Production inference for open models
Evaluate, deploy, optimize, and improve open models around the requirements of your production workload.
Choosing an open model is only the start. In production, the workload depends on the system around it: serving architecture, quality, latency, capacity, economics, and how model behavior improves over time.
Token Factory helps teams evaluate, deploy, optimize, and improve open models around the production workloads they actually run.
Where are you in the production AI journey?
Start with the workload that hurts most. Each path leads to one concrete next step — a review, a benchmark, or a workshop.
Stage
Your situation
Route to
What to do
Scaling inference
You already have production traffic, but serving infrastructure is taking too much roadmap.
Optimized Inference
Choosing open models
You are comparing open models, moving from closed APIs, or need evidence before shifting volume.
Open Models
Building model advantage
You have traces, evals, data, or domain workflows that could improve model behavior.
Build Custom AI
Stop managing GPUs Start shipping AI
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 roadmap: capacity, routing, replicas, cache behavior, model upgrades, incidents, and billing visibility.
Token Factory helps you map what should stay self-managed, what should move, and what can be optimized.
Best for:
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Teams running vLLM, SGLang, TensorRT-LLM, KServe, custom serving, or GPU fleets
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Teams with latency, reliability, capacity, or on-call pressure
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Teams that want model control without every serving problem becoming platform work
Your workload is the benchmark
Leaderboards help you choose what to test. They do not tell you which model clears your quality bar, fits your latency budget, works with your tools, scales with your traffic, and protects your margin.
Token Factory helps you benchmark open models against the workload you actually run.
Best for:
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Teams evaluating or migrating to open models
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Teams comparing open models, closed APIs, and custom weights
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Teams that need production evidence before moving volumework
Build model advantage from production signal
Generic models get everyone to the same starting line. The advantage starts when production traces become evals, datasets, tuned models, deployment decisions, and measurement loops.
Token Factory helps you map how product-specific data and workflows can become better quality, stronger economics, safer behavior, and differentiated user experience.
Best for:
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Teams with production traces, evals, failed tasks, or domain data
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Teams exploring fine-tuning, post-training, RFT, Custom Speculator, BYOW, or custom weights
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Teams where model behavior is part of the product experience
What you get
Control the parts of AI that decide whether production works.
How the process works
Share the workload, current model path, traffic shape, and production constraints.
Map the cost, latency, quality, capacity, and data-loop requirements.
Choose the right path: optimize inference, benchmark open models, or build custom AI.
Deploy, measure, and improve the workload over time.
Cut inference waste, not quality
A practical session on reducing AI inference cost without weakening the user experience.
Learn how to evaluate quality, latency, and cost together, and how to decide what to optimize, benchmark, or move to a production endpoint.
Questions and answers
Yes. The strongest fit is teams with real workloads, traffic, evals, deployment constraints, or production cost pressure.
Start with one workload
Bring the workload that matters most. We will help map the model, endpoint, optimization path, and data loop.