8 Best SAS Alternatives for Statistics and Analytics in 2026

Your SAS license is up for renewal, the one person who actually wrote SAS code left in June, and nobody wants to sign another eye-watering invoice to keep the lights on. Sound familiar?

I spent the last few weeks testing the tools people move to when they leave SAS, grouped by the real reason they're leaving: cost, hiring, a pile of legacy code nobody wants to rewrite, or a move to the cloud. Some are free. One runs your existing SAS programs as they are. My top pick, Julius, lets you ask your analysis in plain English on your own data, no code needed.

Below is an honest, ranked shortlist of 8 SAS alternatives, grouped so you can match a tool to your situation, plus a comparison table so you can skip straight to the one that fits.

The 8 best SAS alternatives, ranked

Here's the full ranking, with a one-line "best for" each. The sections after this group them by why you'd leave SAS, so you can jump to your situation.

1. Julius: plain-English analysis on your own data, no code.
2. Python: free, open-source stats and data science at scale.
3. R: free statistics with publication-grade graphics.
4. Stata: point-and-click plus scripted stats and econometrics.
5. IBM SPSS Statistics: menu-driven statistics, no programming needed.
6. Rapidminer SLC: the only pick that runs your existing SAS code.
7. Databricks: enterprise-scale data and AI on a lakehouse.
8. Alteryx: code-free, repeatable analytics workflows.

Why look for SAS alternatives?

SAS is a serious tool, and this isn't a hit piece. But a few things push teams to look for SAS alternatives, and they tend to cluster into the same handful of reasons.

Cost and licensing: SAS is closed-source and license-gated. Without a current license, the tools simply stop working, and renewals are rarely cheap.

Hiring for SAS skills: getting value out of SAS means writing SAS code, and that talent pool is shrinking as universities teach Python and R first.

Learning curve: SAS statements and procedures take real time to learn, which slows down anyone new to the team.

Modern-workflow fit: default SAS graphics and batch jobs can feel dated next to cloud notebooks and AI tools you talk to in plain English (which is where a lot of analysis is heading).

Each of those maps to one of the groups below. If cost is your issue, start with the open-source pair. If it's the code you can't lose, jump to Rapidminer SLC.

SAS alternatives compared at a glance

Here's every pick side by side. The one column SAS refugees care about most is "Runs SAS code," and only one tool can say yes.

*Pricing correct as of October 2026. Verify with vendor.

Free and open-source code: Python and R

If cost is the reason you're leaving SAS, this is your group. Both tools are free, open-source, and license-free, and together they answer most "open source SAS" and "SAS alternatives free" searches. The trade is that both are code-first, so you'll be writing scripts.

Python: free, open-source stats and data science at scale

Python is my #2 pick and the first place I send anyone who wants to leave SAS without paying for anything. It's a free, open-source language stewarded by the Python Software Foundation, and the current series is Python 3.14 (3.14.8 at the time of writing, with 3.13 still in active maintenance).

The catch: Python on its own doesn't do statistics. The power lives in its libraries. pandas handles the data wrangling, statsmodels covers classical statistical modeling, and scikit-learn brings machine learning, all built on the NumPy and SciPy numeric foundation. Together they replace a large chunk of what SAS procedures do.

Key features:

• pandas, statsmodels, and scikit-learn: a stats and data-science stack covering regression, modeling, and ML.
• PyPI: hundreds of thousands of installable packages for almost any analysis.
• Notebooks and scripts: work interactively in Jupyter or write repeatable scripts.

✅ Completely free, with no license to renew and no seats to count. ✅ Skills transfer everywhere; Python is one of the most-taught languages in the world. ❌ You write code. There's no point-and-click interface out of the box. ❌ Setup and version management (environments, package conflicts) can trip up beginners.

Best for: analysts and engineers who are comfortable writing code and want free analysis at scale. If you're newer to it, our guide to Python for statistical analysis walks through the basics.

Pricing: free and open source under the PSF license. See the downloads.

Runs SAS code: No. Python is a general-purpose language, not a SAS runtime.

Bottom line: If budget is the reason you're leaving SAS and you don't mind code, Python is hard to beat.

R: free statistics with publication-grade graphics

R is my #3 pick and the tool statisticians reach for when they want free software built for statistics first. It's open-source under the GNU GPL, maintained by the R Foundation, with R 4.6.1 "Happy Hop" as the current stable release (4.6.2 is due at the end of October 2026).

Where Python is a general language that learned statistics, R was born for it. The graphics are publication-ready, which is why so many academic papers run their charts through it. Most people pair it with RStudio, the IDE from Posit (the company formerly called RStudio, PBC).

Key features:

• CRAN: a huge archive of statistical packages for almost any method.
• Publication-grade graphics: ggplot2 and base graphics produce journal-ready figures.
• RStudio and Posit: the standard IDE for writing and running R.

✅ Free and open source, with the deepest library of statistical methods anywhere. ✅ Chart quality that's tough to match without paying. ❌ Steeper learning curve, and it's code-first with no native point-and-click mode. ❌ Package and version management takes some care on bigger projects.

Best for: researchers, academics, and statisticians who want free stats and great charts. If you're weighing it against similar tools, we compare the best alternatives to R separately.

Pricing: free and open source under the GNU GPL. See the R project.

Runs SAS code: No.

Bottom line: For publication-quality statistics on zero budget, R is the classic answer.

Point-and-click statistics: Stata and IBM SPSS Statistics

Not everyone leaving SAS wants to trade one programming language for another. If you came to SAS for statistics and want to keep clicking through menus to run them, this is your group. Both tools below give you a real graphical interface, with the option to script when you want to.

Minitab sits in this same point-and-click camp, so if that's your world, our guide to Minitab alternatives covers a few more options.

Stata: point-and-click and scripted stats for researchers

Stata is my #4 pick and a favorite among economists and applied researchers. It gives you both worlds: point-and-click menus for quick work, and reproducible do-files when you need your analysis to run the same way twice. The current release is Stata 19, made by StataCorp.

Pricing is a flat annual license, and it's far gentler than enterprise SAS. For a US single-user business license, Stata/SE runs $1,015 per year, while the multi-core Stata/MP editions start at $1,195 per year and scale up with cores (as of October 2026). Academic and student pricing is much lower.

Key features:

• Dual interface: menus and dialogs plus a command language.
• Do-files: reproducible scripts so your analysis repeats cleanly.
• Deep methods: panel data, survival analysis, Bayesian, SEM, and time series.

✅ Reproducible by design, which researchers and reviewers love. ✅ Clear, published pricing at a fraction of enterprise SAS. ❌ It's still a paid license with its own command language to learn. ❌ The cheapest edition (Stata/BE) caps how big your datasets can be.

Best for: academics, research teams, and applied economists who want point-and-click plus scripted, reproducible statistics.

Pricing: Stata/SE from $1,015/yr; Stata/MP from $1,195/yr (US single-user business, as of October 2026). See Stata's pricing.

Runs SAS code: No.

Bottom line: The sweet spot between a friendly interface and serious, reproducible statistics.

IBM SPSS Statistics: menu-driven statistics, no programming needed

IBM SPSS Statistics is my #5 pick and probably the most recognizable name in menu-driven statistics. You click through dialogs to run tests, and the current release (v32) adds an AI Output Assistant, powered by watsonx.ai, that translates results into plain-language summaries. It won a 2026 TrustRadius Buyer's Choice Award in the statistical analysis category.

Pricing is a subscription, per authorized user, and this is where you'll want to pay attention. The base starts at $99 per user per month (as of October 2026), and the capabilities most researchers actually need live in add-ons that start at $79 per month each.

Key features:

• Point-and-click dialogs: run most tests without writing a line of code.
• AI Output Assistant: plain-language explanations of your results (v32, watsonx.ai).
• Add-on modules: advanced stats, forecasting, decision trees, and complex sampling.

✅ Easy for non-coders; the menu workflow is decades-polished. ✅ Integrates with R and Python when you outgrow the menus. ❌ Costs stack fast once you add the modules that hold the key features. ❌ Add-ons gate capabilities you might assume are in the base.

Best for: social-science and market researchers, and enterprises that want menu-driven statistics with no coding. If it's not quite right, we also round up the best IBM SPSS alternatives.

Pricing: base from $99/user/mo plus add-ons from $79/mo each, as of October 2026. See IBM's pricing.

Runs SAS code: No. SPSS uses its own syntax.

Bottom line: The friendliest on-ramp for people who want statistics without code, as long as you budget for the add-ons.

Keep your existing SAS code: Julius and Rapidminer SLC

This group is for anyone sitting on years of SAS programs they can't afford to rewrite. There are two ways through it. You can skip the old code entirely and ask your analysis in plain English on your own data (that's Julius, and it's why it's my overall #1). Or you can run your actual SAS-language programs as they are, on a platform that isn't SAS (that's Rapidminer SLC, the only tool here that does it). This group picks up a lot of "SAS code alternative" searches, so it's worth going deep.

Julius: plain-English analysis on your own data

Julius is my top pick, and I'll be upfront that I'm biased: we built it. We don't fake-review our own product, so here's what Julius does and who it's for, with an honest limitation at the end.

We built Julius as an AI-native data analysis tool, a pocket data scientist you talk to in plain English. For someone leaving SAS, the wedge is simple: upload your dataset or connect your own data (Postgres, Snowflake, BigQuery, and more), then ask your question in plain English and get the analysis back, no code required. You can also start without a file at all, using general data search and our Financial Datasets integration covering roughly 17,000 companies.

Key features:

• Plain-English analysis: ask a question and get charts, tables, and models back.
• Data connectors: link Postgres, Snowflake, BigQuery, and your files directly.
• Semantic Layer: Julius learns your tables and columns over time for sharper answers.
• Scheduled runs: schedule an analysis to re-run on your data so results stay current.

✅ No code and almost no learning curve; the barrier that frustrates SAS teams mostly disappears. ✅ Sandboxes that retry and fix their own errors, so you get a working result, not a single guess. ❌ On open-ended prompts, AI output isn't deterministic; scheduled, saved analyses keep a repeatable version. ❌ It won't run your existing SAS programs line for line. For that, Rapidminer SLC is the next entry.

Best for: non-technical business users (marketers, finance, ops, and PMs) who want SAS-style analysis on uploaded or connected data by asking in plain English.

Pricing: credit-based, starting with a Free tier ($0, daily refresh credits), then Plus at $20/mo ($16/mo annual), Pro at $45/mo, Max at $200/mo, and Business at $450/mo (as of October 2026). SOC 2 Type II compliant. See Julius pricing.

Runs SAS code: No. Julius is natural-language chat over your data.

Bottom line: If your real goal is answers from your data and not the SAS syntax itself, this is the fastest path there.

Rapidminer SLC: the only pick that runs your SAS code as-is

Rapidminer SLC (formerly Altair SLC, and before that WPS Analytics) is my #6 pick, and it's the one tool in this list that runs your existing SAS-language programs natively. It's now owned by Siemens, which acquired Altair Engineering in a deal worth around $10 billion that closed in early 2025 and folded the product into the Rapidminer brand.

This is the payoff of the whole group: Rapidminer SLC runs programs written in the SAS language without translation and without any SAS license. Its compiler supports SAS-language and macro syntax, and it runs Python, R, and SQL alongside them in the same workbench.

Key features:

• SAS-language compiler: runs your existing SAS programs and macros as they are.
• Multi-language workbench: SAS language, Python, R, and SQL side by side.
• Flexible deployment: on-premises, cloud, hybrid, and even mainframe.

✅ The only pick here that preserves legacy SAS code without a SAS license. ✅ Modern IDE (Rapidminer Analytics Workbench) with both code and drag-and-drop. ❌ Enterprise, contact-sales pricing with no public list price. ❌ It's a migration and modernization tool, so it won't give you plain-English analysis.

Best for: enterprises migrating off SAS that must keep legacy SAS-language code running without paying for SAS.

Pricing: contact sales for a quote; a free Personal Edition exists for individuals who want to learn or evaluate it. See Siemens' page.

Runs SAS code: Yes. This is the only tool in the list that does.

Bottom line: If rewriting thousands of lines of SAS is a non-starter, this is your escape hatch.

Enterprise data platforms: Databricks and Alteryx

If you're replacing SAS at the scale of a whole organization, you're probably looking at a platform. These two show up most often when big teams move SAS workloads onto modern cloud tooling.

Databricks: enterprise-scale data and AI on a lakehouse

Databricks is my #7 pick and a common enterprise destination for teams moving large SAS workloads to the cloud. It's a cloud lakehouse platform where data teams run notebooks, SQL, and pipelines over very large datasets, across AWS, Azure, and Google Cloud.

Pricing is consumption-based and billed per Databricks Unit (DBU), with no up-front cost. Data engineering starts around $0.15 per DBU and data warehousing from $0.22 per DBU (as of October 2026). There's a free Community Edition for learning and a free trial to test it.

Key features:

• Lakehouse architecture: one platform for data engineering, warehousing, and AI.
• Multi-language notebooks: Python, SQL, Scala, and R.
• Jobs and pipelines: schedule and automate production workloads.

✅ Built to handle data volumes that overwhelm desktop tools. ✅ Free Community Edition and trial make it easy to start. ❌ Consumption pricing is hard to predict before you're running real workloads. ❌ It's built for data teams, so non-technical users will feel lost.

Best for: data teams and platform engineers moving SAS workloads onto a modern cloud lakehouse.

Pricing: pay-as-you-go from about $0.15/DBU (data engineering); free Community Edition and trial (as of October 2026). See Databricks' pricing.

Runs SAS code: No.

Bottom line: Overkill for a solo analyst, but a serious destination for enterprise SAS workloads.

Alteryx: code-free, repeatable analytics workflows

Alteryx is my #8 pick and the most visual option on this list. You build analytics and data-prep workflows by dragging and connecting blocks on a canvas, no code required, then schedule them to run on their own. It's now private under Clearlake Capital, which took it private in 2024, and the platform is repackaged as "Alteryx One."

Pricing starts with the Starter edition at $250 per user per month, billed annually, with Professional and Enterprise editions on contact-sales (as of October 2026). A 30-day free trial is available.

Key features:

• Drag-and-drop Designer: build data-prep and analytics workflows visually.
• 100+ connectors: pull from Snowflake, Databricks, and more on higher tiers.
• Automation: schedule and repeat workflows without rebuilding them.

✅ Code-free, so analysts can automate without engineering help. ✅ Repeatable workflows that document themselves as you build. ❌ Per-user cost adds up quickly across a team. ❌ Most of the depth sits behind contact-sales tiers.

Best for: analyst teams that want code-free, repeatable data-prep and analytics workflows.

Pricing: Starter $250/user/mo billed annually; Professional and Enterprise contact sales; 30-day free trial (as of October 2026). See Alteryx's pricing.

Runs SAS code: No.

Bottom line: The friendliest way to automate analytics work, if you can fit the per-seat cost.

Should you stay on SAS?

To be fair to SAS, leaving isn't the right call for everyone. SAS earned its place for real reasons: it's a well-documented language, it covers an enormous range of procedures, and its vendor support and validation matter in industries where an audit is always around the corner.

Stick with SAS if you work in a heavily regulated field (pharma, banking, or government), you have a large validated estate of production code, or you need the compliance guarantees and vendor support that come with a commercial contract. For those teams, the switching cost often outweighs the savings.

And if you like SAS but want something more modern, SAS sells its own paths. SAS Viya is its cloud, pay-as-you-go platform, and JMP is its point-and-click statistical tool. Both keep you in the SAS family while moving you off older setups, which is why I've kept them out of the ranked list above.

How to choose a SAS alternative

The right SAS alternative depends mostly on your reason for leaving. Match your situation to a group:

Choose open-source (Python or R) if you: want zero license cost and don't mind writing code. For a wider look at the field, our roundup of statistical analysis tools compares more options side by side.

Choose point-and-click stats (IBM SPSS or Stata) if you: came to SAS for statistics and want to keep clicking through menus.

Choose Rapidminer SLC if you: have legacy SAS code you can't rewrite and need to run it without a SAS license.

Choose Databricks or Alteryx if you: are replacing SAS at enterprise scale on modern cloud platforms.

Choose Julius if you: want answers from your data in plain English, fast, with no code to write or manage.

Run SAS-style analysis by asking in plain English

Picking between these SAS alternatives usually comes down to one question: do you want to manage a tool, or just get answers from your data? If it's the second, that's exactly what we built Julius to do.

Here's how Julius helps:

• General data search: ask a question and let Julius pull public data, no upload needed.
• Financial Datasets: tap institutional-grade financials for roughly 17,000 companies.
• Private data connectors: link Postgres, Snowflake, BigQuery, and your own files.
• Built-in visualization: get charts, shareable reports, and scheduled runs in one place.

Ready to skip the SAS syntax and go straight to the insight? Try Julius for free today.

Frequently asked questions

Is SAS still relevant in 2026?

Yes, SAS is still relevant in 2026, especially in regulated industries that value its reliability, validation, and compliance support. Its share is shrinking elsewhere as open-source languages (Python and R) and cloud platforms take over, but it's far from gone.

Does anyone use SAS anymore?

Yes, plenty of organizations still use SAS, particularly in pharmaceuticals, banking, and government, where validated legacy systems are expensive to replace. Usage is receding in other sectors as teams move to Python, R, and cloud data platforms.

What is comparable to SAS?

The closest tools to SAS span four groups: Python and R for free, code-based analysis; IBM SPSS Statistics and Stata for point-and-click statistics; Rapidminer SLC for running SAS code directly; and Databricks and Alteryx for enterprise-scale data work. Julius covers the plain-English analysis angle with no code.

Who are SAS's main competitors?

SAS's main competitors include Databricks, Alteryx, IBM SPSS Statistics, and Stata, along with the open-source pair Python and R. Which one fits depends on whether you need statistics, enterprise data work, or plain-English analysis.

Is there a free version of SAS?

Yes, with caveats: SAS OnDemand for Academics is free but limited to academic use. For genuinely free analysis, the open-source options are Python and R, Databricks offers a free Community Edition, and Julius has a free credit-based tier.