What challenges did Knowunity face before AI implementation?

Predictable, high-throughput GenAI performance during traffic spikes without capacity constraints. Production AI workloads needed guaranteed latency and controllable capacity.

What was the impact of the AI solution?

Knowunity reported processing over 1 million tokens per second at peak. Reve said its most critical interactive features became over twice as fast. Freepik said PT enabled predictable and controllable global scaling with better cost management.

Source

GCP

Provisioned Throughput on Vertex AI enables predictable GenAI performance

Use case typeTraining infrastructure modernizationUpdated Feb 19, 2026

Google Cloud’s Provisioned Throughput on Vertex AI gives customers reserved resources for predictable GenAI capacity and performance. The article highlights several named implementations, including Knowunity, Palo Alto Networks, Reve AI, Juicebox, and Freepik, using PT with Gemini models and other supported models to scale production workloads with confidence.

Organization: Knowunity
Industry: Education
Location: United States
Published: February 2026

Reported outcomes

+100%

interactive feature speedupTime & speed

1,000,000 tokens per secondpeak token throughput

Strategic outcomes

Scale & capacityGuaranteed production scale for GenAI workloadsCustomer experience & trustPredictable latency for production AI apps

Catalog median for time & speed deployments: +80% across 203 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 2 of 2

1Training infrastructure modernization
2Operations optimization

Predictable, high-throughput GenAI performance during traffic spikes without capacity constraints.
Production AI workloads needed guaranteed latency and controllable capacity.

Use Vertex AI Provisioned Throughput to reserve guaranteed compute and throughput.
Apply PT across model portfolios, including Gemini models, Anthropic models, and open models.
Isolate reservations per use case and use flexible term lengths and scheduled change orders to manage peak demand.

Technologies

Vertex AI Vertex AI Provisioned Throughput Gemini models Anthropic models Gemini Live API

Knowunity reported processing over 1 million tokens per second at peak.
Reve said its most critical interactive features became over twice as fast.
Freepik said PT enabled predictable and controllable global scaling with better cost management.

Architecture

The article describes reserved capacity on Vertex AI Provisioned Throughput for production GenAI workloads. Customers use PT to secure guaranteed throughput and latency, isolate reservations per use case, manage multiple model families in a single console workflow, and schedule capacity changes ahead of demand spikes.

Sources & evidence1

Groundedness: 4/5Type: Blog PostPublished: Feb 19, 2026Publisher: Google CloudEvidence: VendorConfidence: Medium

Primary source

AI-generated summary. Verify important details with the linked sources before relying on this case.

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Customer evidence