SERVE OPEN & CUSTOM MODELS IN YOUR CLOUD
Host AI models without building an inference platform from scratch
Run open-weight models like GLM-5.3, Deepseek v4 Pro, or Kimi K3 while keeping inference and data within your environment. Reduce the cost of model calls compared to running frontier closed state-of-the-art models and maintain data sovereignty.
Trusted by teams running AI/ML in production
One-click deploys for open models.
Chalk handles the orchestration, so you can launch models, scale with demand, and operate inference easily while retaining your control.
Control your inference costs.
Avoid dependency on an inference infrastructure provider, and meet performance, reliability, and cost targets without undifferentiated platform work. No per token markup, and inference is next to your data.
Own your data.
Inference, model weights, prompts, outputs, and traffic remain in your cloud environment. Run production AI in your own cloud, not somewhere else. Data doesn’t leave your cloud.
Manage and route inference across models.
Automatically route each task to the right model based on its complexity, optimizing for the best balance of cost and speed.
Serve models on the
AI data platform for inference.
Compute
Run inference workloads on serverless or self-hosted infrastructure. Chalk handles the deployment so you don’t have to worry about infrastructure.
Scaling Groups
Scale to zero when there is no traffic so you only pay for capacity when you're using it. When traffic returns, Chalk provisions capacity and gets the model serving automatically. Chalk handles optimized model weights fetches so a new replica goes from zero to serving fast, and KV Cache aware routing to route follow-up requests to the same replica with the cache for a given conversation.
Volumes
Distributed file system optimized for model weight distribution. Store the weights for your model rather than downloading them from Hugging Face every time. Download the weights into a volume, and then load them fast from there to keep weights inside your VPC, reduce dependency on external services, and speed up model startup.
AI Router
Create keys, and track total token requests and usage by key. Set daily budgets, and block if a limit is exceeded.
Context Engine
Call deployed models from online or offline Chalk queries, and use production data to build datasets for supervised fine-tuning, all within the same cloud environment where you serve your models.
Your cloud
Run inference, model weights, prompts, outputs, and traffic in your own cloud environment, without taking on the operational burden of deploying and scaling the infrastructure yourself.
Go deeper on model serving.
Bring us your most
expensive monthly model spend bill.
We will fine-tune an open model on your data, help you serve it in your cloud and compare quality and cost side by side.
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