Fireworks

Serverless runs popular open models with pay-per-token billing and no infrastructure to manage. Fireworks offers Standard, Priority, and Fast paths, so you can trade cost against latency depending on the request. The OpenAI compatible endpoint means existing code often works with a changed base URL. It's the quickest way to test a model before you invest in a deployment.

When a shared endpoint isn't enough, on-demand deployments run your model on dedicated GPUs with fast autoscaling and short cold starts. You can tune a deployment for speed or throughput and serve multi-region. Reserved capacity goes further, guaranteeing throughput and higher quotas, and it gets you new hardware first. This is the layer production workloads settle into once usage is steady.

Fireworks handles supervised fine-tuning and reinforcement fine-tuning for models up to a trillion parameters and beyond. You can take a guided path where you describe the task and review a plan, configure a run yourself, or write your own training logic on the platform's GPUs. Every checkpoint deploys to production in seconds, which keeps the loop from training to serving short.

The catalog covers text, vision, audio, image, and embedding models, so you can analyze documents, generate images, and build retrieval on one platform. Vision models read images and documents, embeddings and reranking power search and context retrieval, and batch inference runs large async jobs at lower cost. For multi-model systems, that range comes from a single account and API.

Function calling connects a model to your tools and APIs, so it can look up data or trigger an action instead of only chatting. Structured outputs return reliable JSON that matches a schema you define, which is what agentic workflows need to move results around without parsing errors. Both run through the same endpoint, so you don't bolt on a second service to add tools.

FireAttention is the serving engine under the platform, tuned for throughput and latency while keeping model quality. Fireworks built it rather than relying on a general-purpose stack, and it's the reason the platform can serve open models fast enough for interactive products. For you, that shows up as lower latency and a lower cost per token on the same hardware.