Fireworks AI 推出 GLOBAL 多区域部署：生产环境请求成功率提升至 99.992%

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PUBLISHED 9/28/2026

TLDR; Discover when to leverage the GLOBAL option and how we architected multi-region routing to handle global workload placement off the inference hot path, optimizing for low latency, resilience and operational ease.

No single region is an infinite GPU pool.

Fireworks has a full spectrum of serving options, and operates on a global scale across 30+ regions across dozens of cloud providers in one of the industry’s largest independent GPU fleets. Yet, a massive global footprint only protects your uptime if your scheduler is actually empowered to use it. Pinning a workload to a single region imposes an artificial capacity ceiling. When regional resources are tight, the next available, compatible GPU might sit in a completely different data center across the US, Europe, Canada or APAC.

Traditionally, teams are forced to manually provision redundant multi-region endpoints, patch together custom regional routers, and constantly juggle failover logic. Fireworks has introduced support for GLOBAL option for you to run your workload across geographies. This keeps your architecture unified under one deployment identity and one endpoint, removing the operational burden of managing a global map.

Fireworks engineered multi-region placement and routing so that global scheduling never becomes the bottleneck of your real-time inference pipelines.

When your workload is marked GLOBAL , the scheduler dynamically maps requests across the broadest possible eligible capacity pool while strictly respecting your unique parameters:

You maintain complete governance and single-endpoint simplicity, while Fireworks absorbs the complexity of navigating global infrastructure.

With Fireworks, we didn’t have to trade latency for reliability. Running across multiple clusters allowed us to adapt to regional variability through autoscaling and ensured a consistently smooth experience. Even after expanding to multi-region, our response times remain industry-leading.

— Ishan Gupta , CEO of Juicebox

Figure 1: Multi-Region Boundaries 99.99% Success Rate in Production In a seven-day production study, the signals pointed in the direction the architecture was designed for: the globally placeable cohort was associated with higher ready-node fulfillment, and a realized multi-cluster footprint was associated with fewer responses per request.

In the external-customer-labeled, globally placeable cohort, deployment-days observed in 2+ serving clusters, request success rate went from 99.269% for a single-region deployment to 99.992% on a multi-region deployment. This helps us achieve our 99.99% reliability target for globally placeable customer deployments.

These are observational signals, not an A/B test or a universal uptime promise. Serving-cluster labels are infrastructure clusters, not normalized regions; traffic was concentrated in a few accounts and paired-account outcomes were mixed. Sensitivity checks preserved the aggregate direction, but did not make it causal.

The numbers describe why the project matters. The implementation story started with an equation that our architecture could not solve.

An inference deployment asks for eight nodes. Virginia has four eligible nodes slots. Iowa has four more.

The fleet has eight. Our legacy scheduler still rejects the deployment.

It is behaving correctly. That is the problem.

The scheduler is not asking, “Does the fleet have eight eligible nodes?” It is asking, “Does one region have all eight?” Neither does, so the deployment ends in RESOURCE_EXHAUSTED .

Copy 1 2 legacy : max ( 4 , 4 ) < 8 → reject global : 4 + 4 = 8 → place

Four plus four was zero.

Figure 2: Four Plus Four Illustration

The constraint was built into the architecture: each deployment belonged to a single region.

With the new architecture, one deployment can draw compatible capacity from multiple regions through a single endpoint. The scheduler respects hardware requirements, regional limits, reliability policies, and geographic boundaries.

Fireworks plans placement ahead of traffic, so requests do not need to consult the scheduler or probe every region.

Network distance still matters: a nearby gateway may route to compute farther away. This design avoids a per-request global routing step; it does not eliminate geographic latency.

Locality and data-residency requirements may call for US, EUROPE, or APAC. For workloads without those requirements, GLOBAL gives the scheduler the most room to find compatible capacity or replan a stuck node when compatible supply exists elsewhere in scope.

Figure 3: Choose the Boundary You Need You choose the boundary. The scheduler finds a viable plan. The gateway keeps one endpoint attached to it.

Four plus four is eight again.