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THE CAKE DIFFERENCE
From simple model calls to
production-grade inference
Vanilla model inference
Simple to start, expensive to scale: Calling an API works for a prototype, but lacks control, efficiency, and visibility.
- High cost per request, no optimization for usage patterns
- Limited visibility into inputs, outputs, and failures
- No fine-grained routing, caching, or fallback logic
- Vendor lock-in with little flexibility across models
Inference with Cake
Fast, observable, and cost-efficient by design: Cake turns inference into a fully managed, production-ready system.
- Deploy vLLM, TGI, or your own optimized serving stack in your cloud
- Add caching, fallback routing, and model switching with zero vendor lock-in
- Built-in observability: log inputs/outputs, costs, latencies, and errors
- Support for open models, finetuned models, and commercial APIs in one pipeline
EXAMPLE USE CASES
How teams run inference securely with Cake
Customer-facing AI
Power chatbots, recommendation engines, and personalization systems that serve millions of users in real time.
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Operational automation
Deploy computer vision, fraud detection, or anomaly detection models into mission-critical business workflows.
Research to production
Take fine-tuned or experimental models from lab environments into compliant, production-grade infrastructure with minimal effort.
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Real-time analytics
Enable fast, on-demand predictions to support decision-making in areas like trading, logistics, and supply chain optimization.
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Edge inference
Run models close to the data source on devices, sensors, or edge servers to minimize latency and bandwidth costs.
Multi-model routing
Dynamically serve, A/B test, or ensemble multiple models to improve accuracy and optimize cost-performance tradeoffs.
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Scott Stafford
Chief Enterprise Architect at Ping
CEO
InsureTech Company
Felix Baldauf-Lenschen
CEO and Founder
COMPONENTS
Tools that power Cake's inference stack
Ray Serve
Ray Serve is a scalable model serving library built on Ray, designed to deploy, scale, and manage machine learning models in production.
NVIDIA Triton Inference Server
Triton is NVIDIA’s open-source server for running high-performance inference across multiple models, backends, and hardware accelerators.
Ollama
Ollama is a local runtime for large language models, allowing developers to run and customize open-source LLMs on their own machines.
vLLM
vLLM is an open-source library for high-throughput, low-latency LLM serving with paged attention and efficient GPU memory usage.
Langfuse
Langfuse is an open-source observability and analytics platform for LLM apps, capturing traces, user feedback, and performance metrics.
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