GoodData Cloud is an agentic analytics platform for building, deploying, and scaling AI agents grounded in governed business data. Unlike general-purpose AI tools, GoodData gives agents access to a trusted semantic layer of defined metrics, business logic, and data rules, so every output is accurate, traceable, and enterprise-ready. With the Agent Builder, teams can launch conversational assistants, autonomous analytical agents, and automated workflows without starting from scratch. The MCP Server extends GoodData's analytics capabilities to any external AI, plugging governed metrics and insights directly into tools like Claude, ChatGPT, or your own custom agents. GoodData supports the full agentic stack: bring your own LLM, define agents as code, govern what data and actions each agent can access, and monitor behavior across your entire AI deployment, all in one place.
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.
Highlights
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.
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
This listing bills through a single Capacity Block dimension. You buy capacity units that combine three resource factors: compute, workspaces, and storage. A workspace is an isolated container that holds a user group's data, model, metrics, and dashboards. Your total depends on how much compute you run, how many workspaces you need, and how much data you store. Pricing is set through a Private Offer arranged directly with the vendor, so quantities and terms are negotiated to fit your capacity needs rather than selected from fixed public tiers.
Top-of-mind questions for buyers
What counts as one workspace for capacity billing?
A workspace is an isolated container holding one user group's data, data model, metrics, calculations, and dashboards. Usually you set up a separate workspace per customer, department, or partner. Users in one workspace cannot see another workspace's data unless you grant access. Each workspace you create adds to your capacity usage.
Which factor drives most of my capacity cost — compute, workspaces, or storage?
All three factors combine into your capacity block. Compute reflects how much processing your queries and dashboards run. Workspaces reflect how many isolated tenant containers you create. Storage reflects how much data you hold. Heavy query and dashboard use raises compute demand, while many tenants raise the workspace count. The vendor sizes your block to your mix.
Are AI queries limited or metered under this capacity model?
By default, each user can run up to 30 AI queries per day under the Fair Usage Policy, and you can configure these limits. Usage is measured by the number of AI queries users raise. You can optionally purchase additional buckets of queries if your teams need more.
www.gooddata.ai
Helpful?
Vendor refund policy
GoodData does not permit refunds.
How can we make this page better?
Tell us how we can improve this page, or report an issue with this product.
Give us feedbackReport a problem with this product or seller
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA).
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Support
Vendor support
GoodData takes a layered approach to enterprise support, combining self-service resources, technical assistance, and hands-on professional services to ensure customers get value from the platform quickly and continuously. Every GoodData Cloud customer has access to a comprehensive support portal at support.gooddata.com, where they can submit tickets, track issues, and browse service announcements. For teams that prefer peer learning and community-driven help, GoodData maintains an active community Slack where customers can connect with other users and GoodData's own team directly. For self-paced learning, GoodData University offers structured training programs covering everything from platform fundamentals to advanced agentic configurations. Customers can also pursue formal recognition through the GoodData Certification Program, which validates expertise on the platform. Beyond standard support, GoodData offers Professional Services for organizations that want hands-on expert involvement. Forward-deployed engineers work alongside customer teams in an agile delivery model, supporting use cases that range from initial implementation and data modeling to custom agent development and ongoing roadmapping. Engagements can be structured as long-term in-team collaborations or as focused project-based work tied to specific outcomes. Full documentation for all features, APIs, and SDKs is available at gooddata.ai/docs/cloud, and customers can reach GoodData directly through the contact page.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Be the first to review this product. We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.