Registro lado a lado

Alteryx vs. Mode

Elaborado a partir del registro investigado y revisado de cada herramienta. Las cifras se contrastan con las páginas públicas de precios en el momento de la investigación — confirma siempre el precio actual con el proveedor antes de comprar.

La versión corta

Mode parte de Free por usuario, frente a Alteryx con $250. Alteryx tiene la valoración agregada más alta (4.6/5 frente a 4.5/5).

Comparación completa

	Alteryxdesde $250 per user/month, billed annually	Modedesde Free
Audyense Score	78ASSólido	56AS*Aceptable
Posicionamiento	Low-code analytics automation and AI insights on a governed cloud platform	Analyst-first BI combining SQL, Python, R, dashboards, and advanced analytics.
Plan gratuito	No	Sí
Implementación	Nube / SaaS, Instalación local	Nube / SaaS
Mejor encaje	Mediana empresa, Empresa	PYME, Mediana empresa, Empresa
Planes de precio	Starter$250 ProfessionalCustom pricing EnterpriseCustom pricing	StudioFree BusinessCustom pricing EnterpriseCustom pricing
AI features	SíAiDIN Copilot, Magic Documents, AutoML, and pluggable LLMs incl. Anthropic, OpenAI.	—
Public API	ParcialREST APIs for cloud and Server, but public endpoints are Enterprise-edition only.	—
Mobile app	NoNo native iOS or Android app; the cloud workspace is browser-only.	—
Role-based access control	SíRole-based access control and workspace permissions in the governance layer.	—
Dashboards & visualization	SíAuto Insights missions and cloud reporting produce shareable dashboards and charts.	—
Embedded analytics	ParcialAnalytic apps embeddable via Server API or iframe; no dedicated embedding SDK.	—
Self-service BI	SíDrag-and-drop canvas with 300+ code-free tools aimed at non-programmer analysts.	—
Semantic layer / data modeling	NoNo dedicated semantic or metrics layer; governance covers workflow lineage only.	—
Alerts & notifications	SíScheduled email subscriptions for missions plus workflow run notifications.	—
Natural language query	SíAuto Insights takes natural-language prompts; Copilot builds workflows from text.	—
Python and R notebooks	—	Sí
Interactive dashboards	—	Sí
Reusable datasets	—	Sí
Governed metrics	—	SíSupports dbt Semantic Layer integration.
Custom data apps	—	Sí
Advanced analytics	—	Sí
Role-based access control	—	SíEnterprise feature.
SQL editor	—	Sí
Integraciones verificadas	18+	—
Valoración agregada	4.6 · 671 reviews	4.5 · 330 reviews
Integraciones	SnowflakeDatabricksGoogle BigQueryAmazon RedshiftAmazon S3Amazon Athena+12 más	SnowflakeAmazon RedshiftGoogle BigQueryPostgreSQLMySQLMicrosoft SQL Server+6 más
Seguridad y cumplimiento	SOC 2 Type IIISO 27001ISO 22301GDPR+2 más	—
Pros	Drag-and-drop workflow canvas lets analysts with no SQL or Python background build repeatable data pipelines Large library of prebuilt tools and connectors covering databases, cloud warehouses, and SaaS applications shortens setup time Automating recurring prep and reporting removes substantial manual spreadsheet work each cycle AI assistance, including Copilot and natural-language insight summaries, is built into the platform rather than bolted on	Native SQL, Python, and R workflow with results flowing into dashboards and reports. Strong bridge between ad hoc analysis, advanced analytics, and self-serve reporting. Reusable datasets and governed metrics reduce repeated analyst work. Custom data apps and embedded analytics expand the use case beyond static dashboards.
Contras	Licensing cost is the most common complaint, with seat and server pricing seen as steep for smaller teams Advanced tools carry a steep learning curve and documentation is described as unfriendly to beginners In-memory processing means performance degrades on very large datasets, especially joins and sorts No native Git integration or real versioning, so collaborative workflow development feels dated	The platform is optimized for analyst-led teams rather than pure business-user self-service. Enterprise identity, access, and support capabilities are not part of the free Studio tier. Pricing and packaging are less transparent for Business and Enterprise. Python/R environments and warehouse performance still require data-team administration.
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