AI Indexes
Finance AI Index
Index › Spend and procurement › Expense management › Ramp vs Rippling Spend
Expense management · October 2026 Edition

Ramp vs Rippling Spend

Seven of fourteen models named Ramp first on the direct prompt; two named Rippling Spend. Ramp was named by fourteen of the fourteen models and Rippling Spend by thirteen and Ramp carries 53 labels and Rippling Spend 24, so the shares are not directly comparable.

Ramp

endorsed leader

Named in seventeen categories this edition.

Rippling Spend

accepted challenger

Named in three categories this edition.

First-choice share35%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate8%8%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#7A position in a field of 11; printed, not drawn.
Labels5324A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Ramp reading right to left. Rank and label count are printed, not drawn.Ramp vs Zoho Expense · Ramp vs Expensify · Ramp vs BILL Spend & Expense

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the expense management page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
RampFirst choices, of fourteen modelsRippling Spend
Direct72
Paraphrase70
Comparative50
Budget-constrained30
Scale-constrained10
Negative204 against Ramp · 2 against Rippling Spend
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Ramp and Rippling Spend stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
Claude Haiku 4.5
GPT-5.4 mini
Gemini 3.5 Flash
Perplexity Sonar
Grok 4.1 Fast
Mistral Small
DeepSeek V4 Flash
Llama 4 Maverick
Qwen 3.7 Flash
Kimi K2
GLM 4.7 FlashX
MiniMax M2.5
GPT-6 Luna
Muse Glimmer 30B
Ramp Rippling Spend first choice named as an alternative argued againstblank: not namedEach cell is one answer, Ramp on the left and Rippling Spend on the right.

The direct prompt

The plain question, one answer per model, grouped by where Ramp and Rippling Spend stood in it.

Ramp first, Rippling Spend an alternative

7 of 14 modelsRippling Spend was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5Ramp, Spendesk alternatives: Airbase, BILL Spend & Expense, Brex, Navan
Gemini 3.5 FlashRamp alternatives: Airbase, Brex, Navan, Rippling Spend
DeepSeek V4 FlashAirbase, Ramp alternatives: Brex, Payhawk
Qwen 3.7 FlashBrex, Ramp alternatives: BILL Spend & Expense, Expensify, Rydoo
Kimi K2Expensify, Ramp alternatives: Brex, Emburse
GLM 4.7 FlashXAirbase, Ramp alternatives: Expensify, Rippling Spend, SAP Concur, Zoho Expense
GPT-6 LunaRamp alternatives: Brex, Navan, SAP Concur

Rippling Spend first, Ramp an alternative

2 of 14 modelsRamp was named in the answer but not as the choice, or not at all.
GPT-5.4 miniRippling Spend alternatives: Brex, Navan, Payhawk, Ramp, SAP Concur
MiniMax M2.5Expensify, Rippling Spend, SAP Concur alternatives: Airwallex, BILL Spend & Expense, Navan, Paylocity, Ramp

Neither was the first choice, one was named

3 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Grok 4.1 FastNavan alternatives: Expensify, Rippling Spend
Mistral SmallAirbase alternatives: BILL Spend & Expense, Eco, Ramp
Muse Glimmer 30BNavan, SAP Concur alternatives: Airbase, Paylocity, Rippling Spend, Rydoo

Neither was named

2 of 14 modelsThe answer made no first choice from these two in this category.
Perplexity SonarPayhawk alternatives: BILL Spend & Expense, Expensify, Procurify, SAP Concur
Llama 4 Maverickno first choice

Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.

By buyer segment

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Ramp leads by twenty-five points.
Ramp25%#2 of 8
Rippling Spend0%#7 of 8
The full small business standing →
Mid-marketThe figures above
Ramp leads by thirty-one points.
Ramp35%#1 of 11
Rippling Spend4%#7 of 11
The full mid-market standing →
Enterprise
Ramp leads by fifteen points.
Ramp15%#2 of 10
Rippling Spend0%#9 of 10
The full enterprise standing →

What the models said about Ramp

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.

“Ramp or Brex (Best for Card-First, Modern Spend Management): Free platform fees, incredibly fast to set up, highly modern UI, and top-tier AI receipt matching.” Gemini 3.5 Flash · scale prompt · first choice
“Ramp (Best Overall Free Option) ... Go with Ramp. It will save your team dozens of hours of manual receipt-chasing, and you will pay $0 in software fees.” Gemini 3.5 Flash · budget prompt · first choice

What the models said about Rippling Spend

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.

“Reviews of broad platforms like Rippling note the initial setup can feel like a larger undertaking than a standalone expense tool.” GPT-5.4 mini · negative prompt · soft negative
“Rippling Spend if you want the best balance of usability, automation, and internal-system integration.” GPT-5.4 mini · direct prompt · first choice
“Rippling Spend (if you want a unified HR/expense platform)” MiniMax M2.5 · direct prompt · first choice
Also compared

Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.