Buying snapshot
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| Decision point | What GoodData states |
|---|---|
Deployment | GoodData Cloud is a SaaS product operated by GoodData on AWS and Azure. Self-hosted uses the same codebase; on Enterprise it is on demand, as are a dedicated cluster and multi-region deployment. |
Data centers | US, EU and Australia; Canada, Singapore, India, Japan and Middle East on demand. |
Contract term | Plans are annual. You can upgrade at any time, but you cannot downgrade before the contract runs out. |
Data connectivity | Amazon Redshift, Azure SQL, Databricks, Google BigQuery, Greenplum, Microsoft SQL Server, PostgreSQL, Snowflake, Synapse SQL, Vertica and more, with data blending across sources. FlexConnect functionality to build self-service connectors is on the Enterprise list. |
Support | Technical support is provided 24/7 worldwide in English. Professional lists standard support; Enterprise customers get unlimited technical support and a 99.5% SLA. Community support runs through the GoodData community Slack. |
Compliance | Professional lists SOC 2 certification and GDPR and ISO 27001 compliance. Enterprise adds HIPAA (on demand) and FedRAMP (on demand). |
Developer tooling | Public Python packages for GoodData Cloud: an API client, gooddata-sdk, gooddata-pandas, gooddata-pipelines and gooddata-flexconnect. The gooddata-fdw Foreign Data Wrapper is deprecated. |
Our take
Shortlist GoodData if you deliver branded analytics, and now AI assistants or agents, to many separate customers and can map each customer to a workspace. If you only need internal dashboards for one team, the workspace model and sales-led buying add structure you may not use.
Evaluate GoodData
Comparisons
Where it stands out
Multi-tenancy is built in. Each workspace holds a user group's data, users, data model, metrics and dashboards, and users in one workspace cannot see another's data unless you allow it. In a workspace hierarchy, child workspaces inherit the parent's data model, metrics and data sources in read-only mode, so a new entity in a parent workspace becomes available to its child workspaces.
Embedding and white-labeling. Professional includes whitelabeling, iFrame, Web Components and APIs for embedding, a React SDK for custom UIs, custom themes and branding per customer, and a custom domain URL.
One semantic layer for people and agents. GoodData says agents, dashboards and APIs work from the same metrics, definitions and rules, and that you can trace which sources and rules shaped each AI answer.
Agents as code. Agents can be defined in JSON or YAML, created via CLI or API, tested with real data and versioned in Git with diffs, reviews and rollbacks. Custom agents and Agent Builder are on the Enterprise list.
Data stays in place. GoodData describes Apache Iceberg zero-copy, which keeps one shared copy of your data available to its platform and other engines without moving it.
Product screenshot
Trade-offs to weigh
No self-serve price. Professional shows a Contact us button and Enterprise shows custom pricing, so a quote needs a sales conversation. Count the workspaces you expect to need before that call.
AI queries are metered. By default each user can run up to 30 AI queries per day under the Fair Usage Policy; GoodData lets you configure the limits. Your query allowance depends on your tier, and extra buckets of queries can be purchased.
Key controls sit on Enterprise. Agent Builder and custom agents, BYOLLM, the MCP Server with 30+ Tools, audit log, SAML and multiple OIDC, and self-hosted deployment are on the Enterprise list. A regulated or self-hosting buyer should expect Enterprise terms.
One environment on Professional. Professional includes 1 environment and Enterprise 3, with CI/CD for data product management on Enterprise. Teams that want separate dev, test and production setups should plan for that.
Pricing and plan conditions
The billing boundary on Professional is the workspace, not the user: adding users or data does not change the plan basis, but each new workspace adds to the count, and GoodData says a separate workspace is usually used for each customer, department or partner. GoodData offers a free trial from its homepage.
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| Plan | Published price | What it adds |
|---|---|---|
Professional (for small & medium companies) | Per-workspace pricing: platform fee + number of workspaces (contact us) | Unlimited users & data; analytics and BI features; full embedding; multi-tenancy and branding; AI and Agents, Embedded AI and IDE Extension; fully managed SaaS on AWS or Azure with SSO; standard support; cache results and FlexQuery storage (limited). |
Enterprise (for large companies) | Custom pricing (custom use-case-based pricing) | Everything in Professional, plus: AI Assistant with 20+ Analytics Skills, Dashboard and Summarization Copilots, Custom Agents, Agent Builder, AI Memory, AI Knowledge, A2A Protocol; dedicated customer success manager, onboarding support, 24/7 prioritized SLAs; WCAG AA accessibility; cache results and FlexQuery storage (talk to us). |
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
280 GitHub commits 90d36 GitHub stars0 vulnerabilities across 1 packageOpenSSF score 3.9/10
See all signals from 6 sources
Three-month score against stable baseline terms—not search volume or adoption.
PyPI · gooddata-sdk@1.79.1
Alternatives to GoodData
The reviewed substitutes for GoodData among the BI platforms, and what would make each one the better answer.
Direct alternatives
Reviewed substitutes: products bought for the same job, where a team picks one.
- Amazon QuickSight
- GoodData and Amazon QuickSight both put dashboards and self-service analysis in front of business users, inside a company or inside a product. GoodData is API-first, with a governed semantic layer and white-label dashboards for SaaS companies serving their own customers. QuickSight is the managed BI service of AWS, with natural-language questions and close ties to AWS data sources.Applies to: Choosing between these two for the enterprise bi decision.
- Looker
- Looker and GoodData both centre on a governed semantic layer from which a company builds dashboards and self-service exploration. Looker defines that layer in LookML and runs on Google Cloud. GoodData is API-first and especially aimed at SaaS companies that deliver white-label analytics to their own customers.Applies to: Choosing the BI platform a team will license for dashboards and self-service exploration.
- Tableau
- Tableau and GoodData are platforms for dashboards and self-service exploration of warehouse data. Tableau puts visual analytics first, giving authors fine control over interactive charts. GoodData puts a governed semantic layer and APIs first, which suits companies that build analytics into their own product.Applies to: Choosing the BI platform a team will license for dashboards and self-service exploration.
- Spotfire
- GoodData and Spotfire are analytics platforms that deliver interactive dashboards and AI-driven insight to business users. GoodData is API-first and built around a governed semantic layer, which suits teams delivering analytics inside their own software products. Spotfire focuses on visual data discovery with predictive and geospatial analysis for technical users.Applies to: Choosing between these two for the enterprise bi decision.
- Domo
- Domo and GoodData both deliver dashboards and self-service analytics to business users from a cloud platform. Domo is an all-in-one SaaS product that also connects, moves and prepares data and builds apps for internal teams. GoodData is API-first and centred on a governed semantic layer, a strong fit for SaaS companies offering white-label analytics to their customers.Applies to: Choosing between these two for the enterprise bi decision.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- Sisense
- Organizations choosing between Sisense and GoodData will often compare their embedding flexibility, multi-tenancy support, and deployment options, since Sisense also supports cloud, hybrid, and on-premises environments. **Sisense** publishes tiered pricing with a Starter plan and a Pro plan for larger data volumes, plus custom Enterprise pricing for organizations with advanced requirements.
See detailed alternatives analysis
GoodData describes its platform as a composable analytics platform built around governed semantics. Its site says business logic can be defined once in a governed semantic layer so that dashboards, agents, and embedded workflows use reliable insights. The platform also presents open architecture as a way to integrate with a cloud ecosystem.
GoodData's agentic-analytics material describes shared metrics, definitions, and rules across agents, dashboards, and APIs. It also describes controls for the data, knowledge, tools, and policies available to an AI experience, plus output tracing that identifies the sources, rules, and inputs that shaped an answer, recommendation, or action. The sections below focus on directly documented capabilities and disclosed commercial terms; where a vendor page does not disclose a price, the missing plan, term, or currency information is stated explicitly.
Top Alternatives Overview
GoodData documents a Professional plan for small and medium companies with per-workspace pricing: a platform fee plus a number of workspaces. The plan lists unlimited users and data, analytics and BI capabilities, full embedding functionality, multi-tenancy, branding, AI experiences and governance, and React and Python SDKs for generative AI. Its connectivity list names Amazon Redshift, Azure SQL, Databricks, Google BigQuery, Microsoft SQL Server, PostgreSQL, Snowflake, Synapse SQL, and Vertica, among others.
Metabase documents an Open Source plan that is free, with 20+ supported data sources, unlimited questions and dashboards, SQL querying or click-based exploration, and unlimited users. Its pricing page also describes a Starter plan at $100 per month, or $90 per month on annual billing, with the first 5 users included and an additional $6 per user per month. Metabase's product material describes a visual query builder, a SQL editor, drill-through, and collections with verified items.
Amazon Quick Sight documents per-user and capacity pricing choices. Its Reader pricing starts at $3 per month per Reader, while the listed Author price is $24 per user per month. The capacity model lets customers buy Reader sessions and Amazon Q question capacities in bulk. Quick Sight's product page describes interactive analytics embedded in applications and business workflows, built-in agents for research and automation, and 40+ application integrations.
Microsoft Power BI lists a free account, Power BI Pro at $14.00 per user per month paid yearly, and Power BI Premium Per User at $24.00 per user per month paid yearly. Microsoft describes Power BI as a unified analytics platform with governed data, trusted metrics, interactive insights, semantic data models, and Copilot in Microsoft Fabric.
ThoughtSpot lists a Developer offering for individuals and small developer teams, starting as low as $25 per user per month billed annually, for 5 to 50 users and up to 25M rows of data. Its page also lists a starting usage price of $0.10 per credit and describes dashboards, API and SDK access, natural-language search, and data exploration.
Sisense documents iframe and Compose SDK embedding, more than 400 connectors, and live warehouse connections. Its enterprise plan is described as built to specifications; it lists SaaS, customer-cloud deployment on AWS, Azure, or GCP, and on-prem deployment. Looker documents a LookML-based semantic layer that translates complex SQL into governed business metrics and supports conversational analytics, dashboard agents, MCP workflows, and custom applications.
Architecture and Approach Comparison
GoodData's documented approach centers on governed semantics and product integration. Its platform page says that business logic can be defined once in a governed semantic layer for dashboards, agents, and embedded workflows. Its agentic-analytics page adds that agents, dashboards, and APIs can work from the same metrics, definitions, and rules. Developers can create, customize, test, and integrate AI agents in products and workflows; the page lists JSON or YAML agent definitions, CLI or API creation and updates, developer SDKs for custom skills, API execution, and testing with real data before deployment.
Looker also documents a semantic-layer approach, specifically LookML. Google Cloud says Looker translates complex SQL into governed business metrics once in LookML and uses that context for human analysts, AI agents, dashboards, Model Context Protocol workflows, and custom applications. Looker's SQL-generation documentation further explains that generated queries are complete SQL commands and that SQL Runner can investigate those queries. Data modelers can provide SQL blocks for custom clauses and derived values.
Sisense documents an API-first composability model with Compose SDK. Its platform material says developers can use APIs, SDKs, embed paths, and multi-tenant architecture, while its repository describes Compose SDK as client-side libraries and components for query composition, visualization, dashboards, filters, and charts. The repository names JavaScript/TypeScript and React, Angular, and Vue support; it also states that Compose SDK requires a Sisense license and access to a Sisense instance, and supports Sisense L2022.10 or newer. Sisense's website separately describes tenant-safe access and row-level security at the query layer.
ThoughtSpot documents governed analytics across AI answers, Liveboards, and embedded applications. Its product page describes reusable logical data models, cloud-data insights, low-code embedded experiences, and governance controls. Its Visual Embed SDK repository documents NPM installation, TypeScript, ESM and UMD modules, React components, and embeds for Liveboards, visualizations, full applications, and search.
Metabase documents both a visual query builder and a SQL editor. Its site says users can build queries without writing SQL or use the SQL editor for more complex analysis; it also documents drill-through menus and verified items. Amazon Quick Sight supports a dataset choice between SPICE and direct query, except when a dataset contains uploaded files. AWS describes SPICE as an in-memory engine; in Enterprise edition, SPICE data is encrypted at rest. Tableau documents live connections and local compressed data extracts, while Power BI documents Import models, Power Query M, DAX, DirectQuery limitations, and dashboard-tile updates as frequently as every 15 minutes for DirectQuery models.
Pricing Comparison
GoodData's pricing page identifies Professional as a per-workspace offering for small and medium companies. The stated basis is a platform fee plus a number of workspaces, with unlimited users and data. The page presents a “Contact us” action for that plan but does not display a public currency amount, platform-fee amount, workspace price, or a complete public breakdown of what determines the total. Its FAQ states that GoodData plans are annual. A buyer should confirm the platform fee, workspace quantity, included embedding and AI capabilities, and any deployment or support terms.
Amazon Quick Sight lists two pricing models: per-user pricing and capacity pricing. The page states that Reader pricing starts at $3 per month per Reader and lists Authors at $24 per user per month. Its capacity model covers Reader sessions and Amazon Q question capacities bought in bulk. The page also includes a “Get started for free” action. Buyers comparing the two models should establish the required Author roles, Reader population, session volume, and Amazon Q question capacity rather than treating either model as a general cost conclusion.
Metabase lists an Open Source plan as Free. Its Starter plan is listed at $100 per month or $1,080 per year, with an annual-billing display of $90 per month; the first 5 users are included, and the additional-user prices shown are $6 per user per month or $65 per user per year. The same page lists Pro at $575 per month, $6,210 per year, and $517.50 per month on annual billing. Metabase says both internal analytics developers and users accessing embeds through a product count as users, so the applicable user population should be confirmed.
Microsoft lists Power BI Pro at $14.00 per user per month paid yearly and Power BI Premium Per User at $24.00 per user per month paid yearly. It lists Power BI Embedded as Variable and pairs that entry with “See pricing” and “Contact sales.” Buyers should distinguish the paid-yearly per-user licenses from the variable Embedded offering.
ThoughtSpot lists Developer starting as low as $25 per user per month billed annually, for 5 to 50 users and up to 25M rows of data. It also lists a starting usage price of $0.10 per credit and says flexible pricing models align with use case and go-to-market requirements. The page states that ThoughtSpot does not meter or charge for LLM tokens, while a customer's own LLM provider may charge fees. Looker's pricing page says individual-user licensing costs vary by user type and permissions and that instance and named-user charges are billed to the attached billing account. It invites organizations to request a custom quote rather than publishing amounts for platform and named-user licensing. Separately, the page describes promotional access to Conversational Analytics without quota limits or overage fees, within fair usage limits. Google Cloud will provide at least 90 days' advance notice before billing and quota enforcement begin. Published future overages beyond the included monthly allocation are $3.00 per 1M input data tokens and $20.00 per 1M output data tokens. Sisense describes a self-serve offering and an enterprise plan built to specifications, but its cited pricing content does not disclose a public amount or billing term.
When to Consider Switching
A platform evaluation can start with the documented architecture required by the product. GoodData documents governed business logic shared by dashboards, agents, and embedded workflows, plus workspace-oriented pricing and full embedding functionality. Its Professional plan also lists multi-tenancy and branding. A team that needs those specific capabilities can use them as requirements when assessing another platform.
For code-driven Sisense implementations, Compose SDK documents query composition, charts, visualizations, dashboards, TypeScript, and React, Angular, and Vue support. Sisense also documents iframe embedding and more than 400 connectors. For deployments subject to a particular hosting requirement, its enterprise description explicitly names SaaS, AWS, Azure, GCP, and on-prem deployment.
For Looker-oriented data models, the relevant documented concept is LookML: Google Cloud describes it as the place where complex SQL becomes governed business metrics. For a Quick Sight implementation, decide whether the relevant dataset should use SPICE or direct query and account for the specific exception for datasets containing uploaded files. For a Power BI implementation, the documented model modes, Power Query M, DAX, and DirectQuery limitations are relevant technical criteria. For a Tableau workflow, the documented choice is between a live connection and a locally stored compressed extract.
For product integration, ThoughtSpot documents embedded analytics through a low-code platform and Visual Embed SDK components, while Metabase documents embeds, white-labeling, dynamic styling, interactive controls, and multi-tenant data segregation. These are distinct documented product capabilities, not like-for-like guarantees; confirm the API, security, tenancy, deployment, and commercial terms required for the intended implementation.
Migration Considerations
A GoodData implementation can include governed business logic used by dashboards, agents, and embedded workflows. GoodData also documents context controls that define the data, knowledge, tools, and policies available to each AI experience, along with output tracing for sources, rules, and inputs. These documented elements identify the items that need an explicit destination and validation approach in any platform transition.
The destination's implementation model may be materially different. Looker documents governed metrics defined in LookML and SQL blocks supplied by a data modeler for custom SQL clauses. Sisense's Compose SDK documents code-driven query composition, filters, visualizations, and dashboards, and its repository says a Sisense license and instance access are required. ThoughtSpot's Visual Embed SDK documents NPM installation, TypeScript modules, React components, and event listeners. Metabase documents a visual query builder, SQL editor, and drill-through. Each of these descriptions concerns a separate product and should be assessed against the integrations actually in use.
Embedding and access controls also require product-specific review. GoodData's Professional material lists full embedding, multi-tenancy, and branding. Sisense documents row-level security at the query layer and tenant-safe access, while Metabase documents granular permissions and multi-tenant data segregation. ThoughtSpot documents governed metrics and trusted insights for workflows, and Quick Sight documents embedded interactive analytics in applications and business workflows. The cited materials do not establish that these mechanisms are interchangeable.
Data-access behavior must be evaluated separately from the interface layer. Quick Sight documents SPICE and direct-query dataset choices. Tableau distinguishes live data connections from compressed local extracts. Power BI documents Import models, Power Query M, DAX, calculated columns, calculated tables, measures, and restrictions on DirectQuery transformations. GoodData's pricing page names a broad set of connectors, including Amazon Redshift, Azure SQL, Databricks, Google BigQuery, Microsoft SQL Server, PostgreSQL, Snowflake, Synapse SQL, and Vertica. Confirm the required source systems, semantic definitions, embedded experience, security model, licensing, and billing terms before selecting a replacement.