An alternative to Fireworks AI.
Build with a French cloud partner.

01 / A partner behind the API

Bring French cloud expertise into your AI stack.

Blade is Shadow’s AI infrastructure brand. Shadow is a French company registered in Paris, identified in our service terms. Blade’s own inference GPUs are operated by Shadow in Europe. That combines a French cloud partner with a concrete European hosting option for Blade-hosted models.

The team brings ten years of cloud experience to the service. Running GPU workloads at scale means managing capacity, availability and responsiveness as everyday engineering work. You can read more about the cloud experience behind Blade.

Years of cloud experience

10

Instances per year

16M

GPUs in the fleet

12,000+

These figures describe Shadow’s cloud operations, the experience behind Blade.

Fireworks also offers European deployment options: its multi-region deployment documentation includes a EUROPE scope. Blade’s argument is therefore the combination of a French service provider and Shadow-operated European inference GPUs, rather than exclusive access to European hosting.

Company origin and processing location answer different questions. External model providers and optional tools can follow separate paths; our hosting and data handling guide explains what is documented. Assess the selected services and contractual terms for your project rather than treating a European GPU location as a guarantee about every data flow.

02 / A familiar workflow

Start with the feature you want to ship.

With Models, your team can choose a hosted model and use the OpenAI SDK setup guide to make a first call. Blade manages the inference infrastructure, leaving you to work on prompts, retrieval, answer quality and the application experience.

Fireworks Serverless Inference also removes GPU sizing and scaling from the hosted-model workflow. Its on-demand deployments address dedicated capacity and custom-model needs. The useful comparison is which catalogue, request interface and operating model fit the feature you are building.

On Blade, you can test supported models through one API entry point. Keep a small evaluation set that includes difficult inputs, long prompts and tool use, so your choice reflects application behaviour rather than a single successful demo.

03 / Tools that serve the product

Evaluate what happens around the model, too.

Web search by Flash by Staan lets supported Blade models retrieve current information and return source annotations. Blade runs the built-in search loop on enabled accounts, reducing the orchestration your application has to implement.

For a research or support assistant, that gives you a concrete workflow to assess: the question, retrieved sources and final answer. The API also offers web fetch and code execution with the relevant permissions. Read the search integration, check account access and include separately billed searches in your estimate.

These are reasons to test Blade’s implementation on your task. They do not imply that another inference platform cannot be connected to tools or a search service.

04 / Compare the whole request

Match spending to the work that needs doing.

Models publishes model rates and has no subscription or minimum spend. Eligible models have dynamic rates: when a batch of documents or evaluations can wait, you can schedule it around lower prices. Our dynamic pricing guide explains that mechanism.

Fireworks distinguishes serverless per-token billing from dedicated GPU capacity. Compare equivalent workloads, including quality, response times, caching where applicable and optional services. A cheaper token or GPU-hour does not automatically produce a cheaper completed task.

Blade and Fireworks AI at a glance.

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