GoodData Cloud

As described on AWS Marketplace.

GoodData Cloud is an agentic analytics platform built for organizations that need AI they can actually trust. Where most AI tools operate on raw, unstructured data and produce outputs that are difficult to verify, GoodData grounds every agent and assistant in a governed semantic layer of defined metrics, business logic, and data rules. The result is AI that doesn't just generate answers, but generates the right answers, ones that are accurate, consistent, and traceable back to a source your business already trusts. At the core of GoodData is Context Management, which gives AI the business knowledge it needs to operate reliably. Teams define their metrics, KPIs, and business rules once, and every agent, dashboard, and API works from the same definitions. This eliminates the inconsistency that plagues most enterprise AI deployments, where different tools interpret the same data differently and produce conflicting outputs. With GoodData, everyone and everything works from a single version of the truth. Building agents is straightforward with the Agent Builder. Teams can create agents tailored to specific workflows and use cases, setting roles, instructions, and behavioral guardrails to ensure agents operate within appropriate boundaries. Agents can be configured, tested, and deployed through a UI or through code, giving both business users and developers the flexibility they need. Prebuilt agents are also available for common analytics tasks like summarization, anomaly detection, forecasting, and recommendations, so teams can go from zero to productive without building from scratch. For developers and data engineers, GoodData offers a full suite of tools designed around software engineering best practices. Agents can be defined as code in JSON or YAML, versioned in Git, and updated through a CLI or API. Changes can be reviewed and rolled back just like any software deployment, and automated checks catch errors before they reach production. This makes it possible to manage AI agents with the same rigor and discipline as any other part of a modern data stack. One of GoodData's most significant capabilities is its MCP Server, which allows any external AI tool to connect directly to GoodData's governed analytics layer. Through the Model Context Protocol, tools like Claude, ChatGPT, Gemini, and custom-built agents can execute analytics end-to-end, querying governed metrics, building visualizations, and triggering automated workflows, all while inheriting the same access controls and business rules that apply to human users. This means organizations can extend trusted analytics to any AI in their ecosystem without rebuilding governance from scratch. GoodData also supports full LLM flexibility. Organizations are not locked into a single model provider. Whether a team uses OpenAI, Anthropic, Google Gemini, Meta's Llama, DeepSeek, or a proprietary model, GoodData integrates with the LLM of their choice and routes tasks to the model best suited for each job. This gives organizations control over cost, performance, and data residency as the AI model landscape continues to evolve. AI Automation capabilities allow teams to go beyond answering questions and into fully automated decision-making. Agents can be orchestrated to handle multi-step analytics processes, running continuously in the background to monitor data, surface anomalies, generate reports, and trigger downstream actions. Operational analytics workflows that previously required manual intervention can be handed off entirely to agents, freeing teams to focus on higher-value work. Governance and observability are built into every layer of the platform. Administrators can control what data, knowledge, tools, and policies are available to each AI experience. Every agent action is logged, and outputs can be traced back to the specific sources, rules, and inputs that shaped them. This level of transparency is critical for organizations in regulated industries or those that need to audit AI behavior for compliance purposes. GoodData Cloud is available as a fully managed SaaS solution deployed on AWS, Azure, or across multiple regions, and as a self-hosted option for organizations with strict data residency or security requirements. The platform is certified under ISO 27001, SOC 2 Type II, HIPAA, and EU GDPR, giving enterprise customers confidence that their data and AI operations meet the highest security and compliance standards. For organizations ready to move beyond static dashboards and one-off AI experiments, GoodData Cloud provides the infrastructure to build, govern, and scale agentic AI that actually works in production.

Highlighted by the publisher on AWS Marketplace.

Governed AI by design. Most AI tools treat governance as an afterthought. GoodData builds it in from the start through a semantic layer where business metrics, KPIs, and data rules are defined once and inherited automatically by every agent, assistant, and API. Every output is traceable to a trusted source, so teams spend less time questioning answers and more time acting on them.

Works with any AI or LLM. GoodData is not locked to a single model or ecosystem. Through its MCP Server, any external AI tool can connect directly to GoodData's governed analytics layer and execute end-to-end analytics while inheriting the same access controls that apply to human users. Teams get full flexibility to use the models and tools that fit their stack.

Agents managed like software. GoodData treats AI agents with the same rigor as any production system. Teams can define agents as code, version them in Git, test against real data, and deploy through CI/CD pipelines with automated error checking. This operational maturity sets GoodData apart from agentic analytics platforms that stop at the prototype stage.

As stated by the publisher on AWS Marketplace.

GoodData does not permit refunds.