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Index GTM data and infrastructure Automation & iPaaS › Celigo vs Tray.ai
Workflow automation and iPaaS · September 2026 Edition

Celigo vs Tray.ai

One of twelve models named Celigo first on the direct prompt; zero named Tray.ai. Celigo was named by eleven of the twelve models and Tray.ai by eight and Celigo carries 18 labels and Tray.ai 23, so the shares are not directly comparable.

Celigo

accepted challenger

Named in one category this edition.

Tray.ai

accepted challenger

Named in one category this edition.

First-choice share12%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%13%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#7A position in a field of 12; printed, not drawn.
Labels1823A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Celigo reading right to left. Rank and label count are printed, not drawn.Workato was named alongside these two in ten of the twelve direct answers. Workato vs Celigo · Workato vs Tray.ai · Make vs Celigo

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve 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 workflow automation and ipaas page.

By framing

How many of the twelve models made each the first choice, per way of asking, and how many argued against it.
CeligoFirst choices, of twelve modelsTray.ai
Direct10
Paraphrase50
Comparative00
Budget-constrained00
Scale-constrained02
Negative003 against Tray.ai
Bars are first choices, 0 to 12 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to twelve.

Across every category in the September 2026 Edition, Celigo and Tray.ai were named in the same answer ten times, of the 34 answers naming Celigo and the 44 naming Tray.ai. In those answers Tray.ai took the first choice one time and Celigo four.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where Celigo and Tray.ai 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
Celigo Tray.ai first choice named as an alternative argued againstblank: not namedEach cell is one answer, Celigo on the left and Tray.ai on the right.

The direct prompt

The plain question, one answer per model, grouped by where Celigo and Tray.ai stood in it.

Celigo first, Tray.ai an alternative

1 of 12 modelsTray.ai was named in the answer but not as the choice, or not at all.
DeepSeek V4 FlashCeligo, Workato alternatives: Boomi, Jitterbit, Tray.ai

Neither was the first choice, one was named

4 of 12 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashWorkato alternatives: Celigo, Make, Tray.ai
Grok 4.1 FastWorkato alternatives: Boomi, Celigo, Make, Tray.ai
Mistral SmallWorkato alternatives: Jitterbit Harmony, Tray.ai
Qwen 3.7 FlashMake alternatives: Tray.ai, Workato

Neither was named

7 of 12 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Workato alternatives: Kissflow, Make, Microsoft Power Automate, Zapier
GPT-5.4 miniWorkato alternatives: Boomi, MuleSoft
Perplexity SonarWorkato alternatives: HubSpot Operations Hub, Make, n8n
Llama 4 MaverickBoomi, SnapLogic, Workato
Kimi K2Make, n8n alternatives: Workato, Zapier
GLM 4.7 FlashXMake alternatives: n8n
MiniMax M2.5Boomi, SnapLogic alternatives: Make, Microsoft Power Automate

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
Celigo leads by two points.
Celigo2%#– of 8
Tray.ai0%#– of 8
The full small business standing →
Mid-marketThe figures above
Celigo leads by eight points.
Celigo12%#4 of 12
Tray.ai4%#7 of 12
The full mid-market standing →
Enterprise
Celigo leads by two points.
Celigo4%#– of 11
Tray.ai2%#7 of 11
The full enterprise standing →

What the models said about Celigo

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

“Best Overall Balance: Celigo ... strikes a perfect middle ground between ease of use and power.” Qwen 3.7 Flash · paraphrase prompt · first choice
“Choose Celigo if you want faster time-to-value and lower operational overhead” DeepSeek V4 Flash · direct prompt · first choice
“Celigo: Best Overall for ERP-Centric & B2B E-Commerce Operations” Gemini 3.5 Flash · paraphrase prompt · first choice

What the models said about Tray.ai

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

“Avoid MuleSoft, Workato, and Tray.io (unless you are a major enterprise)” Gemini 3.5 Flash · negative prompt · hard negative
“powerful, but often overkill for smaller teams, and pricing/implementation complexity can be a major caution” Perplexity Sonar · negative prompt · soft negative
“Entirely quote-based pricing with no self-serve option—estimated $595+/month starting point” Kimi K2 · negative prompt · soft negative
“Tier 1 Middleware platforms (like Workato, Tray.io, or Microsoft Fabric)” Qwen 3.7 Flash · scale prompt · first choice
“Default choice: Workato or Tray.ai” Kimi K2 · scale prompt · first choice
“Choose Workato/Tray.io if you are a large company connecting CRMs to ERPs, require audit trails” Qwen 3.7 Flash · comparative prompt · alternative
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.