11 Best Power BI Alternatives for Teams That Don’t Have a Data Engineer (2026)

Switching off Power BI doesn’t always mean switching off the data engineer. Eleven alternatives, and which ones actually remove the dependency.

TL;DR

• Power BI’s April 2025 price hike (Pro up 40% to $14/user/month) and the gradual retirement of Premium P SKUs in favor of Microsoft Fabric are pushing non-technical teams toward alternatives that don’t require DAX expertise or Windows-only installs.
• Databox is the strongest fit for marketing, sales, and ops teams: 4,000+ pre-defined metrics, 140+ templates, unlimited users on Pro and Growth plans starting at $159/month, and native integrations with HubSpot, Google Ads, Salesforce, and Shopify, with no SQL or data engineer required. See the full Databox vs. Power BI comparison.
• Tableau and Looker lead for analyst-heavy teams with dedicated data resources, but their pricing and technical requirements make them poor fits for functional leaders who need dashboards in minutes.
• Data Studio (Google’s free tool, renamed back from Looker Studio in April 2026) is the best free option for Google-ecosystem teams; Metabase is the best open-source option for technically resourced startups willing to self-host.

Microsoft has spent two years repositioning Power BI as accessible to non-technical users, part of a wider industry shift toward self-serve analytics. The catch: Power BI’s version still requires a data engineer, and as of April 2025, the bill for the setup work, the per-seat licenses, and the Fabric capacity behind it is materially higher.

Marketing and sales teams want fast answers to practical questions: What drove MQLs down? Which campaign is wasting spend? Are we on pace to hit pipeline? If every new question still goes through the data team, the tool isn’t really self-service, it’s a slower version of what came before, at a higher price.

Below are 11 Power BI alternatives for teams that need dashboards now, without DAX, complex setup, or a long procurement process.

Why Teams Are Looking for Power BI Alternatives Right Now

Five specific changes, all concentrated in 2025 and 2026, are pushing non-technical teams away from Power BI.

The pricing reset hit hardest.

Power BI Pro’s jump to $14/user/month (from $10) sounds manageable until you multiply it across every stakeholder who needs dashboard access. A 40-person go-to-market team now pays $6,720/year just for viewer-level access. Premium Per User at $24/user/month nearly doubles that. Teams that relied on Premium P SKUs for capacity-based licensing are losing that option entirely as Microsoft consolidates onto Fabric’s F-SKU model, where an F64 capacity setup costs approximately $5,000/month (one-year reserved) or about $8,400/month pay-as-you-go before a single dashboard loads.

The Copilot dependency chain.

Microsoft heavily promotes Copilot as the answer for non-technical users: natural language queries, AI-generated visuals, no DAX required. The fine print: Copilot only runs on Fabric F2+ or Premium P1+ capacity, which adds at least ~$262/month (F2 pay-as-you-go) on top of per-seat Pro licenses for builders. And Copilot’s accuracy depends entirely on a pre-built semantic model: relationships, measures, business definitions, that still has to be built by someone who knows DAX and data modeling. Microsoft is explicit in its own documentation: without that preparation, Copilot outputs are “low-quality and inaccurate” and “might be incorrect or even misleading.” For a marketing team that wants dashboards without first standing up a data warehouse and a modeling layer, the prerequisite chain defeats the purpose.

DAX is a wall, not a learning curve.

Power BI’s formula language is genuinely powerful for data engineers who model complex datasets for a living, spending most of their workday writing and debugging calculated measures. A marketing manager calculating month-over-month MQL change or a sales ops lead building a weighted pipeline view shouldn’t need to write DAX to get there, and even with Copilot writing the DAX for them, they still need a clean model underneath, and a Fabric capacity to run it on. The gap between “the tool can do it” and “my team can do it” is where Power BI loses non-technical users.

Power BI Desktop is Windows-only.

The full authoring experience requires a Windows installation. Teams running Mac-heavy environments, common in marketing, creative, and agency settings, are stuck using the browser version, which lacks key Desktop features like data modeling and custom visuals. For a browser-first, remote-first workforce, a Windows-only desktop app creates daily friction.

Non-Microsoft integrations require workarounds.

Power BI connects natively to Azure, SQL Server, and the Microsoft 365 ecosystem. Connecting HubSpot, Google Ads, Facebook Ads, Salesforce, or Shopify, the tools that marketing, sales, and e-commerce teams actually run on, typically requires third-party connectors, custom APIs, or middleware like Power Automate. Every workaround adds cost, maintenance, and a point of failure that a non-technical team cannot debug independently.

When You Probably Don’t Need to Switch

Before evaluating alternatives, consider three scenarios where staying on Power BI is the right call.

You’re a Microsoft 365 and Azure shop with IT support. If your organization already runs on Azure, uses SQL Server for data warehousing, and has IT or data engineering staff managing the Power BI environment, the ecosystem advantages are real. Native Azure integration, row-level security, and DirectQuery performance against SQL Server are genuinely strong, and they compound when your entire data stack is Microsoft-native.

Your team has invested heavily in DAX models. Complex DAX measures, calculated columns, and role-based security rules represent months of work. Migrating that logic to another platform takes two to six months, depending on complexity. If your existing models are working and your team has the skills to maintain them, the switching cost may exceed the benefit.

You need an on-premises deployment. Power BI Report Server remains one of the few enterprise BI tools with a true on-prem option. Organizations with strict data residency requirements or air-gapped environments may have limited alternatives.

If none of those describe your team, keep reading.

How We Evaluated These Alternatives

Every tool was evaluated around one practical question: can a marketing, sales, or ops leader, or an exec, get useful dashboards without turning the project into an IT request?

Ease of use for non-technical users. Can you connect data, build a dashboard, and answer basic questions without SQL, DAX, or help from a data engineer? Tools that require a dedicated analyst scored lower.

Pricing transparency and model. Published pricing, free tiers, and unlimited-user plans scored higher because they make it easier to evaluate and share a tool. Per-seat pricing and “contact sales” plans scored lower because they slow down teams that are ready to choose now.

Native integrations with marketing, sales, and ops tools. HubSpot, Google Ads, Salesforce, Facebook Ads, Shopify: if a tool requires custom connectors or middleware to reach these platforms, it adds cost and maintenance for teams without engineering resources.

Time-to-first-dashboard. How fast can you go from signup to a dashboard with real data? Tools with no-code setup, guided workflows, and ready-made metrics scored higher than blank-canvas platforms that make you build everything from scratch.

AI assistance. Does the tool’s AI actually help a non-technical user spot trends, answer questions, or generate insights, or does it sit behind a setup project? Tools where AI works on top of pre-defined metrics scored higher than tools where AI depends on a clean warehouse, semantic model, or other engineering work first.

Mobile access and sharing. Can you share a dashboard with a client via link? Stream it to a TV in the office? Send automated snapshots to Slack? Mobile-first access and flexible sharing options matter for teams that consume data outside a desktop browser.

1. Databox

Overview

Databox is an AI-powered analytics platform for teams that need clear, trusted answers fast — without writing SQL, maintaining a semantic layer, or waiting on a data team. It sits between static dashboards and heavyweight BI, with 130+ native integrations to the tools marketing, sales, ops, and agency teams already run.

Key features

• 130+ native integrations, including HubSpot, Salesforce, GA4, Meta Ads, Shopify, LinkedIn Ads, Stripe, and QuickBooks.
• Drag-and-drop dashboards built in minutes — no SQL, no modeling layer to maintain.
• AI Analyst to ask plain-language questions like “What’s driving our MQL drop this month?” and get an answer grounded in your actual pipeline data.
• Reports for client- and stakeholder-ready exports, shareable links, TV mode, and scheduled deliveries to email or Slack.
• Goals & Forecasting built into the same platform where your performance data lives.
• Databox MCP server to connect your performance data to Claude, ChatGPT, n8n, and other AI tools.
• Native iOS and Android apps with full dashboard functionality.

Pros and cons

✅ Non-analysts can build dashboards same-day — no SQL, no training session.
✅ Unlimited users on Pro and Growth plans, so cost doesn’t scale against you when more people need access.
✅ Native connectors mean no middleware, no Supermetrics, no third-party billing.
✅ AI answers grounded in your verified metrics, not a raw warehouse query.

❌ Not built for ad-hoc SQL exploration against a raw warehouse.
❌ Pricing scales with data sources, so a sprawling stack costs more than a focused one.
❌ Less suited to teams who want to build statistical models or run regression analysis.

Pricing

• Free plan: 1 user, 3 data sources.
• Analyst: From $64/month — 1 user, 5 data sources.
• Pro: From $159/month — unlimited users, 3 data sources, limited datasets.
• Growth: From $399/month — unlimited users, 3 data sources, datasets, 15-minute sync, forecasting, sub-accounts, dedicated CSM.
• Custom plans available for white-labeling, SSO, advanced security, and account setup support.
• 14-day Growth trial, no credit card. Monthly billing runs ~20% higher than annual.

Best for

Marketing managers, sales ops leads, agency teams, and business owners tracking KPIs across CRM, paid media, email, SEO, and finance — without a data analyst on standby.

2. Tableau

Overview

A visual analytics platform for teams that need highly flexible, interactive dashboards and have analysts available to build them.

Key features

• Deep visualization library with broad chart-type coverage.
• Interactive dashboards with drill-down and filtering.
• Strong Salesforce CRM integration.
• Large community plus an extensive template and training ecosystem.
• Tableau Agent and Tableau Pulse for natural-language questions, anomaly detection, and AI-driven metric summaries (Tableau+ Bundle, English/US data residency only).

Pros and cons 

✅ Best-in-class visualization depth and flexibility for analysts. 
✅ Mature ecosystem — community, templates, certifications, and third-party content are abundant. 
✅ Tight Salesforce integration if you’re already in that stack.

❌ Not a quick self-service option for most marketing or sales leaders — non-technical users usually need training or analyst support. 
❌ Per-seat pricing climbs quickly as more people need access, especially on Enterprise tiers. 
❌ AI features (Agent, Pulse) are gated to the Tableau+ Bundle and restricted to English/US data residency.

Pricing

• Tableau Cloud Standard (annual billing): Creator $75/user/month, Explorer $42/user/month, Viewer $15/user/month.
• Tableau Cloud Enterprise: Creator $115/user/month, Explorer $70/user/month, Viewer $35/user/month.

Best for 

Data analysts, BI teams, and organizations with dedicated reporting staff that need advanced visualization depth.

3. Looker

Overview

Google’s enterprise BI platform, built around LookML — a SQL-based modeling layer that creates one governed metric definition for the whole company. An analyst’s tool, not a marketer’s tool.

Key features

• LookML semantic layer so a VP of Sales and an analyst see the same pipeline number without reconciliation.
• Native BigQuery integration; also supports Snowflake and Redshift.
• Git-based version control for data models.
• Embedded analytics for customer-facing dashboards.
• Gemini in Looker for natural-language queries grounded in the semantic model.

Pros and cons

✅ Best-in-class governance — one source of truth across the company.
✅ Strong fit if you already run BigQuery and have a data team.
✅ Embedded analytics works well for SaaS products serving customer-facing reports.

❌ A marketing manager cannot build or modify a Looker dashboard without analyst support.
❌ Pricing isn’t published. Procurement is a sales conversation.
❌ Most teams spend more on LookML development than on the license itself.

Pricing

• Standard Edition: ~$60,000–$67,000/year (1 production instance, 10 Standard Users, 2 Developer Users).
• Enterprise and Embed Editions: Custom, typically $100,000+/year.
• Total cost commonly reaches $150,000+/year for mid-market deployments once development resources are factored in.

Best for

Organizations with a mature cloud data warehouse and a dedicated data team that needs one governed source of truth.

4. Qlik Sense

Overview

A BI platform built around an associative data engine that lets analysts explore relationships across datasets in any direction, rather than answering pre-defined questions.

Key features

• Associative engine for multi-source data exploration.
• Insight Advisor for natural-language queries; Qlik Answers (2026) for generative AI on unstructured data.
• Qlik AutoML for no-code predictive modeling.
• Active intelligence triggers for real-time alerts.
• Enterprise-grade security and a data catalog for large organizations.

Pros and cons

✅ Genuinely powerful for exploring complex, multi-source relationships.
✅ Strong AutoML capability for teams wanting predictive analytics without a data science team.
✅ Mature enterprise security and governance.

❌ Learning curve assumes a technically proficient user who understands data relationships.
❌ Less accessible than lighter dashboard tools for non-technical business users.
❌ Cost at scale runs higher than most alternatives on this list.

Pricing

• Qlik Sense Business: $30/user/month (billed annually).
• Qlik Sense Enterprise SaaS: $70/user/month.
• Enterprise (Self-Managed): Custom quote.
• 30-day free trial available.

Best for

Analytics teams in manufacturing, supply chain, or financial services that need to explore complex, multi-source data relationships.

5. Domo

Overview

A cloud-native platform that combines ETL, dashboards, and collaboration. Targets the enterprise mobile-dashboard use case more directly than most competitors.

Key features

• 1,000+ data connectors and built-in ETL via Magic ETL.
• Real-time data updates and a native mobile app with full dashboard functionality.
• Domo AI Chat for natural-language exploration.
• AI Agent Builder and MCP Server (2026) for connecting Domo data to Claude, Gemini, and other AI tools.
• Role-based access controls and an app development framework for custom analytics.

Pros and cons

✅ Strong all-in-one if you want ETL + BI + mobile in one platform.
✅ Best-in-class mobile experience for executives.
✅ Broad connector library.

❌ Pricing is opaque and consumption-based — verified G2 reviewers have flagged renewal surprises, including a reported 1,120% year-over-year increase for the same usage.
❌ Magic ETL charges credits on both ingestion and transformation, which can double-count the same data.
❌ Overkill for teams that just need marketing and sales dashboards.

Pricing

• Median annual contracts run ~$60,500/year (Vendr data).
• Practical floor around $30,000/year for small deployments.
• Large enterprise contracts can exceed $130,000/year.
• 30-day free trial; no credit card required.

Best for

Enterprise teams (500+ employees) consolidating ETL, BI, and mobile-first executive reporting into one platform.

6. ThoughtSpot

Overview

An analytics platform built around natural-language search. Users type questions like “What were our top 5 products by revenue last quarter?” and the tool generates the chart.

Key features

• Search-driven analytics interface with the Spotter AI Agent for conversational queries (25 queries per user per month on Pro).
• SpotIQ for automated insight surfacing.
• LiveBoard for real-time collaborative dashboards.
• Native connectors for Snowflake, BigQuery, and Redshift.
• Embedded analytics options for product teams.

Pros and cons

✅ Strongest natural-language interface in the category for warehouse-native data.
✅ Automated insight surfacing genuinely catches things humans miss.
✅ Good fit for organizations that already invested in a clean data warehouse.

❌ Answer quality depends entirely on the data foundation. ThoughtSpot doesn’t fix messy or inconsistently modeled data — it amplifies the gaps.
❌ Per-user pricing creates scaling pressure beyond 50+ users.
❌ Data modeling services for proper setup commonly run $50,000–$150,000 upfront.

Pricing

• Essentials: $25/user/month, billed annually (5–50 users, 25M rows).
• Pro: $50/user/month (25–1,000 users, 250M rows, Spotter AI Agent included).
• Enterprise: Custom, typically $100,000+/year.
• Free trial available.

Best for

Business users in organizations that already have a clean, governed cloud data warehouse and want to query it in plain English.

7. Data Studio

Overview

Google’s free, browser-based reporting tool. On April 11, 2026, Google renamed Looker Studio back to Data Studio to end years-long confusion with the enterprise Looker platform (entry #3 — a completely separate product). Existing reports, data sources, and permissions transitioned automatically.

Key features

• Unlimited free dashboards and reports.
• Native connectors for all major Google products: GA4, Google Ads, BigQuery, Sheets, Search Console.
• Community connector marketplace for third-party data sources.
• BigQuery conversational agents (new in April 2026) and Colab data apps integration.
• Shareable report links with viewer/editor permissions.

Pros and cons

✅ Genuinely free for unlimited dashboards.
✅ Best-in-class for Google Analytics, Google Ads, and BigQuery.
✅ Familiar to anyone who’s used Google Sheets.

❌ Connecting HubSpot, Salesforce, or Shopify reliably often requires paid third-party connectors like Supermetrics.
❌ Handles simple dashboards well; struggles with complex blending, calculated fields, or large datasets.
❌ No native mobile app.

Pricing

• Data Studio: Free.
• Data Studio Pro: $9/user/month — adds enterprise security, team workspaces, Conversational Analytics, and Policy User controls.
• Some community connectors are paid ($10–$50/month per connector).

Best for

Marketing teams, SEO/PPC specialists, and agencies that live in Google Analytics, Google Ads, and Search Console and want a free reporting layer on top.

8. Metabase

Overview

An open-source BI tool with a visual query builder. The most widely deployed open-source BI platform, with 60,000+ companies running the self-hosted version.

Key features

• SQL editor and visual query builder.
• Self-hosted (free, AGPL v3) or Metabase Cloud deployment options.
• Embedded analytics for product teams.
• Broad database support: PostgreSQL, MySQL, MongoDB, and more.
• Metabot AI add-on for natural-language SQL generation.

Pros and cons

✅ Free, self-hosted option for technical teams who want to own their stack.
✅ Solid visual query builder for non-SQL users — if a developer maintains the environment.
✅ Strong embedded analytics for early-stage SaaS products.

❌ Self-hosting needs a team that can manage server infrastructure, upgrades, and security. Budget ~$15,000–$20,000/year in hidden DevOps cost.
❌ Not built for non-technical users without a developer maintaining the environment.
❌ Interactive embedding viewers count as paid users on Pro — 500 viewers ≈ $74,000/year.

Pricing

• Self-hosted (open source): Free.
• Cloud Starter: $100/month base + $6/user/month (5 users included).
• Cloud Pro: $575/month base + $12/user/month (10 users included).
• Enterprise: Starts ~$20,000/year; median contracts ~$39,000/year per Vendr.
• Metabot AI: $100/month add-on for 500 requests.

Best for

Engineering teams and technical startups that want a low-cost BI tool they can connect directly to their database.

9. Zoho Analytics

Overview

A self-service BI tool with native integration across the Zoho product ecosystem (CRM, Books, Desk, Projects). For businesses already running on Zoho, it’s the easiest analytics layer to add.

Key features

• Drag-and-drop report builder.
• Zia AI assistant for natural-language queries.
• White-label reporting for agencies.
• Broad connector library beyond the Zoho ecosystem.
• Collaboration features including comments and scheduled email reports.

Pros and cons

✅ Easiest analytics layer to add for Zoho CRM, Books, or Projects customers.
✅ Aggressive entry pricing — $30/month for 5 users.
✅ Permanent free tier for small teams (2 users, 10K rows).

❌ Strongest inside the Zoho ecosystem. Outside it, integrations are less polished.
❌ Visualization depth falls short of Tableau or Power BI for complex dashboards.
❌ Lacks the depth of Looker or ThoughtSpot for warehouse-native analysis.

Pricing

• Free: 2 users, 10,000 rows, 5 workspaces.
• Basic: $30/month (5 users, 1M rows).
• Standard: $60/month (10 users, 5M rows).
• Premium: $145/month (25 users, 25M rows).
• Enterprise: $575/month (50 users, 50M rows).
• All paid plans billed annually; monthly billing runs ~20% higher. 15-day free trial.

Best for

SMBs already using Zoho CRM, Zoho Books, or other Zoho products that want reporting without leaving the ecosystem.

10. Sisense

Overview

A BI platform specialized in embedded analytics — adding dashboards and reporting directly into SaaS products or customer-facing applications.

Key features

• Compose SDK for embedding in React, Angular, or Vue applications.
• Sisense Intelligence AI assistant and MCP server for embedded use.
• In-chip technology for handling large datasets without extract latency.
• White-label and custom theming options.
• Bring-your-own-LLM or Sisense-managed LLM (2026 GA).

Pros and cons

✅ Genuine best-in-class for embedded analytics inside a SaaS product.
✅ Strong customization and white-labeling for customer-facing dashboards.
✅ In-chip engine handles large datasets without extract pipelines.

❌ Not a strong fit for internal-only BI use cases.
❌ Implementation typically takes 14+ weeks and requires developer resources.
❌ Custom pricing is hard to forecast as embedded usage grows.

Pricing

• Entry-level deployments start around $399/month.
• Mid-market embedded use cases: $100,000–$150,000/year.
• Large enterprise contracts can match or exceed Looker pricing.
• 14-day free trial.

Best for

SaaS product teams and ISVs that need to build analytics features into their own applications.

11. Amazon QuickSight

Overview 

AWS’s BI tool — the lowest-friction option when your data already lives in S3, Redshift, Athena, or other AWS services.

Key features

• Deep integration with the AWS ecosystem (S3, Redshift, Athena, RDS).
• Reader session pricing for occasional or read-only users.
• Q natural language querying for ad-hoc questions.
• ML-powered anomaly detection and forecasting built in.

Pros and cons 

✅ Lowest friction if your data already sits in AWS. 
✅ Session-based reader pricing is genuinely cheap for infrequent dashboard viewers. 
✅ Built-in ML features (anomaly detection, forecasting, Q) without separate tooling.

❌ Loses most of its appeal outside the AWS ecosystem. 
❌ Not a self-service tool for non-technical marketing or sales teams — someone needs to understand the AWS environment. 
❌ Author Pro tier carries a $250/month infrastructure fee when Q&A is enabled, which can surprise smaller accounts.

Pricing

• Readers: $3/user/month minimum, or $0.30 per 30-minute session capped at $5/user/month.
• Authors: $24/user/month, or $18/user/month with annual billing.
• Author Pro: $40/user/month.
• $250/month per-account infrastructure fee if any Pro user has Q&A enabled.

Best for 

AWS-native engineering and data teams that need dashboards on top of S3, Redshift, Athena, and related AWS data services.

Power BI Alternatives Compared: Quick-Reference Table

See how Databox compares to Power BI feature-by-feature →

How to Choose the Right Power BI Alternative for Your Team

Three questions narrow the field from eleven tools to one or two real candidates.

Question 1: Is your team technical or non-technical?

Non-technical teams, those without a dedicated data engineer, analyst, or SQL-literate user on staff, need a tool where the business user is the builder. Databox, Data Studio, and Zoho Analytics are the strongest fits. Tableau, Qlik, and Looker require technical users for initial setup and ongoing model maintenance.

Question 2: What tools do you already use?

The fastest path to a working dashboard is a tool that connects natively to your existing stack. Marketing teams on HubSpot, Google Ads, and Facebook Ads get the most pre-built coverage from Databox. Teams running almost entirely on Google properties should evaluate Data Studio first. AWS-native data teams already in Redshift or Athena should look at QuickSight. Zoho-ecosystem companies get the most value from Zoho Analytics. Salesforce-heavy revenue teams working with dedicated analysts have strong options in Tableau.

Question 3: How many people need access?

Per-seat pricing becomes expensive fast. A 30-person go-to-market team paying $14/user/month on Power BI Pro pays $5,040/year just for access, before anyone builds a single report. Databox’s unlimited-user model on Pro and Growth plans changes that math entirely: the same 30-person team pays $1,908/year on the Pro plan. For teams where broad access across marketing, sales, and operations is the goal, the pricing model matters as much as the feature set.

Quick decision guide:

• Non-technical team, marketing/sales/ops tools → Databox
• Google-first, tight budget → Data Studio
• Dedicated analyst, complex visualizations → Tableau
• Mature data warehouse, metric governance → Looker
• AWS-native data team → Amazon QuickSight
• Open-source, technical startup → Metabase
• Zoho ecosystem, SMB → Zoho Analytics
• Enterprise consolidation of ETL + BI → Domo
• Clean warehouse data, natural language queries → ThoughtSpot
• Embedded product analytics → Sisense
• Exploratory multi-source analysis → Qlik Sense

The Tool Choice Is Also a Team Structure Choice

Eleven alternatives, and the right one is not always the one with the most features or the deepest AI layer. The right one is the one your team will actually use next Monday without filing a support ticket or spending a weekend in documentation.

Power BI’s capabilities are not in question for organizations with the right setup. For a data engineering team inside a Microsoft Azure environment, it remains a strong platform. But for the marketing manager who just needs to see MQL volume and ROAS in one view, or the RevOps lead who wants pipeline coverage visible to the whole sales team without paying $14/head, Power BI is solving a different problem than the one they have, and the 2025 pricing changes made tolerating that misfit more expensive.

Choosing a tool is also choosing who owns your data practice. A warehouse-native tool like Looker puts that ownership with a data engineer. A self-hosted tool like Metabase puts it with DevOps. Databox puts it with the marketing manager, sales ops lead, or agency account manager who needs the number now.

That is the structural difference the tool comparison table cannot fully capture. Pick the one that puts the dashboard in the hands of the person who needs to act on it.

Try Databox free — no technical setup required. Connect your first data source and have a dashboard running in minutes.

Or compare Databox to Power BI feature-by-feature before you decide.