
Platforms engineered to handle spikes, high TPS, and large transaction volumes with stability.
Which brand leads in AI visibility and mentions.
Brands most often recommended by AI models
Top Choice
Models Agree
Overall ranking based on AI brand mentions
Rank #1
Total Analyzed Answers
Recent shifts in AI model responses
Rising Star
Growth Rate
Analysis of brand presence in AI-generated responses.
Brands ranked by share of AI mentions in answers
Visibility share trends over time across compared brands
Key insights from AI Apps comparisons across major topics
Kubernetes emerges as the leading concept for scaling, with models emphasizing its architectural robustness over raw hardware as critical for scalability.
Gemini shows a preference for Kubernetes with a 2% visibility share, highlighting its architecture as key to scaling due to its orchestration capabilities. Its tone is positive, focusing on Kubernetes’ ability to manage distributed systems efficiently.
ChatGPT equally favors Kubernetes and AWS at 2.8% visibility share each, underscoring Kubernetes’ architectural flexibility for scaling over hardware dependency, while AWS is noted for infrastructure support; the tone is positive and pragmatic.
Perplexity does not focus on scaling-relevant brands like Kubernetes or AWS, instead emphasizing consumer platforms like Airbnb (1.6%); its neutral tone lacks direct engagement with architecture or hardware for scaling.
Grok prioritizes Kubernetes (2.4%) alongside Google and Netflix, valuing its architectural framework for scaling over raw hardware like NVIDIA (1.2%); its tone is positive, emphasizing ecosystem integration and scalability potential.
Deepseek leans toward Kubernetes (1.6%) as central to scaling, citing its architectural design for container management over hardware-centric solutions; the tone is positive, focusing on practical adoption in distributed environments.
Shopify emerges as the leading platform for reliability under load during sales spikes like Black Friday, owing to its consistently high visibility and positive sentiment across most models for handling high-traffic events.
ChatGPT strongly favors Shopify with an 11% visibility share, likely due to its reputation for scalable infrastructure during high-traffic sales events. Its tone is positive, emphasizing platforms like Cloudflare (7.9%) and Fastly (6.7%) for their CDN capabilities, which ensure uptime under load.
Gemini shows a balanced view with Shopify and Google tied at 3.5% visibility share, suggesting reliability through robust infrastructure, while AWS (3.1%) is noted for enterprise-level stability. The tone is neutral, focusing on technical capacity without strong advocacy for a single platform.
Perplexity leans toward AWS, Google, Flipkart, and Walmart (each at 2.8% visibility share) for their ability to manage massive traffic spikes during sales, with Shopify (1.6%) less prominent. Its tone is neutral, prioritizing diverse ecosystem strength over a standout brand.
DeepSeek aligns Shopify and Cloudflare (both at 3.1% visibility share) as reliable under load, likely due to their scalable architectures suited for sales surges. The tone is positive, highlighting technical resilience with minimal skepticism.
Grok favors Shopify, Cloudflare, Google, and AWS (each at 3.1% visibility share) for their proven performance in high-demand scenarios like Black Friday. Its tone is positive, reflecting confidence in these platforms' ability to maintain uptime and user experience under pressure.
Amazon Web Services (AWS) emerges as the leading choice for migration concerning volume limitations due to its consistent visibility and perceived scalability across multiple models.
Grok shows a balanced view with AWS and Windows tied at a 3.1% visibility share, suggesting a preference for established platforms with robust infrastructure for handling volume constraints. Its neutral tone highlights AWS as a viable option for migration when scalability is a concern.
ChatGPT strongly favors AWS and Windows, both at a 5.9% visibility share, emphasizing their ecosystem maturity and user accessibility for managing large data volumes. Its positive tone underscores AWS as a reliable choice for migration under volume limitations.
Deepseek leans slightly toward AWS with a 1.2% visibility share, focusing on its innovative cloud solutions for handling volume-intensive workloads. Its neutral tone indicates a practical, albeit understated, endorsement of AWS for migration needs.
Perplexity gives limited insight with AWS and IBM at a modest 0.4%-0.8% visibility share, reflecting a cautious stance on migration solutions for volume issues. Its skeptical tone suggests no strong preference, though AWS remains visible.
Gemini ranks AWS highest at a 2% visibility share, pointing to its strong adoption patterns and ecosystem support for volume-heavy migrations. Its positive tone positions AWS as a leading contender for addressing volume limitations effectively.
Visa emerges as the leading gateway for handling 10,000+ TPS reliably today, due to its consistent visibility across models and proven track record in high-throughput transaction environments.
Perplexity shows a balanced view with Paytm and Hedera leading at 2.4% visibility share, though no clear favorite for high TPS emerges. Its neutral tone suggests a focus on diverse options without explicit endorsement for reliability at scale.
Grok favors Stripe and AWS at 3.5% visibility share, closely followed by Visa and Google at 3.1%, indicating a preference for established tech and payment giants likely capable of handling high TPS with a positive sentiment tone.
Gemini highlights Google and AWS at 3.1% visibility share, alongside API gateway solutions like Envoy and Kong, reflecting a neutral tone focused on technical infrastructure rather than direct TPS reliability for payment gateways.
ChatGPT strongly favors Visa with a 6.7% visibility share, far ahead of others, suggesting confidence in its ability to manage high TPS reliably, reinforced by a positive tone emphasizing scalability and established infrastructure.
Deepseek gives Visa a 2% visibility share, the highest among listed brands, with a neutral tone indicating a mild preference for its reliability at high TPS but lacking strong emphasis compared to niche blockchain solutions.
Gatling emerges as the leading tool for simulating load testing on new payment platforms due to its consistent visibility and perceived reliability across multiple models.
ChatGPT favors Gatling with a visibility share of 10.6%, emphasizing its robust capabilities for load testing simulations for payment platforms. Its tone is positive, highlighting Gatling’s scalability and detailed reporting features as critical for pre-launch testing.
Gemini shows a balanced view with Gatling and Apache Ignite both at 3.5% visibility, but leans slightly toward Gatling for its focus on performance testing tailored to high-transaction environments like payment platforms. The tone is neutral, reflecting a practical assessment of tools without strong advocacy.
DeepSeek equally favors Gatling and Apache Ignite at 3.1% visibility, appreciating Gatling for its ease of scripting load tests for payment systems under stress. Its tone is positive, underscoring the tool's relevance for ensuring platform stability before launch.
Perplexity gives Gatling a modest 1.2% visibility share but still positions it as a viable option for load testing due to its community support and versatility for payment platform scenarios. The tone is neutral, focusing on functionality without significant enthusiasm.
Grok prioritizes Gatling and Apache Ignite, both at 3.9% visibility, with a strong endorsement of Gatling for its integration capabilities in simulating real-world payment traffic. The tone is positive, reflecting confidence in Gatling’s ability to handle complex load scenarios.
Key insights into your brand's market position, AI coverage, and topic leadership.
Top systems aim for >10,000 TPS, with peaks into tens of thousands. :contentReference[oaicite:1]{index=1}
Card networks (Visa, Mastercard), Alipay, UPI are benchmarks in architecture. :contentReference[oaicite:2]{index=2}
Using horizontal scaling, caching, queuing, distributed architecture & regional nodes.
Yes — design, throughput, failover, redundancy matter more than raw CPU.
Top platforms negotiate better rates and optimize routing to keep marginal cost low.