What challenges did Fireworks.ai face before AI implementation?

Making foundation-model inference widely available while keeping costs reasonable. Meeting enterprise security and performance requirements for production inference deployments. Reducing latency and increasing throughput for demanding generative AI workloads.

What was the impact of the AI solution?

The solution delivers four times higher throughput per instance than open-source solutions. Latency was cut by up to 50% for some customers. One customer reduced total costs by 4x and improved summarization latency by 30% to 50%. Sourcegraph doubled its completion acceptance rate and improved backend latency by more than 2x when using the platform.

Source

Fireworks.ai delivers 4x generative AI throughput and cuts latency using AWS EC2 P5 (NVIDIA H100)

Use case typeAI platformUpdated Jun 13, 2026

Fireworks.ai provides a fast, affordable, and customizable generative AI inference platform for developers and enterprises. The company uses AWS infrastructure to host and scale its inference service, including containerized deployments in customer virtual private clouds. It upgraded from Amazon EC2 P4d to Amazon EC2 P5 Instances powered by NVIDIA H100 Tensor Core GPUs to improve performance and cost efficiency.

Organization: Fireworks.ai
Industry: Tech & Comms
Location: United States
Published: April 2026

Reported outcomes

quantified impactTime & speed

−50%quantified impact4xcost−30%cost−50%cost

Strategic outcomes

New product / capabilityLaunched scalable generative AI inference platformCustomer experience & trustImproved enterprise inference performanceSpeed & agilityReduced latency for generative AI workloadsNew product / capabilityEnabled hosted and customer-VPC deployments

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

Primary read

Use case focus

Showing 3 of 4

1Generative AI inference
2Model hosting
3Model fine-tuning

Making foundation-model inference widely available while keeping costs reasonable.
Meeting enterprise security and performance requirements for production inference deployments.
Reducing latency and increasing throughput for demanding generative AI workloads.

Fireworks.ai built its generative AI inference solution on AWS and uses Amazon EC2 for secure, resizable compute capacity.
The company upgraded to Amazon EC2 P5 Instances powered by NVIDIA H100 Tensor Core GPUs to increase throughput and improve cost per request.
Its platform supports hosted generative AI SaaS and containerized deployments in customer VPCs, with support for open-source language, image, and multimodal foundation models.

Technologies

Amazon EC2 Amazon EC2 P5 Instances Amazon EC2 P4d Instances NVIDIA H100 Tensor Core GPUs

The solution delivers four times higher throughput per instance than open-source solutions.
Latency was cut by up to 50% for some customers.
One customer reduced total costs by 4x and improved summarization latency by 30% to 50%.
Sourcegraph doubled its completion acceptance rate and improved backend latency by more than 2x when using the platform.

Implementation partners1

NVIDIA

Sources & evidence1

Groundedness: 5/5

Primary source

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Customer evidence