The infrastructure layer for continuously improving specialized intelligence
"Fireworks' Multi-LoRA capabilities align with Cresta's strategy to deploy custom AI through fine-tuning cutting-edge base models. It helps unleash the potential of AI on private enterprise data."
“Using Fireworks AI on Foundry, we can run repeatable, high-volume evaluations through a single Azure endpoint, which helps our team move faster from deployment to informed model decisions with more confidence.”
why did Cursor rollout Composer 2 with @FireworksAI_HQ?
"...because it's way more performant than the open source engines and is what we use in production. our rl inference scales elastically and globally because of it. when we have low prod traffic we scale up RL, when we have high prod traffic, we scale down RL."
"Vercel’s v0 model is a composite model. The SOTA in this space changes every day, so you don’t want to tie yourself to a single model. Using a fine-tuned reinforcement learning model with Fireworks, we perform substantially better than SOTA. In our evaluation, Sonnet 3.5 compiled at 62%, and we got our error-free generation rate well into the 90s."
"By partnering with Fireworks to fine-tune models, we reduced latency from about 2 seconds to 350 milliseconds, significantly improving performance and enabling us to launch AI features at scale. That improvement is a game changer for delivering reliable, enterprise-scale AI"
"Fireworks enabled us to own our AI journey, and unlock better quality in just four weeks."
"We've had a really great experience working with Fireworks to host open source models, including SDXL, Llama, and Mistral. After migrating one of our models, we noticed a 3x speedup in response time, which made our app feel much more responsive and boosted our engagement metrics."
"Fireworks has been a fantastic partner in building AI dev tools at Sourcegraph. Their fast, reliable model inference lets us focus on fine-tuning, AI-powered code search, and deep code context, making Cody the best AI coding assistant. They are responsive and ship at an amazing pace."
By running Fireworks AI on Azure Foundry, UiPath powers both Autopilot and Delegate with open models that are significantly faster and more cost-efficient for Computer Use, all while matching the quality of Claude's Sonnet 4.6. It's a step-change in how we deliver AI at scale to our customers.
“Fireworks has been an amazing partner getting our Fast Apply and Copilot++ models running performantly. They exceeded other competitors we reviewed on performance. After testing their quantized model quality for our use cases, we have found minimal degradation. Fireworks helps implement task specific speed ups and new architectures, allowing us to achieve bleeding edge performance!”
"Fireworks enabled us to own our AI journey, and unlock better quality in just four weeks. This resulted in a better user experience for our customers."
Fireworks AI on Microsoft Foundry gives us the inference throughput and latency we need to power Bolt at production scale and all within the Azure ecosystem.
"The rLLM team is dedicated to pushing the boundaries of autonomous AI, which means our time is best spent on innovation rather than managing backend clusters. The Fireworks Training SDK lets us focus on our research instead of wrestling with infrastructure. The platform is fast, well-optimized, and just works."
"Fireworks' Multi-LoRA capabilities align with Cresta's strategy to deploy custom AI through fine-tuning cutting-edge base models. It helps unleash the potential of AI on private enterprise data."
“Using Fireworks AI on Foundry, we can run repeatable, high-volume evaluations through a single Azure endpoint, which helps our team move faster from deployment to informed model decisions with more confidence.”
why did Cursor rollout Composer 2 with @FireworksAI_HQ?
"...because it's way more performant than the open source engines and is what we use in production. our rl inference scales elastically and globally because of it. when we have low prod traffic we scale up RL, when we have high prod traffic, we scale down RL."
"Vercel’s v0 model is a composite model. The SOTA in this space changes every day, so you don’t want to tie yourself to a single model. Using a fine-tuned reinforcement learning model with Fireworks, we perform substantially better than SOTA. In our evaluation, Sonnet 3.5 compiled at 62%, and we got our error-free generation rate well into the 90s."
