| Category | Function | Share | Rank | Negative rate | Labels | Quadrant | Since September 2026 |
|---|---|---|---|---|---|---|---|
| ETL and ELT | Data platform | 15% | 3 of 75 | 0% | 29 | accepted challenger | ▲+11Since September 2026: 6% → 17%, +11 points. Past the 11-point floor: movement. Read over the models both editions asked. |
| Workflow orchestration | Data platform | 2% | 14 of 79 | 0% | 1 | under 10 labels · led by Prefect at 21% | newNew since September 2026: not ranked then, 2% now. |
| Streaming platforms | Data platform | 0% | 83 of 98 | 100% | 1 | under 10 labels · led by Confluent Cloud at 39% | newNew since September 2026: not ranked then, 0% now. |
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| ETL | 6% | 17% | ▲+11Since September 2026: 6% → 17%, +11 points. Past the 11-point floor: movement. Read over the models both editions asked. | Moved past the floor | Rank 4 → 3 of 75 |
| Orchestration | 0% | 2% | newNew since September 2026: not ranked then, 2% now. | New this edition | Rank 14 of 79, unchanged |
| Streaming | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 83 of 98, unchanged |
Shares here are read over the models both editions asked, so they can differ by a point or two from the standing above, which counts every model in this edition.
The floor is 11 points of share, measured: how far the models move a leader on their own when the same questions are asked twice with nothing changed. A larger change is movement; a smaller one is noise, and both are shown. Movement is read over the twelve models both editions asked; GPT-6 Luna, Muse Glimmer 30B joined this edition and are in the standing but not yet in the comparison. How the floor is measured · The editions
| Model | First choice | Alternative | Mention | Negative | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 2 | 0 | 1 | 0 | 3 |
| GPT-5.4 mini | 0 | 0 | 0 | 0 | 0 |
| Gemini 3.5 Flash | 1 | 2 | 0 | 0 | 3 |
| Perplexity Sonar | 1 | 1 | 1 | 0 | 3 |
| Grok 4.20 | 0 | 1 | 3 | 0 | 4 |
| Mistral Small | 0 | 1 | 1 | 1 | 3 |
| DeepSeek V4 Flash | 1 | 1 | 1 | 0 | 3 |
| Llama 4 Maverick | 1 | 0 | 0 | 0 | 1 |
| Qwen 3.7 Flash | 1 | 1 | 0 | 0 | 2 |
| Kimi K2 | 1 | 0 | 3 | 0 | 4 |
| GLM 4.7 FlashX | 1 | 1 | 2 | 0 | 4 |
| MiniMax M2.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-6 Luna | 0 | 0 | 0 | 0 | 0 |
| Muse Glimmer 30B | 0 | 1 | 0 | 0 | 1 |
Verbatim evidence the judge attached to positive labels.
“Choose Hevo Data if you want a "set it and forget it" experience with clear, low monthly pricing (~$239/mo) and minimal maintenance.” Qwen 3.7 Flash · ETL · budget prompt · first choice
“The best ETL tool for a company with a limited budget is Hevo Data, which offers usage-based pricing from $239/month” Llama 4 Maverick · ETL · budget prompt · first choice
“For most mid-market B2B companies, Hevo Data strikes the best balance of cost, ease of use, and capability.” DeepSeek V4 Flash · ETL · direct prompt · first choice
“Hevo is highly regarded as one of the most budget-friendly, fully managed ETL tools on the market” Gemini 3.5 Flash · ETL · budget prompt · first choice
Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.
“Hevo Data may be overkill and less reliable for high-throughput, real-time event streaming compared to more established platforms.” Mistral Small · Streaming · negative prompt · soft negative
Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 99 of the 99 answers that named Hevo Data and are not a share of its labels.
367 of the 448 domain citations in answers naming Hevo Data came from somebody else's page.
Pages are listed as the models cited them.
Search figures are US estimates from DataForSEO, read September 28, 2026; AI search demand is its modeled, directional estimate, not a count of queries to any assistant. The answers are this edition's. Two measurements side by side: neither is read as the cause of the other.
An email the morning each edition publishes: where this product moved, where it held, and by how much against the noise floor. One address, confirmed by a click; a stop link in every email.
Already following? Everything you follow, with a stop for each.
What Hevo Data's own pages state, read September 28, 2026: hevodata.com/streaming-elt, hevodata.com/hevo-vs-others, hevodata.com/cloud-etl-tool, hevodata.com/hevo-vs-airbyte, hevodata.com/hevo-engineering. A claimed page can correct any of them.
Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when Hevo Data's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as Hevo Data, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.
A new claim receives the current edition's vendor brief for Hevo Data by email, built from the raw record of the edition. It shows:
Hevo Data is among the products AI models recommend first in ETL and ELT this edition. Each badge says so in the buyer's words, names the edition, and links to the standing. The next edition issues a new badge; this one stays true as a record of October 2026.
Badges read on light and dark pages. Alt text carries the claim, the category, the edition and the index, so it stays a citation where the image does not load.