Streaming platforms · October 2026 Edition
Amazon MSK vs Google Cloud Pub/Sub
Zero of fourteen models named Amazon MSK first on the direct prompt; zero named Google Cloud Pub/Sub. Amazon MSK was named by fourteen of the fourteen models and Google Cloud Pub/Sub by thirteen and Amazon MSK carries 42 labels and Google Cloud Pub/Sub 30, so the shares are not directly comparable.
Named in one category this edition.
Named in two categories this edition.
First-choice share2%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate10%23%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#5#8A position in a field of 11; printed, not drawn.
Labels4230A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Amazon MSK reading right to left. Rank and label count are printed, not drawn.Confluent Cloud was named alongside these two in nine of the fourteen direct answers. Confluent Cloud vs Amazon MSK · Confluent Cloud vs Google Cloud Pub/Sub · Redpanda vs Amazon MSK
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 streaming platforms page.
By framing
How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
Amazon MSKFirst choices, of fourteen modelsGoogle Cloud Pub/Sub
Direct001 against Amazon MSK · 1 against Google Cloud Pub/Sub
Paraphrase10
Comparative101 against Google Cloud Pub/Sub
Budget-constrained013 against Amazon MSK
Scale-constrained001 against Google Cloud Pub/Sub
Negative004 against Google Cloud Pub/Sub
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.
Across every category in the October 2026 Edition, Amazon MSK and Google Cloud Pub/Sub were named in the same answer fifty-nine times, of the 127 answers naming Amazon MSK and the 98 naming Google Cloud Pub/Sub. In those answers Google Cloud Pub/Sub took the first choice three times and Amazon MSK four.
Every model, every framing
The eighty-four answers behind the chart above, one cell each: where Amazon MSK and Google Cloud Pub/Sub stood in it.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| 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 | | | | |
Amazon MSK Google Cloud Pub/Sub first choice named as an alternative argued againstblank: not namedEach cell is one answer, Amazon MSK on the left and Google Cloud Pub/Sub on the right.The direct prompt
The plain question, one answer per model, grouped by where Amazon MSK and Google Cloud Pub/Sub stood in it.
Neither was the first choice, one was named
5 of 14 modelsThe answer put something else first and named one of the two as an alternative.Grok 4.1 FastConfluent Cloud alternatives: Amazon Kinesis Data Streams, Google Cloud Pub/Sub, Redpanda
Qwen 3.7 FlashConfluent Cloud, Redpanda alternatives: Amazon MSK, Azure Event Hubs, Google Cloud Pub/Sub
MiniMax M2.5Aiven for Apache Kafka, Redpanda alternatives: Apache Flink, Google Cloud Dataflow, Google Cloud Pub/Sub, Restream, WebEx
GPT-6 LunaConfluent Cloud alternatives: Amazon MSK, Azure Event Hubs, Redpanda
Muse Glimmer 30BApache Kafka, Confluent Cloud, Redpanda alternatives: Amazon MSK, Apache Pulsar, Azure Event Hubs, Google Cloud Pub/Sub, Vimeo Livestream, YouTube Live
Neither was named
9 of 14 modelsThe answer made no first choice from these two in this category.Claude Haiku 4.5Dacast alternatives: Accelevents, Bizzabo, Goldcast, Virtual PRO
GPT-5.4 miniAzure Event Hubs alternatives: Amazon Kinesis Data Streams, Apache Kafka, Confluent Cloud
Gemini 3.5 FlashRedpanda alternatives: Confluent Cloud, Demio, Goldcast, Livestorm, WarpStream
Perplexity SonarBizzabo alternatives: Dacast
Mistral SmallMicrosoft Teams Live Events, Zoom Events alternatives: Amazon Kinesis Data Streams, Apache Kafka, Cisco Webex Events, Vimeo Livestream
DeepSeek V4 FlashRedpanda alternatives: Amazon Kinesis Data Streams, Confluent Cloud
Llama 4 MaverickDacast alternatives: Contentflow, Kaltura
Kimi K2Confluent Cloud alternatives: Amazon Kinesis Data Streams, Redpanda
GLM 4.7 FlashXConfluent Cloud alternatives: Aiven for Apache Kafka
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
Google Cloud Pub/Sub leads by five points.
Google Cloud Pub/Sub6%#3 of 16Amazon MSK2%#9 of 16Mid-marketThe figures above
Level: the same share of first choices.
Amazon MSK2%#5 of 11Google Cloud Pub/Sub2%#8 of 11Enterprise
The order flips: Amazon MSK leads at enterprise.
Amazon MSK4%#5 of 10Google Cloud Pub/Sub0%#7 of 10What the models said about Amazon MSK
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“slashes infrastructure bills by up to 70% to 80% compared to standard Kafka or AWS MSK” Gemini 3.5 Flash · budget prompt · soft negative
“standard Kafka hosting (like Confluent Cloud or AWS MSK) can get expensive quickly” Qwen 3.7 Flash · budget prompt · soft negative
“Lock‑in to AWS; pricing can be opaque and tied to EC2/ELB costs.” GLM 4.7 FlashX · direct prompt · soft negative
“I\u2019d start with Redpanda Cloud or Amazon MSK Serverless\u2014both give you strong capability without the enterprise price tag” Kimi K2 · paraphrase prompt · first choice
“dominant standard, ~de facto leader with broad enterprise use ... For most enterprises, Kafka ecosystem reigns supreme.” Grok 4.1 Fast · comparative prompt · first choice
“If you are heavily invested in AWS, MSK Express delivers up to 3x higher throughput and integrates natively with AWS IAM.” Gemini 3.5 Flash · paraphrase prompt · alternative
What the models said about Google Cloud Pub/Sub
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“convenient "walled gardens." You should be cautious about locking yourself into these” Qwen 3.7 Flash · negative prompt · hard negative
“comes with heavy cloud lock-in and lacks the rich open-source ecosystem of Kafka (e.g., AWS Kinesis, GCP Pub/Sub)” Gemini 3.5 Flash · scale prompt · soft negative
“At-least-once delivery with no ordering guarantees — problematic for use cases that need strict ordering” DeepSeek V4 Flash · negative prompt · soft negative
“Google Cloud Pub/Sub is a strong budget-friendly default” GPT-6 Luna · budget prompt · first choice
“fully managed, globally distributed messaging and event ingestion service built for massive scale” Muse Glimmer 30B · comparative prompt · alternative
“Google Cloud Pub/Sub or Azure Event Hubs if you prefer a fully managed serverless posture” Muse Glimmer 30B · direct prompt · alternative
Also compared
Confluent Cloud vs Amazon MSKConfluent Cloud vs Google Cloud Pub/SubRedpanda vs Amazon MSKRedpanda vs Google Cloud Pub/SubApache Kafka vs Amazon MSKApache Kafka vs Google Cloud Pub/SubYouTube Live vs Amazon MSKYouTube Live vs Google Cloud Pub/SubAmazon MSK vs Aiven for Apache KafkaAmazon MSK vs Apache PulsarAiven for Apache Kafka vs Google Cloud Pub/SubApache Pulsar vs Google Cloud Pub/SubAll twenty-eight head-to-head pages and the full streaming standing
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.