February 11th, 2026
9 Best Predictive Analytics Software: Features & Pricing in 2026
By Tyler Shibata · 31 min read
After testing dozens of platforms, here are the 9 best predictive analytics software tools for forecasting, scenario planning, and data-driven decisions in 2026.
9 Best predictive analytics software in 2026: At a glance
Tool | Best For | Starting price | Key strength |
|---|---|---|---|
No-code Q&A style data analysis | Connects to your databases and generates charts from natural language questions | ||
Search-driven analytics | AI-powered search across connected data | ||
Interactive dashboards and associative analytics | $200/month for 10 users | No-code interface with automated insights | |
Marketing and customer analytics | Predictive customer behavior modeling | ||
Finance and planning teams | Integrated forecasting with planning workflows | ||
Data preparation and predictive workflows | Drag-and-drop predictive model building | ||
Automated forecasting | Pre-built models for time series analysis | ||
Custom AutoML and predictive modeling | Open source AutoML with model explainability | ||
Enterprise ML pipelines | Cloud-native platform with deep Azure integration |
1. Julius: Best for no-code Q&A-style data analysis
What it does: Julius is an AI-powered data analysis tool that connects to your databases and generates charts when you ask questions in everyday language. It handles query execution automatically and learns your data structure to support trend analysis and repeated forecasting queries over time.
Who it's for: Business teams managing structured data who need analytical insights without SQL expertise.
We built Julius so business teams can analyze data from their databases without writing code. You can connect sources like Postgres, Snowflake, or BigQuery and start working with your data. You can ask about quarterly performance or customer segments, and Julius returns visual results like charts and tables.
As you ask questions, Julius maps your database relationships and learns how your tables connect. Each query teaches it more about which columns matter and how to interpret your setup, so forecasting queries and trend analysis get more accurate over time.
The Notebooks feature lets you save analyses worth repeating. Set them to run weekly or monthly, and Julius updates the data automatically before sending results to your inbox or Slack channel. Teams use it for recurring business forecasts and performance reports that inform business planning.
Key features
Natural language queries: Ask questions and get visual answers without SQL
Connected data sources: Links to Postgres, BigQuery, Snowflake, Google Sheets, and more
Reusable Notebooks: Save recurring analyses that refresh automatically
Scheduled reporting: Send results to Slack or email on a set cadence
Semantic learning: Improves understanding of your database relationships with each query
Pros
Quick setup for structured data
Clear charts for exploratory analysis
Automated recurring updates
Cons
Built more for business analysis than advanced statistical modeling
Requires clean, consistently formatted data
Pricing
Julius starts at $37 per month.
Bottom line
2. ThoughtSpot: Best for search-driven analytics
What it does: ThoughtSpot is a business intelligence platform that uses AI-powered search to answer data questions. You type questions in natural language, and it returns visualizations and insights from your connected data sources. The platform can suggest related questions and highlight anomalies automatically.
Who it's for: Teams that need quick answers from their data without building dashboards first.
I tested ThoughtSpot by connecting it to a sales database and asking questions about revenue patterns, customer segments, and regional performance. The platform returned visualizations quickly without requiring me to specify chart types or build dashboards first.
The search suggestions helped when I wasn't sure how to phrase complex questions. ThoughtSpot offered related queries based on my data structure, and clicking those suggestions surfaced metrics I hadn't considered, like quarter-over-quarter comparisons.
ThoughtSpot handled ambiguous phrasing better than I expected. When I asked about "performance last quarter," it interpreted that correctly using the date fields in my data without needing clarification.
Key features
Natural language search: Type questions and get instant visualizations
AI-powered insights: Suggests related queries and detects anomalies automatically
Connected data sources: Links to cloud data warehouses, databases, and spreadsheets
Pros
Fast query responses
Intuitive search interface
Helpful suggestions for exploration
Cons
Requires clean, well-structured data to work effectively
Advanced customization needs technical knowledge
Pricing
Bottom line
3. Qlik Sense: Best for interactive dashboards and associative analytics
What it does: Qlik Sense is a business intelligence platform that lets you create interactive dashboards and explore data relationships through associative analytics. Predictive modeling features are available through Qlik AutoML in the cloud platform or by connecting to external machine learning tools.
Who it's for: Teams that need flexible dashboards with exploration capabilities built in.
I uploaded sales and inventory data to test how Qlik Sense handled connections between datasets through its associative engine. When I clicked on a product category, the platform automatically highlighted metrics like related regions, time periods, and revenue figures without requiring manual relationship setup.
The drag-and-drop interface made building dashboards straightforward. I selected fields and dropped them into chart templates, and the platform suggested appropriate chart types based on what I chose.
The AutoML feature let me build a basic forecast model by selecting variables and following guided prompts. The platform handled algorithm selection and training automatically, then displayed accuracy scores so I could evaluate reliability.Key features
Associative analytics: Click any data point to see related metrics across all datasets
Drag-and-drop dashboard builder: Create visualizations without coding
Integrated AutoML: Build predictive models through guided prompts
Pros
Flexible exploration without predefined paths
Intuitive dashboard creation
No-code predictive modeling
Cons
Can be slow with very large datasets
Learning curve for advanced features
Pricing
Bottom line
4. Adobe Analytics: Best for marketing and customer analytics
What it does: Adobe Analytics is a digital analytics platform built for tracking customer journeys and measuring campaign performance across websites and apps. It offers predictive features for estimating conversion trends, but its core strength is real-time reporting and marketing optimization rather than general predictive modeling.
Who it's for: Marketing teams that need predictive insights into customer behavior and campaign performance.
I tested Adobe Analytics by connecting it to website data and running queries on user behavior, traffic sources, and conversion funnels. The platform identified drop-off points in the checkout flow and flagged unusual traffic spikes without requiring me to set up custom alerts.
The predictive features stood out during forecasting tests. I used the tool to estimate expected conversions for an upcoming campaign, and Adobe Analytics generated predictions based on historical patterns while showing a range of likely outcomes.
Segmentation tools let me group users by behavior rather than just demographics. I created audience segments based on browsing patterns and purchase history, then used those segments to test how different groups responded to marketing messages.Key features
Customer journey analytics: Track user behavior across channels and touchpoints
Predictive insights: Estimate conversions, churn risk, and campaign performance
Automated segmentation: Group audiences by behavior patterns using machine learning
Pros
Strong predictive capabilities for marketing metrics
Detailed customer journey tracking
Real-time anomaly detection
Cons
Expensive for smaller teams
Complex interface with a steep learning curve
Pricing
Adobe Analytics uses custom pricing.
Bottom line
5. SAP Analytics Cloud: Best for finance and planning teams
What it does: SAP Analytics Cloud is a business intelligence platform that combines budgeting, forecasting, and planning in one system. You can build budgets, run scenarios, and create reports using SAP data or external sources. It uses built-in machine learning to automate forecasts.
Who it's for: Finance teams that need integrated planning and predictive analytics within their existing SAP environment.
I connected SAP Analytics Cloud to sample financial data to test how well it handled budget planning and forecast workflows. I built what-if scenarios by adjusting revenue assumptions or cost structures, and the platform updated related metrics automatically without requiring me to write formulas.
The planning features worked better than I expected for collaborative workflows. I created a budget template, shared it with mock team members, and tracked changes as they submitted inputs. Version control showed who made which adjustments and when, so I could review the approval trail before finalizing numbers.
The predictive forecasting tools worked within the same interface I used for planning. I selected historical revenue data, chose a forecasting method, and the platform generated projections for the next quarter. The results appeared alongside actual numbers in the same dashboard, which made it easier to compare predictions against performance.
Key features
Integrated planning and analytics: Build budgets and forecasts in the same platform where you analyze results
Scenario modeling: Test multiple assumptions and see how they affect outcomes
Automated forecasting: Generate predictions using machine learning on historical data
Pros
Strong integration with SAP systems
Collaborative planning workflows
Built-in version control and audit trails
Cons
Complex setup for non-SAP environments
Steep learning curve for new users
Pricing
SAP Analytics Cloud uses custom pricing.
Bottom line
6. Alteryx: Best for data preparation and predictive workflows
What it does: Alteryx is a data analytics platform that combines data preparation, blending, and predictive modeling in a drag-and-drop interface. You can clean datasets, merge information from multiple sources, and build forecasts without code for most tasks. More complex models may require Python or R integration.
Who it's for: Analysts who need to prepare messy data before running predictive models.
I uploaded customer data from three different sources to test how Alteryx handled data preparation for forecasting. The drag-and-drop interface let me connect spreadsheets and databases, then clean inconsistencies like duplicate records or missing values by selecting tools from a menu.
After preparing the data, I built a predictive model by dragging a regression tool onto the canvas and selecting my target variable. Alteryx processed the data and returned accuracy metrics in the same workflow, which meant I didn’t have to export cleaned data to another platform.
The pre-built workflow templates saved time when I needed standard forecasting setups. I selected a time series template, mapped my data fields to the required inputs, and Alteryx guided the analysis setup based on the template structure.
Key features
Drag-and-drop data prep: Clean and blend data from multiple sources without code
Integrated predictive modeling: Build forecasts within the same workflow as data preparation
Pre-built templates: Use standard workflows for common analysis tasks
Pros
Powerful data preparation capabilities
No coding required for most tasks
Handles complex data transformations
Cons
Expensive for small teams
Steeper learning curve than simpler BI tools
Pricing
Alteryx uses custom pricing.
Bottom line
7. SAS Viya: Best for automated forecasting
What it does: SAS Viya is an enterprise analytics platform with capabilities for forecasting, machine learning, and statistical analysis. For time series work, it analyzes your data patterns and applies appropriate forecasting techniques automatically. It runs on cloud infrastructure and includes validation tools for testing accuracy.
Who it's for: Teams that need reliable forecasting without manually designing statistical models.
I ran SAS Viya through automated forecasting tests using three years of sales data. The platform analyzed the data structure, spotted seasonal trends, and applied a forecasting method automatically. I didn’t need to choose between models.
The model validation tools helped me evaluate forecast reliability. SAS Viya split my historical data into training and testing sets, then showed accuracy metrics like mean absolute error so I could assess whether the predictions were reliable enough to use for planning.
I tested scenario planning by adjusting assumptions about market growth and seasonal factors. The platform regenerated forecasts based on the new parameters, which made it easier to prepare for different business conditions without rebuilding models.
Key features
Automated model selection: Platform chooses appropriate forecasting methods based on your data
Built-in validation: Test forecast accuracy using historical data splits
Scenario planning: Adjust assumptions and regenerate predictions quickly
Pros
Strong statistical foundation
Minimal manual configuration
Reliable for time series forecasting
Cons
Less flexible for non-forecasting analytics
Complex pricing structure
Pricing
SAS Viya uses custom pricing.
Bottom line
8. H2O.ai: Best for custom AutoML and predictive modeling
What it does: H2O.ai is an open-source machine learning platform that automates the process of building and testing predictive models. You upload your data, and the platform tries different approaches to find which one produces the most accurate predictive results. It includes tools that show which features most influenced model predictions.
Who it's for: Data teams that want flexible AutoML capabilities with transparency into how models work.
I tested H2O.ai by uploading customer churn data to see how well its AutoML feature handled model selection. The platform tested multiple model types automatically, ranked them by accuracy, and showed performance comparisons across different model types so I could evaluate tradeoffs between speed and precision.
The explainability tools helped me understand model decisions. H2O.ai generated charts showing which variables had the strongest influence on predictions, like how contract length or support tickets affected churn risk. That transparency made it easier to explain forecasts to stakeholders who needed to understand the reasoning.
Key features
Automated model testing: Platform evaluates multiple algorithms and ranks them by performance
Model explainability: Visual tools show which variables drive predictions
Open-source foundation: Free access to core features with enterprise options available
Pros
Strong AutoML capabilities
Transparent model explanations
Flexible customization options
Cons
Requires technical knowledge for advanced use
Steeper learning curve than business-focused tools
Pricing
Bottom line
9. Azure Machine Learning: Best for enterprise ML pipelines
What it does: Azure Machine Learning is a cloud-based platform for building and deploying predictive models that work across your organization. You can create models using visual tools or code, then make them available as services that other applications can use. It connects with Azure's data storage and supports automated model building through AutoML.
Who it's for: Enterprise teams that need scalable machine learning infrastructure integrated with cloud data systems.
I built a forecasting model in Azure Machine Learning using retail sales data. The visual designer let me connect my data and build models by dragging components onto a canvas, so I didn’t need to write code for basic setups.
The platform processed complex models faster by scaling computing resources when needed. I could run quick tests with minimal resources or scale up for detailed analysis without changing how I worked.
After building a forecast model, I deployed it so other business applications could access the predictions. Azure tracked performance and managed updates automatically, which meant I could improve the model over time without breaking existing connections.
Key features
Visual and code-based development: Build models using drag-and-drop designers or Python notebooks
Scalable compute resources: Train models on cloud infrastructure that adjusts to your needs
Production deployment: Deploy models as APIs with built-in monitoring and version control
Pros
Strong integration with Azure services
Flexible development options
Enterprise-grade scalability
Cons
Requires cloud infrastructure knowledge
Pay-as-you-go pricing can be unpredictable
Pricing
Azure Machine Learning uses pay-as-you-go pricing.
Bottom line
How I tested these predictive analytics tools
I evaluated each platform by running it through forecasting tasks that business teams need to complete. That meant connecting datasets, building or configuring models for revenue projections and trend analysis, and checking whether the results were accurate enough to use in planning sessions.
I focused on finding platforms that balanced speed with transparency, so teams could trust the outputs without needing a data science background to validate them.
Here's what I measured during testing:
Setup complexity: How long it took to connect data sources and run the first forecast
Forecast accuracy: Whether predictions reasonably aligned with known outcomes when tested on historical data
Learning curve: How much training someone would need before generating reliable results
Explanation quality: Whether the platform showed which factors influenced predictions
Scalability: How well performance held up when I increased the dataset size or complexity
Integration options: Whether I could connect the tool to existing databases and business systems
Which predictive analytics software should you choose?
Your choice of predictive analytics software depends on your team's technical skills, the complexity of your forecasting needs, and how you plan to use the predictions.
Choose:
Julius if you need to analyze connected databases through natural language and want recurring reports that update automatically without writing SQL.
ThoughtSpot if your team needs fast answers during meetings and prefers searching for insights rather than building dashboards in advance.
Qlik Sense if you want to explore how data points relate to each other and need flexible dashboards that reveal unexpected connections.
Adobe Analytics if your primary goal is forecasting customer behavior, campaign performance, and conversion trends across digital channels.
SAP Analytics Cloud if your finance team needs to combine budgeting, scenario planning, and predictive forecasting in one platform.
Alteryx if your datasets arrive messy and need significant cleaning before you can build reliable predictive models.
SAS Viya if you want the platform to select and apply forecasting methods based on your data patterns with minimal manual setup
H2O.ai if you need to test multiple machine learning approaches and want transparency into which factors drive each prediction.
Azure Machine Learning if you need to deploy predictive models as services that other business applications can access across your organization.
My final verdict
I noticed teams gravitate toward ThoughtSpot or Qlik Sense when they need interactive exploration, while finance departments lean on SAP Analytics Cloud for integrated planning. Alteryx and H2O.ai fit technical users building custom workflows, and Azure Machine Learning serves enterprises that need production-grade deployment at scale.
Julius works differently by focusing on recurring analysis instead of one-off questions. You connect your databases, ask questions to get charts, then save those analyses as Notebooks that refresh on a schedule. I’ve found this setup helps business teams run forecasting queries and performance checks without rebuilding the same reports each week or waiting on analysts.
Want to run predictive analysis without writing SQL? Try Julius
The best predictive analytics software helps you forecast trends and model scenarios, but those insights depend on clean data analysis first. Julius helps teams explore metrics through natural language and build charts without writing SQL or code. It connects to your databases and files, then delivers visualizations, summaries, and recurring reports that support ongoing planning.
Here’s how Julius helps:
Smarter over time with the Learning Sub Agent: Julius's Learning Sub Agent learns your database structure, table relationships, and column meanings as you use it. With each query on connected data, it gets better at finding the right information and delivering faster, more accurate answers without manual configuration.
Quick single-metric checks: Ask for an average, spread, or distribution, and Julius shows you the numbers with an easy-to-read chart.
Built-in visualization: Get histograms, box plots, and bar charts on the spot instead of jumping into another tool to build them.
Catch outliers early: Julius highlights suspicious values and metrics that throw off your results, so you can make confident business decisions based on clean and trustworthy data.
Recurring summaries: Schedule analyses like weekly revenue or delivery time at the 95th percentile and receive them automatically by email or Slack.
One-click sharing: Turn a thread of analysis into a PDF report you can pass along without extra formatting.
Direct connections: Link your databases and files so results come from live data, not stale spreadsheets.
Ready to see how Julius can help your team make better decisions? Try Julius for free today.
