I ditched SUMIF for SUMIFS in Excel—and my spreadsheets finally work the way I need them to


Excel’s SUMIF and SUMIFS functions both let you add up values that match criteria you specify. The problem is that many people treat them as separate tools: SUMIF for one condition, SUMIFS for multiple conditions.

I did the same for years until I realized SUMIFS works just as well with a single condition. Making the switch from SUMIF gave me formulas that are easier to expand, read, and maintain.

In this guide, I’ll use the simple sales tracker in the screenshot below, which is formatted as a table named T_SalesData. To follow along, download a free copy of the workbook. When you click the link, you’ll see the download button in the top-right corner of your screen.

A Microsoft Excel table with regions in column A, quarters in column B, and sales in column C.

SUMIFS works just as well with one criterion

Remember one syntax instead of two

Many Excel users think of SUMIF and SUMIFS as separate tools: SUMIF for simple calculations and SUMIFS for anything more complex. But that distinction isn’t necessary. SUMIFS can handle a single criterion just as easily as SUMIF, which means you don’t need to switch functions depending on how many conditions your calculation has.

The biggest practical difference when switching between the two functions is their argument order:

=SUMIF(range, criteria, [sum_range])

SUMIFS(sum_range, criteria_range1, criteria1, [criteria_range2, criteria2], ...)

SUMIF asks for the range being evaluated first, then the criteria, and finally the values to add. SUMIFS does the opposite, putting the sum range first and adding each range-criteria pair afterward. That small difference is what makes SUMIFS such a useful default. Once you learn the syntax, you only need to remember one formula structure instead of switching between two depending on the complexity of your calculation.

Using the T_SalesData table from above, after typing a region into cell F1, I would use this in cell F2:

=SUMIFS(T_SalesData[Sales], T_SalesData[Region], F1)

Once SUMIFS becomes your default, you no longer have to decide which function fits each calculation. You just use the same structure every time.

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SUMIFS grows with your spreadsheets

Add conditions without starting over

I found that my spreadsheets rarely stayed static. I’d start with one condition, then a few weeks later, I’d need to add another. That’s when switching back to SUMIF became a nuisance. If you build a calculation using SUMIF and your manager later asks to filter the results by an additional parameter, you end up rewriting the formula and rearranging the arguments.

This is where SUMIFS really pays off. Need another condition? Just add a comma to the end of the formula and append the new range-criteria pair.

If your initial single-criterion setup looks like this:

=SUMIFS(T_SalesData[Sales], T_SalesData[Region], F1)

You simply add the extra argument inside the parentheses:

=SUMIFS(T_SalesData[Sales], T_SalesData[Region], F1, T_SalesData[Quarter], F2)

When a formula starts to include several criteria pairs, press Alt+Enter while editing the formula to place each argument on a separate line. This makes it much easier to see which columns are being summed and which conditions are being applied.

The core of the formula stays exactly the same. You never have to switch tools or rearrange your existing arguments to accommodate new layers of information.

SUMIFS puts the important part first

See what’s being totaled immediately

Adding criteria to the end of a SUMIFS formula doesn’t just make it easier to expand—it also makes it easier to read. Placing the numbers you’re adding up at the very front of your formula makes it easier to audit formulas, troubleshoot errors, or hand files off to coworkers.

Compare these two formulas side by side:

=SUMIF(T_SalesData[Region], F1, T_SalesData[Sales])

versus

=SUMIFS(T_SalesData[Sales], T_SalesData[Region], F1)

With SUMIF, the range being added is buried at the end of the formula, after the criteria range and condition. When you open a workbook later, you have to scan through the arguments before you know what value is actually being calculated. SUMIFS fixes this by putting the sum range first. In the example above, T_SalesData[Sales] immediately tells you what the formula is totaling before you even look at the filters.

That small change makes formulas much easier to scan when you’re troubleshooting, reviewing someone else’s work, or revisiting a workbook months after creating it.

When SUMIF still makes sense

Your old friend still has its place

While I’d recommend SUMIFS almost every time, a few specific scenarios might require you to stick with SUMIF:

  • You’re maintaining an existing workbook: If a large workbook already uses SUMIF throughout, keeping the same formula style can make it easier for other people to understand and maintain. You also can’t use a simple Find and Replace to swap SUMIF for SUMIFS, because the two functions use different argument orders. Converting an existing workbook means reviewing and rebuilding each formula carefully.
  • You’re sharing workbooks with people using older Excel versions: SUMIFS was introduced in Excel 2007, so workbooks that need to run in Excel 2003 or earlier will need to use SUMIF instead.
  • You’re working with another tool that doesn’t support SUMIFS: Some older spreadsheet apps or systems that import Excel files may not recognize newer functions, making SUMIF the safer option.

Older Excel sheets also relied on SUMPRODUCT for multi-condition sums before SUMIFS existed. While SUMPRODUCT still has its place for more advanced array calculations, SUMIFS is generally the simpler, more readable, and easier-to-maintain choice for conditional sums.

Microsoft has never deprecated SUMIF, so it’s unlikely to disappear anytime soon. The point isn’t that it’s broken—it’s that SUMIFS has become the more practical default for new workbooks.


Out with the old, in with the new (most of the time)

Despite the plural “S” in its name, SUMIFS isn’t just for multiple conditions—it’s the formula I’d recommend using from the start. You’ll write formulas that are easier to expand, easier to read, and far less likely to need rewriting as your spreadsheets grow. But replacing SUMIF doesn’t mean every older Excel function deserves to be retired—some legacy Excel functions are still worth keeping. The key is knowing when a newer function genuinely improves your workflow and when the older option still has a place.



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TL;DR

India debates sovereign AI after the US forced Anthropic to kill Fable 5, with proposals for a $5B fund and calls to embrace open-source models.

When the US government ordered Anthropic to shut down Fable 5 and Mythos 5 on 12 June, the export control directive was aimed at restricting foreign nationals from accessing America’s most capable AI. In India, Anthropic’s second-largest market, it landed as a warning shot about what happens when your AI infrastructure runs on someone else’s politics.

The suspension cut off Indian developers and enterprises from Claude’s most advanced models overnight. India’s Claude run-rate revenue had doubled since October 2025, and Tata Consultancy Services had announced a partnership just one day earlier, on 11 June, to train 50,000 employees on Claude and build a dedicated Anthropic business unit. That deal is now in limbo.

The timing has turned what was already a simmering debate about AI sovereignty into a full strategic reckoning. Proposals that sounded ambitious a week ago now sound urgent.

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Mohandas Pai, former Infosys CFO and one of India’s most prominent tech investors, has called for a ₹50,000 crore (roughly $5 billion) annual sovereign AI fund. He has also proposed a ₹2 lakh crore (approximately $21 billion) credit guarantee to finance cloud infrastructure, hardware procurement, and semiconductor development. The figures dwarf the government’s existing commitment.

India approved its IndiaAI Mission in March 2024 with a budget of ₹10,372 crore, approximately $1.25 billion. The programme has deployed around 38,000 GPUs so far. Pai’s proposal would quadruple annual spending and add a credit backstop an order of magnitude larger.

Sridhar Vembu, the founder of Zoho, has gone further. He argued that India should embrace smaller and open-source models, including Chinese ones, rather than depend on American frontier systems that can be switched off by executive order. “Technology is the ultimate weapon,” Vembu said. “Globalization is dead and Bharat must find her own way ahead.

The argument has teeth because the suspension demonstrated exactly the vulnerability Vembu is describing. Amazon’s CEO reportedly triggered the government crackdown by telling Treasury Secretary Scott Bessent that researchers had used Fable 5 to obtain information that could be used in cyberattacks. Anthropic called the action disproportionate, but compliance was immediate and global.

Policy expert Prasanto Roy put it bluntly: “American AI models are bound to American geopolitics.” For Indian enterprises that had built workflows around Claude, the lesson was that access to frontier AI is a privilege that can be revoked without notice, without consultation, and without regard for the commercial relationships it disrupts.

The Indian startup ecosystem is already adapting. Sarvam, a Bengaluru-based AI company, released 30-billion and 105-billion parameter open-source models at the India AI Impact Summit in 2026. Krutrim, founded by Ola’s Bhavish Aggarwal, has pivoted from building foundational models to providing cloud and AI infrastructure services, reporting ₹3 billion in revenue for fiscal year 2026.

Neither company is close to matching the capabilities of Fable 5 or Mythos 5. But the argument for sovereign AI was never about matching frontier performance immediately. It is about ensuring that the floor does not fall out when Washington makes a unilateral decision about who gets to use which models.

Aakrit Vaish, founder of the AI startup Activate, said the suspension “completely changes things” for the sovereign AI debate. Vijay Rayapati, CEO of Atomicwork, raised concerns about what the precedent means for Indian companies with multi-country teams that depend on American AI providers. If the US can shut off model access to enforce export controls, any country that relies on American AI is one policy decision away from disruption.

Not everyone agrees that India needs to build its own frontier models. Hemant Mohapatra, a partner at Lightspeed Venture Partners, argued that talent and compute access matter more than capital for building competitive AI. India has the engineering workforce, but the compute gap is significant, and closing it requires either massive domestic investment or continued access to foreign cloud infrastructure.

Anthropic opened a Bengaluru office as part of its India expansion, and the TCS partnership was designed to be a cornerstone of its enterprise strategy in the country. Whether those plans survive the suspension intact depends on how quickly Anthropic can restore access and whether Indian enterprises still trust a provider whose most capable models can vanish overnight.

The broader pattern is unmistakable. The US has spent four years tightening controls on AI technology, from chip export restrictions to model-level interventions. Each escalation pushes more countries toward the conclusion that dependence on American AI infrastructure carries political risk. India, with its 1.4 billion people and rapidly growing technology sector, is now asking whether it can afford that risk, and what it would cost to eliminate it.

The Opendoor layoffs in June 2026, which shut the company’s India office and affected roughly 250 employees, added another dimension. CEO Kaz Nejatian cited AI-native teams as the reason, suggesting that some US companies are using AI to reduce their reliance on Indian engineering talent at the same time that India is debating its reliance on American AI. The relationship is becoming less complementary and more competitive.

For now, the sovereign AI proposals remain proposals. Pai’s fund has no legislative vehicle, Vembu’s call for open-source adoption has no coordinated policy framework, and the IndiaAI Mission’s GPU deployment is still in early stages.

But the Anthropic suspension has done something that years of policy papers and conference speeches could not: it has given the sovereign AI movement a concrete, recent, and viscerally felt example of why dependence on foreign AI is a strategic liability. The debate is no longer theoretical.



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