in for users, and potentially leveraging the broader AI community for model development and innovation. The substantial investment in research indicates a long-term strategy to differentiate through performance, efficiency, and cutting-edge capabilities (e.g., FlashAttention). Offering various deployment options (serverless vs. dedicated) reflects a deliberate choice to cater to different operational needs, cost sensitivities, and control requirements of diverse AI workloads. The provision of extensive developer resources underscores a decision to foster a strong developer ecosystem and reduce friction for adoption.

Recommendation

When making strategic product decisions, clearly define the target persona (e.g., "AI Native") and tailor the entire offering, from features to messaging, to meet their specific needs. Prioritize investments in areas that differentiate the platform, such as performance optimization, unique research contributions, or a strong open-source ecosystem, especially in a competitive market. Offer flexible deployment and pricing models to address a wider range of customer requirements and use cases.

Uncertainty: Low, as these are direct interpretations of the company's stated mission, offerings, and publicly highlighted investments.

Observation

Together AI provides "Serverless InferenceHigh-performance inference as APIs," "Batch InferenceInference for batch workloads," "Dedicated Model InferenceInference on custom hardware," "Dedicated Container InferenceInference for custom models," "GPU ClustersReliable GPU clusters at scale," "AI FactoryCustom infrastructure at frontier scale," "SandboxBuild development environments for AI," "Managed StorageStore model weights & data securely," "Fine-TuningShape models with your data," and "EvaluationsMeasure model quality." They also offer "Documentation," "Demos," and "Cookbooks" as developer resources.

Inference

A developer can leverage Together AI to deploy and run open-source large language models (LLMs) for various applications, from real-time interactive experiences like chatbots (using Serverless Inference) to large-scale data processing and analysis (using Batch Inference). They can also fine-tune existing open-source models with custom datasets to improve performance or adapt them to specific domains, and then evaluate the quality of these shaped models. For more control over the environment or for highly specialized models, developers can provision dedicated GPU clusters or utilize the "AI Factory" for custom infrastructure. The "Sandbox" provides a safe and isolated environment for experimentation and rapid prototyping. The platform provides the necessary APIs, infrastructure, and supporting documentation to build AI-powered applications without the overhead of managing underlying hardware or complex MLOps pipelines.

Recommendation

To build an AI application using a platform like Together AI, start by clearly identifying the specific AI task (e.g., text generation, summarization, code completion). Then, select the appropriate inference service based on latency, throughput, and cost requirements (e.g., serverless for low-latency, batch for high-throughput). Utilize the provided documentation, demos, and cookbooks to quickly integrate with the platform's APIs. For custom models or specific performance needs, explore the fine-tuning capabilities and dedicated compute options. Always begin with the simplest viable solution and scale up to more complex services as requirements evolve. Leverage managed storage for secure and efficient data handling.

Uncertainty: Low, as the services explicitly describe their purpose and how they can be used to build AI applications.

Observation

The primary navigation is organized into high-level categories: Inference, Compute, Model Shaping, Research, Developers, Company, and Pricing. Each category further branches into specific services or resources. For example, "Inference" includes Serverless, Batch, Dedicated Model, and Dedicated Container Inference, along with a "Model library." "Developers" includes Documentation, Demos, Cookbooks, and specific tools like Playground and Together Chat. Some links, such as those for inference and compute options, are repeated under the "Pricing" section.

Inference

The information architecture is designed to cater to different user personas and their journey through the platform. Developers are guided to technical resources, while business users might explore "Customer stories" or "Startup accelerator." The clear categorization helps users quickly locate relevant services, from core infrastructure (Inference, Compute) to supporting tools (Model Shaping, Developers). The repetition of some links under "Pricing" suggests an emphasis on commercial offerings and ease of access to cost-related information for key services.

Recommendation

To improve discoverability for new users, consider adding a "Getting Started" or "Solutions" section that maps common use cases directly to the relevant services across Inference, Compute, and Model Shaping. This could help users who aren't yet familiar with the specific terminology to understand how the platform addresses their needs. When designing information architecture, ensure that key commercial information is easily accessible from multiple relevant points.

Uncertainty: Low, based on direct observation of navigation links and their hierarchical grouping.

Observation

The website features distinct sections for different service types, such as "Serverless Inference," "GPU Clusters," and "Fine-Tuning." It explicitly lists specific open-source models (e.g., MiniMax M2.5, Qwen3.5-397B, Llama 4 Maverick) and hardware configurations (e.g., GB300, H100). Content components include "Documentation," "Demos," "Cookbooks," and a "Research blog." Interactive elements like "Contact sales" and "Sign in" links are present.

Inference

The platform is built upon modular components, both in terms of its service offerings and its content delivery. Functional components include various inference types, compute resources, and model shaping tools, which users can select and combine. Content components like documentation and blogs are crucial for developer enablement and knowledge transfer. The explicit mention of specific models and hardware suggests a component-based approach to resource allocation and service configuration, allowing users to choose granular elements. The consistent presence of call-to-action components like "Contact sales" indicates a standardized approach to user engagement.

Recommendation

For a more consistent user experience, ensure that common interactive elements, such as "Contact sales" buttons or "Sign in" links, maintain a uniform appearance, placement, and behavior across all pages. This reduces cognitive load, reinforces brand identity, and streamlines user interaction. When building a platform, identify core functional and content components early to promote reusability and consistency.

Uncertainty: Medium, as the

STACK_GUESS.md

Generated as educational analysis. Inferences are hypotheses, not source-code claims.

Observation

• Cloudflare: cloudflare
• Google Analytics: googletagmanager
• Cloudflare: cloudflare
• Google Analytics: googletagmanager
• Cloudflare: cloudflare
• Google Analytics: googletagmanager

Inference

• Technology detection is probabilistic because production builds can remove or disguise signatures.

Recommendation

• Verify stack choices using public engineering sources before adopting them.

Observation

Together AI offers a range of services including "Serverless Inference," "Batch Inference," "Dedicated Model Inference," "Dedicated Container Inference," "GPU Clusters," "AI Factory," "Sandbox," "Managed Storage," "Fine-Tuning," and "Evaluations." The platform also highlights its commitment to "Research" and "Open-source AI," with specific research projects like FlashAttention and ATLAS mentioned.

Inference

The architecture appears to be a multi-tenant, cloud-based platform designed specifically for AI workloads. It likely features a robust control plane responsible for managing user accounts, resource allocation, and orchestrating various services. The data plane would consist of distributed GPU clusters, potentially heterogeneous given the mention of various NVIDIA GPUs, capable of running diverse AI models efficiently. Inference services suggest an API gateway for external access, load balancing for traffic distribution, and auto-scaling mechanisms to handle fluctuating demand. Managed storage implies a secure and scalable data persistence layer for model weights and user data. The "AI Factory" and "Sandbox" point to dedicated or isolated environments for development, experimentation, and large-scale custom deployments. The strong emphasis on "Research" and "Open-source AI" indicates a deep integration with model development, optimization, and deployment pipelines, potentially involving custom kernels and runtime optimizations.

Recommendation

When designing a platform with diverse compute and inference needs, adopt a microservices architecture to allow independent scaling, development, and deployment of each service (e.g., separate services for serverless inference, batch processing, fine-tuning). Implement robust API versioning and clear service contracts to manage complexity and ensure backward compatibility. For high-performance AI workloads, consider a distributed system design that leverages specialized hardware and optimizes data flow, potentially incorporating custom low-level optimizations as demonstrated by Together AI's research projects.

Uncertainty: Medium, as the architecture is inferred from the services offered rather than explicit architectural diagrams. The specifics of internal component interactions, data flow, and underlying infrastructure choices beyond GPUs are unknown.

Observation

The navigation links provided reveal a hierarchical structure of pages. Key top-level categories include Inference, Compute, Model Shaping, Research, Developers, Company, and Pricing. Under these, various sub-pages are listed. For example, Inference includes Serverless Inference, Batch Inference, Dedicated Model Inference, Dedicated Container Inference, and a Model library. Developers includes Documentation, Demos, Cookbooks, Playground, and Together Chat. Some links, like Model Library, appear under multiple top-level categories, suggesting different contexts or entry points.

Inference

The sitemap reveals a deep and broad content structure, reflecting the comprehensive nature of the "AI Native Cloud" platform. The clear categorization under main navigation items indicates a logical organization for users to explore services, resources, and company information. The presence of specific model libraries under both "Inference" and "Model Shaping" suggests distinct user journeys for model exploration (e.g., running pre-trained models versus fine-tuning them). The extensive developer section highlights the platform's focus on empowering builders with tools and knowledge. The inclusion of company-related pages (About, Careers, Blog, Events) indicates a well-rounded corporate presence.

Recommendation

When designing a sitemap for a complex platform, group related services and resources under intuitive top-level categories to enhance navigability. Ensure consistent naming conventions and clear paths to key information like pricing, documentation, and support. Regularly review the sitemap to ensure it accurately reflects current offerings and user journeys, and consider user testing to validate the intuitiveness of the information architecture. For items appearing in multiple contexts, ensure the user understands the specific relevance of that link within its current navigation path.

Uncertainty: Medium for exact URL paths for inferred pages, as only a few full URLs were provided. The structure is based on navigation text and common web patterns.