Streaming platforms · October 2026 Edition
Apache Pulsar vs Google Cloud Pub/Sub
Zero of fourteen models named Apache Pulsar first on the direct prompt; zero named Google Cloud Pub/Sub. Apache Pulsar was named by thirteen of the fourteen models and Google Cloud Pub/Sub by thirteen and Apache Pulsar carries 33 labels and Google Cloud Pub/Sub 30, so the shares are not directly comparable.
Named in two categories 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 rate21%23%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#7#8A position in a field of 11; printed, not drawn.
Labels3330A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Apache Pulsar 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 Apache Pulsar · Confluent Cloud vs Google Cloud Pub/Sub · Redpanda vs Apache Pulsar
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.
Apache PulsarFirst choices, of fourteen modelsGoogle Cloud Pub/Sub
Direct001 against Google Cloud Pub/Sub
Paraphrase00
Comparative001 against Google Cloud Pub/Sub
Budget-constrained11
Scale-constrained001 against Apache Pulsar · 1 against Google Cloud Pub/Sub
Negative006 against Apache Pulsar · 4 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, Apache Pulsar and Google Cloud Pub/Sub were named in the same answer fifty-one times, of the 83 answers naming Apache Pulsar and the 98 naming Google Cloud Pub/Sub. In those answers Google Cloud Pub/Sub took the first choice one time and Apache Pulsar zero.
Every model, every framing
The eighty-four answers behind the chart above, one cell each: where Apache Pulsar 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 | | | |
Apache Pulsar Google Cloud Pub/Sub first choice named as an alternative argued againstblank: not namedEach cell is one answer, Apache Pulsar on the left and Google Cloud Pub/Sub on the right.The direct prompt
The plain question, one answer per model, grouped by where Apache Pulsar and Google Cloud Pub/Sub stood in it.
Neither was the first choice, one was named
4 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
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
10 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
GPT-6 LunaConfluent Cloud alternatives: Amazon MSK, Azure Event Hubs, Redpanda
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 six points.
Google Cloud Pub/Sub6%#3 of 16Apache Pulsar0%#16 of 16Mid-marketThe figures above
Level: the same share of first choices.
Apache Pulsar2%#7 of 11Google Cloud Pub/Sub2%#8 of 11Enterprise
Level: the same share of first choices.
Apache Pulsar0%#9 of 10Google Cloud Pub/Sub0%#7 of 10What the models said about Apache Pulsar
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: ... self‑hosted Pulsar (unless you’re a large enterprise)” GLM 4.7 FlashX · negative prompt · hard negative
“Pulsar introduces unnecessary operational overhead” Gemini 3.5 Flash · negative prompt · hard negative
“Apache Pulsar is not recommended for small-scale applications or those without strict multi-tenancy needs.” Llama 4 Maverick · negative prompt · soft negative
“## Top Recommendation: Apache Pulsar (Self-Hosted)” GLM 4.7 FlashX · budget prompt · first choice
“A cloud-native, multi-tenant event streaming platform designed for high scalability, geo-replication, and flexible messaging patterns.” Muse Glimmer 30B · comparative prompt · alternative
“Geo-replication, tiered storage, multi-tenancy, separate compute/storage | Higher resource use (3 layers), smaller ecosystem” Grok 4.1 Fast · comparative 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 Apache PulsarConfluent Cloud vs Google Cloud Pub/SubRedpanda vs Apache PulsarRedpanda vs Google Cloud Pub/SubApache Kafka vs Apache PulsarApache Kafka vs Google Cloud Pub/SubYouTube Live vs Apache PulsarYouTube Live vs Google Cloud Pub/SubAmazon MSK vs Apache PulsarAmazon MSK vs Google Cloud Pub/SubAiven for Apache Kafka vs Apache PulsarAiven for Apache Kafka 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.