Stop managing GPUs.
Start shipping AI
Keep full control of your models. Stop owning the capacity planning, routing, upgrades, and on-call that come with running them at scale.
Self-managed inference is often the right place to start. Then production traffic grows, customers depend on it, and the serving layer becomes its own product: capacity planning, routing, replicas, cache behavior, model upgrades, incidents, and cost visibility.
Token Factory helps teams map what should stay self-managed, what should move, and what can be optimized for production.
What we evaluate
The criteria we use to map your inference architecture.
Why teams move
Self-managed inference scales into platform work. A custom serving stack gives control at the beginning. At scale, it can become a second roadmap that competes with the product roadmap. Teams usually feel it first through:
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Capacity planning that keeps changing
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Latency targets that are hard to hold
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Model upgrades that create risk
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Missing cost and usage visibility
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Support handoffs across infra, product, and customer teams
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On-call load from serving problems that are not core product differentiation
How the review works
Share the current serving stack, model path, runtime choices, and traffic shape.
Map latency targets, quality constraints, cache behavior, capacity, and cost drivers.
Decide what should stay self-managed, what should move, and what should be optimized.
Leave with a practical endpoint and operations recommendation.
What you get
Leave with a clear production recommendation.
What you bring
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Current serving stack
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Model and runtime choices
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Token volume and request shape
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p95/p99 latency targets
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Cache and prompt reuse
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Quality constraints
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Region and capacity needs
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Support and reliability requirements
What you receive
A workload-specific recommendation for the best production inference path on Token Factory.
Built for production inference paths
Token Factory supports teams moving from experimentation to production, with OpenAI-compatible APIs, shared and dedicated endpoint paths, open-model access where supported, and engineering guidance for production workloads.
OpenAI-compatible APIs, shared and dedicated endpoint paths, and a solutions engineer on the architecture review — so you can move one workload without re-platforming.
Questions and answers
No. The first step is deciding what your team should keep, what should move, and what should be optimized.
Your inference stack should not become the roadmap
Bring one workload. We will help map the right production path.