GoodData customers are evaluating alternatives over MAQL’s learning curve, workspace-per-tenant pricing, quote-only procurement, and, for a narrower group, a MongoDB connector retirement. Strong options include Sisense, ThoughtSpot, Sigma, Looker, Power BI, Tableau, and Knowi, compared here on semantic layer migration, workspace scaling, and pricing transparency.
Quick Summary (TL;DR)
- GoodData customers most often cite three recurring frustrations rather than one dramatic breaking point: MAQL’s proprietary modeling language, workspace-per-tenant pricing, and quote-only procurement.
- MAQL, GoodData’s proprietary modeling language, has no standard SQL replacement, so switching means rebuilding the semantic model in whatever approach the new vendor uses.
- Workspace-per-tenant billing means GoodData’s cost and admin overhead can grow with every new customer, a pattern teams describe as penalizing growth.
- GoodData’s pricing stays custom-quote only across Professional and Enterprise tiers, with quotes reported to take four to eight weeks and the lower tier withholding SLA, HIPAA, and usage analytics.
- For MongoDB and Elasticsearch users specifically, GoodData is also retiring its MongoDB connector by the end of September 2026 with no native replacement planned.
- GoodData is not without strengths: its late-2025 Full-Stack Data Intelligence platform, Understand Labs acquisition, and SOC 2, ISO, and HIPAA certifications reflect real investment.
- Power BI and Tableau are the only two alternatives here with fully published per-user pricing and instant self-service signup.
- Knowi replaces MAQL with a declarative semantic layer and GoodData’s workspace-per-tenant model with a single multi-tenant architecture, and its self-hosted plan keeps AI processing inside the customer’s environment.
Why Teams Are Shortlisting GoodData Alternatives Now
GoodData switchers cite several recurring frustrations rather than one dramatic breaking point. The most common are MAQL’s learning curve, workspace-per-tenant pricing, and quote-only procurement, with a MongoDB connector retirement adding urgency for a specific subset of teams.
MAQL and the Semantic Layer You Have to Rebuild
GoodData’s semantic layer runs on MAQL, a proprietary modeling language with a learning curve users describe as taking months to master. GoodData has not shipped a standard SQL alternative to MAQL, so switching means re-creating your metrics, dimensions, and joins in whatever approach the new tool uses. That migration cost varies widely, from LookML’s code-based model to declarative, GUI-defined semantic layers, and it is often the largest line item in a GoodData migration project.
Workspace-Per-Tenant Pricing at Scale
GoodData’s architecture assigns one workspace per tenant, and its pricing follows that structure. Teams report this can explode costs and admin overhead as a SaaS product adds customers or use cases, since workspace-based billing penalizes growth rather than absorbing it. For SaaS teams serving many end customers, this is frequently cited as the costlier problem to solve, ahead of any single feature gap.
Custom-Quote Procurement
GoodData’s pricing remains sales-led, with no public price list. It offers two tiers, Professional and Enterprise, priced by number of workspaces, and even the lower Professional tier withholds SLA coverage, HIPAA support, and usage analytics. Third-party analyses put quotes at four to eight weeks to finalize, with one estimate placing the entry Professional tier around $1,500 per month, a timeline that frustrates teams trying to move quickly.
The MongoDB Connector Retirement
For a narrower set of GoodData Cloud customers, there is also a hard deadline. GoodData has confirmed its MongoDB connector will be deprecated by the end of September 2026, citing MongoDB’s own retirement of its BI Connector as the reason, and it will not build a native replacement. GoodData.CN, the self-hosted edition, is a separate product, and the notice does not state that the same cutoff applies there, so teams on MongoDB or Elasticsearch should confirm their own roadmap directly with GoodData rather than treat this as a general BI complaint.
What GoodData Still Does Well
GoodData is not without real strengths, and an honest comparison should say so. Its MAQL-based semantic layer, despite the learning curve, gives administrators one centrally governed metric definition rather than the per-report calculations found in tools like Tableau or Power BI, which have no comparable universal semantic layer. Its workspace-per-tenant model, while expensive at scale, gives each tenant a fully isolated environment rather than shared-schema row-level security, an approach some regulated buyers still prefer despite the cost.
GoodData has also invested seriously in AI. In late 2025 it launched a unified Full-Stack Data Intelligence platform combining its AI Lake, AI Hub, and AI Apps, and it acquired the NLP startup Understand Labs to strengthen its natural language capabilities. Its AI and NLQ features support bring-your-own-LLM configurations on top of a governor semantic layer, and GoodData Cloud is SOC 2 Type II, ISO 27001, and HIPAA-certified, credentials worth matching against any replacement.
How to Evaluate a GoodData Replacement
GoodData switchers are pricing a specific migration, not evaluating BI tools from a blank slate, so the criteria below weight what that migration actually costs.
Data and Modeling Fit
- Native MongoDB or Elasticsearch connectivity, and whether the vendor’s connector also depends on the MongoDB BI Connector.
- Proprietary modeling language like MAQL or LookML versus a more standard approach, and how much of your GoodData model must be rebuilt.
- Whether cross-source joins require a warehouse first, or work directly against existing sources.
AI, Deployment, and Tenant Scaling
- Private or on-prem AI versus only a third-party cloud LLM.
- Cloud-only versus self-hosted or VPC deployment, especially for regulated data.
- Tenant isolation model: per-workspace like GoodData, per-instance, or shared multi-tenant.
Commercial Fit
- Published pricing versus custom quote, and how long procurement typically takes.
- Embedding and white-label support if you are serving external customers through an embedded analytics for SaaS product, not just internal analysts.
- Compliance certifications relevant to your industry, such as SOC 2, ISO 27001, and HIPAA.
How the Top GoodData Alternatives Compare
The table below is scoped to what a GoodData switcher cares about most: whether the MongoDB gap is solved, what replaces MAQL, and how tenant costs scale. It reflects documentation reviewed as of August 2026 and should be confirmed against current vendor docs before you commit.
| Tool | NoSQL after the BI Connector retirement | Semantic layer vs. MAQL | Tenant/workspace scaling | AI / NLQ | Pricing and procurement |
|---|---|---|---|---|---|
| Sisense | MongoDB via the MongoDB BI Connector; Elasticsearch via a partner connector. Confirm its own retirement exposure. | Graphical ElastiCube/Live model on standard SQL; no new query language. | Row-level security plus per-tenant ElastiCubes; more flexible than one workspace per customer. | Sisense Intelligence Assistant; bring-your-own cloud LLM today, managed LLM service arriving in 2026. | Not public. Self-serve tier plus custom enterprise; reported median around $21K/year for smaller deployments. |
| ThoughtSpot | No native NoSQL connectors; Mongo/Elasticsearch data must land in a warehouse first. | Search-driven metadata layer, no DSL; translated to SQL under the hood. | “Organizations” multi-tenancy is Enterprise-only; otherwise separate workspaces or instances. | Spotlight/SpotIQ search, cloud-only, no on-prem LLM; MCP server for external models. | Essentials $25/user/mo and Pro $50/user/mo published; higher tiers are custom quote. |
| Sigma | Warehouse-first only; Mongo/Elasticsearch needs an ETL step, same limitation as GoodData. | Spreadsheet-style workbook, no DSL; added dbt Semantic Layer integration. | “Sigma Tenants” (Sept 2025) gives each tenant an isolated environment. | Sigma Assistant/AI Agents via customer-configured external LLMs; no built-in private LLM. | Custom quote only; reported median contract around $61K/year. |
| Looker | SQL/warehouse only; Mongo or Elasticsearch data must land in BigQuery, Snowflake, or similar first. | LookML, a code-based DSL comparable to MAQL; requires developer resources. | Not built for SaaS multi-tenancy; large customers often run separate instances. | Gemini in Looker adds AI/NLQ, cloud-based via Google Gemini; no on-prem LLM. | Custom enterprise quote, historically per developer seat plus query capacity. |
| Power BI | No native MongoDB/Elasticsearch connector; third-party ODBC exists, but data generally needs shaping into tables. | Tabular model with DAX; GUI or code, no shared DSL across reports. | Not built for embedding; Power BI Embedded bills by capacity, workspaces generally single-tenant. | Copilot runs on Azure OpenAI Service; cloud-based, now unlimited on included credits. | Published: Pro ~$10/user/mo, Premium Per User ~$20/user/mo, Premium Capacity in the thousands/mo. Instant signup. |
| Tableau | No native MongoDB/Elasticsearch connector; brought in via extracts or live connections. | No central semantic model; calculations live per workbook unless published as a shared source. | Isolated sites by default; SaaS embedding needs Tableau Embedded plus added licensing. | Tableau Agent (2025) runs on Einstein, requires Tableau Cloud 2025.3+; Trust Layer excludes customer data from training. | Published per-user tiers: Creator/Explorer/Viewer around $70/$40/$15 per month. Free trial available. |
| Knowi | Native MongoDB connector (not the MongoDB BI Connector), plus Elasticsearch and REST APIs, cross-source joins, no ETL. | Declarative semantic layer: metrics, dimensions, and joins defined once, not a proprietary query language. | Purpose-built multi-tenant architecture with per-customer isolation and central management, versus one workspace per tenant. | NLQ and AI agents via Model Context Protocol; self-hosted deployments run Knowi’s own models so data stays in the customer’s environment (self-hosted only, not standard cloud). | Starts at $20K/year for five users, scaling with users and deployment plan. |
Vendor Notes: Where Each Alternative Is Strongest
Sisense and ThoughtSpot
Sisense has the most flexible tenant architecture of the group, with shared row-level security, per-tenant models, or fully isolated instances. Because its Mongo connectivity still runs through the MongoDB BI Connector, verify its roadmap rather than assume it is exempt. ThoughtSpot’s Spotlight search is the most mature conversational analytics experience here and its pricing is published, but it does not solve the NoSQL problem: Mongo and Elasticsearch data still has to land in a warehouse first.
Sigma and Looker
Sigma’s spreadsheet-style workbooks and dbt Semantic Layer integration suit teams already standardized on a modern warehouse, and Sigma Tenants is a genuine answer to multi-tenant isolation, but it shares GoodData’s warehouse-first limitation. Looker’s Gemini integration is the most capable AI layer among the SQL-first group, and LookML offers the same kind of centrally governed model GoodData buyers expect from MAQL, at the cost of developer resources to rebuild it.
Power BI and Tableau
Power BI and Tableau are the only two vendors here with fully published per-user pricing and instant self-service signup, which matters if a slow GoodData procurement cycle is part of the frustration. Neither has native MongoDB or Elasticsearch connectivity, and neither ships a centralized semantic layer comparable to MAQL, so metric governance has to be rebuilt workbook by workbook or model by model.
Where Knowi Fits Best
Knowi fits SaaS and data teams whose production data includes MongoDB or Elasticsearch alongside relational sources. Its own connector does not depend on the MongoDB BI Connector, so it is one of the few platforms here where querying that data directly is unaffected by GoodData’s retirement. The same architecture also replaces GoodData’s one-workspace-per-tenant billing with a single multi-tenant setup, which matters most for teams serving many external customers from one product.
For regulated data, Knowi’s per-tenant row-level security and self-hosted deployment option keep AI processing inside the customer’s environment, an option GoodData, Looker, Power BI, and Tableau do not offer since their AI features route through a third-party cloud LLM. That privacy scope applies specifically to Knowi’s self-hosted or on-prem plan, not its standard cloud deployment, so confirm which plan you are evaluating.
Two trade-offs are worth naming directly. If your data already lives in a governed SQL warehouse and your team wants the deepest code-based modeling ecosystem, Looker’s LookML and Gemini integration or Sigma’s dbt Semantic Layer support may fit better than rebuilding in a new semantic layer. And if procurement speed matters more than NoSQL connectivity, Power BI’s self-service signup will get a team running faster than any quote-based platform here, Knowi included, since Knowi’s pricing starts at $20,000 per year for five users and is negotiated from there.
No Proprietary Modeling Language. No Workspace Tax.
If GoodData’s MAQL learning curve, workspace-per-tenant pricing, or multi-week quote cycle is what pushed you to look, Knowi takes a different approach. It connects directly to your existing SQL, NoSQL, and REST API sources, defines metrics once in a governed semantic layer, and serves every tenant from a single dashboard template.
What you can do with Knowi:
- Define metrics once in a governed semantic layer, without learning a proprietary modeling language.
- Serve every customer from one dashboard template with query-level tenant isolation, instead of a workspace per account.
- Query SQL, NoSQL, and REST API sources directly and join across them without moving data to a warehouse.
- Ask questions in natural language and get answers backed by the underlying query.
- Embed dashboards and AI assistants directly into your product, white-labeled and multi-tenant.
- Deploy in the cloud, or keep AI processing entirely inside your environment with self-hosted deployment.
Used by SaaS, healthcare, manufacturing, and enterprise teams that need governed analytics across multiple data sources without the complexity of traditional BI stacks.
No Proprietary Modeling Language
Multi-Tenant by Design
No ETL Required
On-prem deployment available
Frequently Asked Questions
What happens to GoodData’s MongoDB connector in 2026?
GoodData has confirmed its MongoDB integration will be deprecated by the end of September 2026 and has said it will not build a native replacement. The change stems from MongoDB’s own retirement of its BI Connector, which GoodData’s connectivity depends on. Affected GoodData Cloud customers are being directed to contact customer success for migration guidance.
Is GoodData pricing public, or still by quote?
GoodData remains sales-led with no published price list. It offers Professional and Enterprise tiers priced by number of workspaces, and third-party analyses report quotes taking four to eight weeks, with one estimate placing the entry tier around $1,500 per month.
What replaces MAQL when I switch off GoodData?
There is no direct MAQL equivalent; each alternative uses its own approach, from Looker’s code-based LookML to Sigma’s spreadsheet-style workbooks to Knowi’s declarative semantic layer. The rebuild effort depends on which model you choose, and GoodData has not shipped a standard SQL replacement for MAQL as of 2026.
Which GoodData alternatives support MongoDB and Elasticsearch without a data warehouse?
Among the platforms compared here, Sisense and Knowi are the only two with documented MongoDB connectivity, and Knowi’s connector does not depend on the MongoDB BI Connector that both GoodData and Sisense route through. ThoughtSpot, Sigma, Looker, Power BI, and Tableau all require MongoDB or Elasticsearch data to be loaded into a supported warehouse first.
How does GoodData’s workspace-per-tenant model compare to the alternatives?
GoodData bills and isolates by workspace, which teams report can multiply cost and admin overhead as customers are added. Sisense supports shared row-level security, per-tenant models, or isolated instances; Sigma introduced isolated Sigma Tenants in September 2025; and Knowi uses a single multi-tenant architecture with per-customer data isolation instead of one workspace per account.
Which GoodData alternatives support private, on-prem AI?
Most platforms run AI and NLQ through a third-party cloud LLM, including Looker with Gemini, Power BI with Azure OpenAI, Tableau with Einstein, and ThoughtSpot. Sisense lets self-hosted customers host their own model, and Knowi runs its own AI models on self-hosted or on-prem deployments so data does not leave the customer’s environment; that scope applies to self-hosted deployment, not Knowi’s standard cloud plan.