The Cake Platform

Implements robust security measures, including role-based access control and encryption at rest and in transit, to safeguard sensitive data and ensure compliance with standards including SOC 2 Type 2.​

Provides real-time insights into system performance and health, enabling proactive issue detection and resolution.​

Supports deployment across various environments, including Kubernetes clusters and cloud services such as AWS, ensuring adaptability to diverse operational needs.​

Utilizes Infrastructure-as-Code practices through integration with code repositories such as GitHub, facilitating consistent and version-controlled infrastructure management.

Supports chunking, vectorization, and retrieval using vector databases (Weaviate, Milvus, Qdrant, pgvector,), embedding models (BGE), and orchestration frameworks (LangGraph, Langflow) to feed LLMs the context they actually need.

Enables analysts with business intelligence tools and dashboards through Metabase, Matomo, and Superset, analytics engines like Spark. Cake includes visualization and EDA tools like Tensorboard and Autoviz.

When labeled data is scarce, Cake helps generate high-quality synthetic datasets via statistical and GAN models. Cake includes frameworks like SDV, Mostly.AI, Synthcity, YData, and Faker.

Supports high-performance serving of large models via frameworks like vLLM and RayServe, with autoscaling and GPU optimization built-in.

Stop manually engineering all your prompts. Take advantage of frameworks like DSPy and allow the computer to generate many options. Track, test, and compare these prompts with multiple foundation and fine-tuned models with tools like Promptfoo and DeepChecks ensuring safe, performant, and cost-effective results.

Visualize and debug complex generative flows using LangFuse and Arize Phoenix. Use integrated tracing to get full observability into system and model behavior, errors, and  latency.

Quickly deploy rich chat interfaces with OpenWebUI, Streamlets and Vercel. Host secure cloud based notebooks while radically accelerating your development and data science using vibe coding tools like Cursor and Cline.

Run distributed training workloads with autoscaling compute across frameworks like PyTorch and XGBoost, powered by Ray for cost-efficient parallelism.

Deploy models using inference frameworks like vLLM, Ray Serve and NVIDIA Triton. Cake provides built-in support for A/B testing, canary deploys, and autoscaling your models.

Monitor model health, latency, and performance with Grafana, Prometheus, and Istio; detect drift in real-world data using tools like Evidently or NannyML before it impacts accuracy.