Streaming platforms · November 2026 Edition
Redpanda vs Google Cloud Pub/Sub
One of fourteen models named Redpanda first on the direct prompt; zero named Google Cloud Pub/Sub. Redpanda was named by fourteen of the fourteen models and Google Cloud Pub/Sub by thirteen and Redpanda carries 54 labels and Google Cloud Pub/Sub 36, so the shares are not directly comparable.
Named in one category this edition.
Named in one category this edition.
First-choice share7%5%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate7%19%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#4A position in a field of 12; printed, not drawn.
Labels5436A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Redpanda reading right to left. Rank and label count are printed, not drawn.Confluent Cloud was named alongside these two in eleven of the fourteen direct answers. Confluent Cloud vs Redpanda · Confluent Cloud vs Google Cloud Pub/Sub · Redpanda vs Apache Kafka
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.
RedpandaFirst choices, of fourteen modelsGoogle Cloud Pub/Sub
Direct10
Paraphrase00
Comparative001 against Redpanda
Budget-constrained221 against Google Cloud Pub/Sub
Scale-constrained111 against Google Cloud Pub/Sub
Negative103 against Redpanda · 5 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 November 2026 Edition, Redpanda and Google Cloud Pub/Sub were named in the same answer seventy-seven times, of the 153 answers naming Redpanda and the 112 naming Google Cloud Pub/Sub. In those answers Google Cloud Pub/Sub took the first choice four times and Redpanda twelve.
Every model, every framing
The eighty-four answers behind the chart above, one cell each: where Redpanda 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 4.20 | | | | | | |
| 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 | | | | | |
Redpanda Google Cloud Pub/Sub first choice named as an alternative argued againstblank: not namedEach cell is one answer, Redpanda on the left and Google Cloud Pub/Sub on the right.The direct prompt
The plain question, one answer per model, grouped by where Redpanda and Google Cloud Pub/Sub stood in it.
Redpanda first, Google Cloud Pub/Sub not the choice
1 of 14 modelsGoogle Cloud Pub/Sub was named in the answer but not as the choice, or not at all.Mistral SmallAiven for Apache Kafka, Redpanda alternatives: Airmeet, Livestorm, Sequel
Neither was the first choice, one was named
10 of 14 modelsThe answer put something else first and named one of the two as an alternative.GPT-5.4 miniConfluent Cloud alternatives: Apache Kafka, Redpanda
Gemini 3.5 FlashConfluent Cloud alternatives: AWS EventBridge, Aiven for Apache Kafka, Amazon Kinesis Data Streams, Google Cloud Pub/Sub, Redpanda, Upstash Kafka
Perplexity SonarConfluent Cloud alternatives: Amazon Kinesis Data Streams, Azure Event Hubs, Bizzabo, Estuary, Redpanda
Grok 4.20Confluent Cloud alternatives: Amazon MSK, Azure Event Hubs, Google Cloud Pub/Sub, Redpanda
DeepSeek V4 FlashConfluent Cloud alternatives: Aiven for Apache Kafka, Amazon MSK, Redpanda
Qwen 3.7 FlashConfluent Cloud alternatives: Amazon Kinesis Data Streams, Amazon MSK, Azure Event Hubs, Google Cloud Pub/Sub
Kimi K2Demio alternatives: Confluent Cloud, Contrast, Goldcast, Livestorm, Redpanda
GLM 4.7 FlashXConfluent Cloud alternatives: Amazon MSK, Apache Pulsar, Google Cloud Pub/Sub
GPT-6 LunaConfluent Cloud alternatives: Amazon MSK, Redpanda
Muse Glimmer 30BConfluent Cloud alternatives: Redpanda
Neither was named
3 of 14 modelsThe answer made no first choice from these two in this category.Claude Haiku 4.5Apache Kafka alternatives: Aiven for Apache Kafka, Amazon Kinesis Data Streams, Confluent Cloud
Llama 4 MaverickAirmeet alternatives: Goldcast, Livestorm
MiniMax M2.5Bizzabo alternatives: Livestorm, ON24
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
Redpanda leads by twenty points.
Redpanda26%#2 of 13Google Cloud Pub/Sub7%#3 of 13Mid-marketThe figures above
Redpanda leads by two points.
Redpanda7%#2 of 12Google Cloud Pub/Sub5%#4 of 12Enterprise
Redpanda leads by two points.
Redpanda2%#4 of 9Google Cloud Pub/Sub0%#8 of 9What the models said about Redpanda
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Review the license carefully if you plan to build a service on top of it or depend on enterprise features.” GPT-6 Luna · negative prompt · soft negative
“Apache Kafka (including Confluent Cloud, MSK, Redpanda, etc.) ... Can become complex to operate at scale” GLM 4.7 FlashX · negative prompt · soft negative
“check its documented compatibility limits against your specific Kafka features and clients” GPT-6 Luna · comparative prompt · soft negative
“Redpanda Cloud: Kafka-compatible, C++ architecture (no Java JVM tuning), ultra-fast performance, simple operations, cost-effective for high throughput.” Gemini 3.5 Flash · scale prompt · first choice
“Needs Kafka API + Lowest Infra Cost | Redpanda (Community) | Saves up to 60–80% on compute vs Kafka; no JVM.” Gemini 3.5 Flash · budget prompt · first choice
“Aiven or Redpanda – These are often cited as the best choices for small to mid-market companies” Mistral Small · direct prompt · first choice
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.
“Avoid or rethink Kinesis (legacy API) and Pub/Sub if you need long‑term replay or durable event logs.” GLM 4.7 FlashX · negative prompt · hard negative
“What to Avoid on a Tight Budget ... Google Pub/Sub” Grok 4.20 · budget prompt · hard negative
“If it uses a proprietary API (like AWS Kinesis or GCP Pub/Sub), are you comfortable with vendor lock-in and potential limitations in client library features?” Gemini 3.5 Flash · scale prompt · soft negative
“All-in on one hyperscaler with a lean platform team → Native cloud service Kinesis, Event Hubs, Pub/Sub.” Muse Glimmer 30B · scale prompt · first choice
“Google Cloud: Pub/Sub is a strong low-operations option for variable workloads.” GPT-6 Luna · budget prompt · first choice
“🏆 Top Pick: Google Cloud Pub/Sub” DeepSeek V4 Flash · budget prompt · first choice
Also compared
Confluent Cloud vs RedpandaConfluent Cloud vs Google Cloud Pub/SubRedpanda vs Apache KafkaRedpanda vs NATS JetStreamRedpanda vs Amazon MSKRedpanda vs Aiven for Apache KafkaRedpanda vs Amazon Kinesis Data StreamsApache Kafka vs Google Cloud Pub/SubGoogle Cloud Pub/Sub vs NATS JetStreamGoogle Cloud Pub/Sub vs Amazon MSKGoogle Cloud Pub/Sub vs Aiven for Apache KafkaGoogle Cloud Pub/Sub vs Amazon Kinesis Data StreamsAll 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.