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Streaming platforms · November 2026 Edition

Apache Kafka vs Amazon Kinesis Data Streams

One of fourteen models named Apache Kafka first on the direct prompt; zero named Amazon Kinesis Data Streams. Both were named by all fourteen models and Apache Kafka carries 51 labels and Amazon Kinesis Data Streams 43, so the shares are not directly comparable.

Apache Kafka

criticized challenger

Named in three categories this edition.

Amazon Kinesis Data Streams

criticized challenger

Named in two categories this edition.

First-choice share7%3%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate33%26%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#8A position in a field of 12; printed, not drawn.
Labels5143A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Apache Kafka 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 Apache Kafka · Confluent Cloud vs Amazon Kinesis Data Streams · 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.
Apache KafkaFirst choices, of fourteen modelsAmazon Kinesis Data Streams
Direct102 against Apache Kafka
Paraphrase00
Comparative110
Budget-constrained304 against Apache Kafka · 1 against Amazon Kinesis Data Streams
Scale-constrained022 against Apache Kafka · 2 against Amazon Kinesis Data Streams
Negative109 against Apache Kafka · 8 against Amazon Kinesis Data Streams
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, Apache Kafka and Amazon Kinesis Data Streams were named in the same answer 100 times, of the 163 answers naming Apache Kafka and the 119 naming Amazon Kinesis Data Streams. In those answers Amazon Kinesis Data Streams took the first choice seven times and Apache Kafka twenty-eight.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Apache Kafka and Amazon Kinesis Data Streams stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
Apache Kafka Amazon Kinesis Data Streams first choice named as an alternative argued againstblank: not namedEach cell is one answer, Apache Kafka on the left and Amazon Kinesis Data Streams on the right.

The direct prompt

The plain question, one answer per model, grouped by where Apache Kafka and Amazon Kinesis Data Streams stood in it.

Apache Kafka first, Amazon Kinesis Data Streams an alternative

1 of 14 modelsAmazon Kinesis Data Streams was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5Apache Kafka alternatives: Aiven for Apache Kafka, Amazon Kinesis Data Streams, Confluent Cloud

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.
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
Qwen 3.7 FlashConfluent Cloud alternatives: Amazon Kinesis Data Streams, Amazon MSK, Azure Event Hubs, Google Cloud Pub/Sub

Neither was named

9 of 14 modelsThe answer made no first choice from these two in this category.
Grok 4.20Confluent Cloud alternatives: Amazon MSK, Azure Event Hubs, Google Cloud Pub/Sub, Redpanda
Mistral SmallAiven for Apache Kafka, Redpanda alternatives: Airmeet, Livestorm, Sequel
DeepSeek V4 FlashConfluent Cloud alternatives: Aiven for Apache Kafka, Amazon MSK, Redpanda
Llama 4 MaverickAirmeet alternatives: Goldcast, Livestorm
Kimi K2Demio alternatives: Confluent Cloud, Contrast, Goldcast, Livestorm, Redpanda
GLM 4.7 FlashXConfluent Cloud alternatives: Amazon MSK, Apache Pulsar, Google Cloud Pub/Sub
MiniMax M2.5Bizzabo alternatives: Livestorm, ON24
GPT-6 LunaConfluent Cloud alternatives: Amazon MSK, Redpanda
Muse Glimmer 30BConfluent Cloud alternatives: 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
Amazon Kinesis Data Streams leads by three points.
Amazon Kinesis Data Streams5%#4 of 13
Apache Kafka2%#9 of 13
The full small business standing →
Mid-marketThe figures above
The order flips: Apache Kafka leads at mid-market.
Apache Kafka7%#3 of 12
Amazon Kinesis Data Streams3%#8 of 12
The full mid-market standing →
Enterprise
Apache Kafka leads by six points.
Apache Kafka8%#2 of 9
Amazon Kinesis Data Streams2%#5 of 9
The full enterprise standing →

What the models said about Apache Kafka

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 Self-Hosting Kafka: The total cost of ownership (TCO) of engineer hours spent troubleshooting Kafka brokers... far exceeds the cost of a managed vendor.” Gemini 3.5 Flash · direct prompt · hard negative
“Why cautious/avoid: Extremely high operational complexity—requires deep expertise in clustering, KRaft mode, tuning, monitoring, scaling, and disaster recovery.” Grok 4.20 · negative prompt · soft negative
“Caution Due to Operational Complexity: Apache Kafka (and Confluent)... You shouldn't necessarily "avoid" it, but you must be extremely cautious.” Qwen 3.7 Flash · negative prompt · soft negative
“Apache Kafka (self-managed or open-source) is generally the best event streaming platform for a company with a limited budget.” Grok 4.20 · budget prompt · first choice
“The open-source standard for durable, replayable event streams... You want control, portability, and a widely used foundation” GPT-6 Luna · comparative prompt · first choice
“Apache Kafka (The Industry Standard) ... Choose Kafka if you need the most mature ecosystem, highest throughput ceiling” Kimi K2 · comparative prompt · first choice

What the models said about Amazon Kinesis Data Streams

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
“Avoid managed cloud services (AWS Kinesis, Azure Event Hubs, Confluent Cloud) if budget is tight” Kimi K2 · budget prompt · hard negative
“internal tech-radar assessments note deprecation” Muse Glimmer 30B · negative prompt · hard 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
“Start with managed Kafka (Confluent Cloud or MSK) for 6-12 months to validate use cases” GLM 4.7 FlashX · scale prompt · first choice
“AWS-native workloads | Amazon Kinesis | Natural fit when applications, security, and operations already center on AWS.” Perplexity Sonar · direct 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.