Tesseract on Cake

CrewAI

Agent Frameworks & Orchestration

CrewAI simplifies the development of agent-based systems where AI agents collaborate on shared goals through defined roles and task coordination. Cake helps you deploy and manage CrewAI workloads in secure, production-ready environments—with built-in robust orchestration, observability, and full support for cloud-native scaling.

AutoGen

Agent Frameworks & Orchestration

AutoGen is a framework for building multi-agent systems where agents communicate and coordinate through natural language messages. With Cake, you can deploy AutoGen into a secure, scalable environment with integrated support for orchestration, prompt management, and observability.

LangGraph

Agent Frameworks & Orchestration

LangGraph is a framework for building stateful, multi-agent applications with precise, graph-based control flow. Cake helps you deploy and scale LangGraph workflows with built-in state persistence, distributed execution, and observability.

LiveKit

Streaming

LiveKit delivers real-time audio and video streaming through its open-source WebRTC platform. Cake makes it easy to integrate LiveKit into multi-agent AI workflows with built-in communication handling, orchestration, and secure infrastructure management.

Pipecat

Data Ingestion & Movement,

Agent Frameworks & Orchestration

Pipecat is a context-aware routing engine that lets LLMs invoke live APIs, trigger tools, or respond to streaming data in real time. Cake provides the infrastructure to run Pipecat in production—handling execution, scaling, and security so you can build dynamic, tool-using agents that integrate with other components.

Katib

Distributed Model Training & Model Formats

Katib is an open-source AutoML system for hyperparameter optimization. It's designed to run natively on Kubernetes. Cake makes it easy to run Katib experiments across environments, automating experiment orchestration, scaling compute, and managing metadata at every step.

Ray Tune

Distributed Model Training & Model Formats,

Pipelines and Workflows

Ray Tune is a Python library for distributed hyperparameter optimization, built on Ray’s scalable compute framework. With Cake, you can run Ray Tune experiments across any cloud or hybrid environment while automating orchestration, tracking results, and optimizing resource usage with minimal setup.

PyCaret

Feature Engineering & Data Lineage,

Pipelines and Workflows

PyCaret is a low-code Python library that accelerates ML workflows—automating training, comparison, and deployment with minimal code. Cake enables you to integrate PyCaret into scalable, production-ready pipelines without sacrificing speed or flexibility.

Optuna

Distributed Model Training & Model Formats,

Pipelines and Workflows

Optuna is a lightweight, open-source framework for hyperparameter optimization. It's designed for fast, flexible, and algorithmically efficient search. Cake lets you run Optuna experiments at scale, handling orchestration, distributed execution, and live experiment tracking across any environment.

Crossplane

Pipelines and Workflows,

Infrastructure & Resource Optimization

Crossplane lets you define and manage cloud infrastructure using Kubernetes-native APIs, treating infrastructure like any other workload. Cake integrates Crossplane into a secure, policy-driven GitOps workflow, making multi-cloud infrastructure as manageable as your apps.

Istio

Pipelines and Workflows

Istio is a Kubernetes-native service mesh that secures and manages traffic between microservices. Cake automates the full Istio lifecycle with GitOps-based deployments, certificate rotation, and policy enforcement.

Kustomize

Pipelines and Workflows

Kustomize lets you template and patch Kubernetes configurations without forking YAML files. Cake makes it easy to manage Kustomize workflows across environments with automation, validation, and Git-based deployments.

Terraform

Pipelines and Workflows,

Infrastructure & Resource Optimization

Terraform is the industry standard for provisioning infrastructure as code. Cake provides a secure, compliant layer for running Terraform modules across any cloud—automating workflows, enforcing policy, and enabling multi-team collaboration.

Argo CD

Pipelines and Workflows

Argo CD enables GitOps for Kubernetes by continuously reconciling the desired state from Git. Cake integrates Argo CD natively, giving you automated sync, rollback, and deployment pipelines across all environments.

Helm

Pipelines and Workflows

Helm simplifies the packaging and deployment of Kubernetes applications. Cake helps you manage Helm charts at scale with version control, rollback support, and secure CI/CD integration.

Dex

Identity & Access Management

Dex is an open-source identity provider for Kubernetes and cloud-native systems that connects with OIDC-compatible services. Cake integrates Dex for centralized, secure authentication across your AI/ML stack, infrastructure, and internal tools.

Karpenter

Infrastructure & Resource Optimization

Karpenter is a fast, flexible autoscaler for Kubernetes that provisions infrastructure in real time. Cake integrates Karpenter to dynamically adjust compute based on workload demands—helping you save on cost while maintaining SLA performance.

AWS

Infrastructure & Resource Optimization,

Cloud Providers

Amazon Web Services (AWS) is the leading cloud platform for compute, storage, and infrastructure services—powering everything from startups to global enterprises. On Cake, AWS becomes part of a modular, declarative AI stack, enabling teams to use familiar AWS services with cloud-neutral tooling and built-in compliance.

Google Cloud Platform (GCP)

Infrastructure & Resource Optimization,

Cloud Providers

Google Cloud Platform (GCP) offers powerful infrastructure, AI services, and data tools used by teams building scalable, production-grade applications. On Cake, GCP becomes part of a modular, cloud-agnostic stack—letting you orchestrate compute, storage, and Kubernetes workloads declaratively without being locked into Google-specific tooling.

IBM Cloud

Infrastructure & Resource Optimization,

Cloud Providers

IBM Cloud is a flexible cloud computing platform that combines traditional infrastructure with AI, data, and compliance services tailored for regulated industries. When used with Cake, IBM Cloud becomes part of a unified, declarative infrastructure strategy—so enterprises can securely deploy across on-prem and cloud environments without rebuilding their stack.

LambdaLabs

GPU Training,

Infrastructure & Resource Optimization,

Cloud Providers

LambdaLabs offers high-performance cloud GPUs designed for AI training and inference at scale. It’s a popular alternative to mainstream clouds for teams needing fast, cost-effective access to NVIDIA hardware. With Cake, LambdaLabs becomes part of a composable AI platform, making it easy to run training jobs, inference endpoints, or agent workloads on GPU-backed infrastructure without managing low-level configuration.

Oracle

Infrastructure & Resource Optimization,

Cloud Providers

Oracle Cloud Infrastructure (OCI) delivers high-performance computing, databases, and enterprise services tailored for critical workloads. It’s widely used in finance, healthcare, and government sectors for its performance and compliance guarantees. With Cake, Oracle becomes part of a modular AI stack, allowing teams to deploy across OCI and other environments using Git-based, declarative workflows.

Azure

Infrastructure & Resource Optimization,

Cloud Providers

Microsoft Azure is a comprehensive cloud platform offering compute, AI, and enterprise services widely used in regulated and global enterprise environments. On Cake, Azure becomes a flexible backend for deploying AI workloads—whether you’re running inference, managing Kubernetes clusters, or provisioning infrastructure.

Bitbucket

Code Repositories

Bitbucket is a Git-based source code management platform built for teams using Atlassian tools. It supports private repositories, CI/CD pipelines, and enterprise access controls. On Cake, Bitbucket becomes the source of truth for declarative infrastructure and ML pipelines—automated, secure, and fully integrated with your stack.

GitHub

Code Repositories

GitHub is the world’s most popular platform for hosting code, managing version control, and running developer workflows. It powers collaboration across open source and enterprise teams alike. With Cake, GitHub becomes your control plane for infrastructure and AI—connecting code changes to secure, auditable, and fully automated deployments.

GitLab

Code Repositories

GitLab is an all-in-one DevSecOps platform that combines source control, CI/CD, and security into a single interface. It’s popular for self-hosted and enterprise-grade deployments where compliance and visibility matter. On Cake, GitLab becomes the operational foundation for managing infrastructure, models, and data pipelines—all from code.

HIPAA

Compliance Standards

The Health Insurance Portability and Accountability Act (HIPAA) sets strict standards for safeguarding protected health information (PHI) in the United States. Cake helps you build and run HIPAA-aligned workloads by integrating access control, audit logging, and secure deployment patterns across your stack.

SOC2

Compliance Standards

SOC 2 is a security and privacy framework developed by the AICPA to evaluate how organizations manage customer data. Cake helps you align with SOC 2 controls by enforcing access policies, audit logging, and change management across your infrastructure—automatically.

Data Hub

Data Source Adapters,

Data Catalogs & Lineage

Data Hub is an open-source metadata platform that helps teams discover, understand, and govern their data. On Cake, Data Hub becomes a critical layer for data observability and governance, plugged directly into your AI and infrastructure workflows.

Unity Catalog

Data Catalogs & Lineage,

Data Quality & Validation

Unity Catalog is Databricks’ unified governance solution for managing data access, lineage, and discovery across your Lakehouse. With Cake, Unity Catalog becomes part of your compliance-ready AI stack, ensuring secure, structured data usage across teams and environments.

Great Expectations

Data Quality & Validation

Great Expectations is an open-source data quality framework that lets you define, test, and monitor expectations about your data. On Cake, Great Expectations is fully operationalized so you can validate data at every stage without bolting on manual checks.

Amundsen

Data Catalogs & Lineage

Amundsen is an open-source data discovery and metadata engine that helps teams understand and trust their data. On Cake, Amundsen becomes part of your data governance layer—automated and integrated with your broader AI infrastructure.

Amazon S3

Data Sources

Amazon S3 is a widely used object storage service for scalable data storage. Cake integrates S3 into your AI stack, making it easy to manage data assets for model training, inference, and analytics—all with governance and automation built in.

Google BigQuery

Databases

BigQuery is Google’s serverless, highly scalable cloud data warehouse. Cake integrates BigQuery into AI workflows, enabling teams to query massive datasets and feed downstream ML pipelines without complex infrastructure.

Kafka

Data Sources

Apache Kafka is a distributed event streaming platform used to build real-time data pipelines and streaming applications. Cake operationalizes Kafka for AI use cases, making event-driven architectures easy to deploy and govern.

Snowflake

Databases

Snowflake is a popular cloud data platform for secure data warehousing and analytics. Cake integrates Snowflake into AI pipelines, making it easy to query, transform, and operationalize data for machine learning at scale.

Apache Hadoop

Data Sources

Apache Hadoop is an open-source framework for distributed storage and batch data processing. Cake integrates Hadoop into modern AI workflows, allowing teams to leverage legacy or large-scale data processing within a governed AI infrastructure.

Atlassian

Data Sources

Atlassian offers a suite of collaboration and project management tools like Jira and Confluence. Cake integrates Atlassian into AI project workflows, helping teams manage experiments, releases, and compliance documentation in one streamlined environment.

Google Cloud Services

Data Sources

Google Cloud Services provide robust infrastructure, AI tools, and storage options. Cake lets teams use GCP as part of a modular AI stack—governed, secure, and fully integrated with open-source tools.

Postgres

Databases

PostgreSQL (Postgres) is an advanced open-source relational database. Cake integrates Postgres as a core data layer for AI applications—making structured data management secure, scalable, and automated.

Presto

Databases

Presto is an open-source distributed SQL query engine optimized for querying large-scale datasets. Cake integrates Presto to enable fast, federated querying of data lakes for AI workflows.

Salesforce

Data Sources

Salesforce is a leading customer relationship management (CRM) platform. Cake enables AI teams to securely integrate Salesforce data into machine learning workflows for personalization, forecasting, and analytics.

Apache Iceberg

Data Sources,

Data Versioning

Apache Iceberg is an open table format for managing petabyte-scale analytic datasets. Cake integrates Iceberg into AI workflows, making it easy to handle versioned, partitioned data across storage layers.

AWS Neptune

Graph Databases

AWS Neptune is a fast, scalable graph database optimized for connected data. Cake integrates Neptune to support AI use cases involving knowledge graphs, recommendations, and complex relationships.

AWS RDS

Databases

AWS RDS offers managed relational databases including Postgres, MySQL, and SQL Server. Cake integrates RDS instances into AI infrastructure, making it easy to manage data pipelines securely and reliably.

AWS Redshift

Databases

Amazon Redshift is a scalable cloud data warehouse for running high-performance analytics. Cake integrates Redshift to power AI data preparation, feature engineering, and real-time insights.

Chroma

Vector Databases

Chroma is an open-source vector database optimized for storing and querying embeddings. Cake integrates Chroma for AI use cases like semantic search, recommendation, and RAG pipelines.

Delta Lake

Data Sources,

Data Versioning

DeltaLake is an open-source storage layer that brings ACID transactions to data lakes. Cake integrates DeltaLake to enable reliable, versioned, and high-performance AI data pipelines.

Milvus

Vector Databases

Milvus is an open-source vector database built for high-speed similarity search. Cake integrates Milvus into AI stacks for recommendation engines, RAG applications, and real-time embedding search.

Neo4j

Graph Databases

Neo4j is the world’s leading graph database, used to model complex relationships and connected data. Cake integrates Neo4j to enable graph-based AI applications, such as fraud detection and recommendation engines.

Pinecone

Vector Databases

Pinecone is a fully managed vector database built for real-time similarity search. Cake integrates Pinecone to help AI teams power semantic search, recommendations, and GenAI pipelines.

PGVector

Vector Databases

PGVector is an open-source Postgres extension for storing and querying vector embeddings. Cake integrates PGVector to enable hybrid search, recommendation, and RAG use cases using existing Postgres infrastructure.

Qdrant

Vector Databases

Qdrant is an open-source vector search engine designed for scalable similarity search and recommendation systems. Cake integrates Qdrant into AI pipelines to enable fast, efficient embedding search with robust governance.

DVC

Data Versioning

DVC (Data Version Control) is an open-source version control system for machine learning projects. Cake integrates DVC to enable reproducible ML pipelines, data lineage, and collaborative model development.

Argilla

Synthetic Data Generators

Argilla is an open-source data labeling and feedback tool for AI teams. Cake integrates Argilla into MLOps workflows to help teams build better models through faster, more collaborative data annotation.

Faker

Synthetic Data Generators

Faker is an open-source Python library for generating synthetic data. Cake integrates Faker to enable secure, reproducible AI testing, model validation, and privacy-preserving development.

Apache Superset

Visualization & BI

Build interactive dashboards and visual analytics at scale with Cake’s seamless Apache Superset integration.

Autoviz

Visualization & BI

Instantly visualize your data with automated charting and no-code workflows using Cake’s Autoviz integration.

Deepchecks

Model Evaluation Tools

Automate data validation and monitor ML pipeline quality with Deepchecks, fully managed and integrated by Cake.

Ragas

Model Evaluation Tools

Rapidly evaluate RAG pipelines and optimize LLM performance with Cake’s streamlined Ragas orchestration.

DeepEval

Model Evaluation Tools

Automate LLM evaluation and quality assurance for your AI projects with Cake’s built-in DeepEval integration.

ClearML

Pipelines and Workflows

Centralize ML workflow management, experiment tracking, and orchestration with Cake’s ClearML integration.

MLflow

Pipelines and Workflows

Track ML experiments and manage your model registry at scale with Cake’s automated MLflow setup and integration.

Weights & Biases

Pipelines and Workflows,

Model Evaluation Tools

Effortlessly log, visualize, and share ML experiments with scalable Weights & Biases tracking powered by Cake.

DeepSpeed

Model Training & Optimization

Accelerate large-scale model training and optimize compute costs with Cake’s DeepSpeed orchestration.

FSDP

Model Training & Optimization

Enable efficient distributed training and automated scaling for large models using Cake’s FSDP integration.

Llama Factory

Fine-Tuning Techniques

Fine-tune Llama models quickly and reliably with Cake’s one-click Llama Factory deployment and workflow support.

LoRA

Fine-Tuning Techniques

Adapt LLMs efficiently with LoRA and manage adapters across projects, all streamlined through Cake’s platform.

TRL

Fine-Tuning Techniques

Set up RLHF workflows and reward modeling for LLMs with Cake’s integrated TRL pipelines and performance tracking.

Unsloth

Model Training & Optimization

Fine-tune LLMs ultra-fast with Unsloth and Cake’s resource-optimized, version-controlled infrastructure.

Anthropic

Foundation Models

Securely integrate Anthropic’s Claude models into your stack with managed access and usage tracking via Cake.

AWS Bedrock

Foundation Models

Deploy AWS Bedrock foundation models at scale, track usage, and manage compliance with Cake’s unified platform.

Cohere

Foundation Models

Build and launch NLP applications rapidly using Cohere’s language models and Cake’s streamlined deployment pipeline.

DeepSeek

Foundation Models

Launch and experiment with DeepSeek LLMs instantly with Cake’s automated model provisioning and collaboration tools.

Gemma

Foundation Models

Deploy and fine-tune open-source Gemma models for any use case with Cake’s cloud-agnostic infrastructure.

Hugging Face

Foundation Models

Instantly search, deploy, and manage Hugging Face models and datasets directly through Cake’s unified interface.

Meta Llama

Foundation Models

Deploy and fine-tune Meta Llama models at scale, across any environment, with full monitoring via Cake.

Mistral

Foundation Models

Seamlessly orchestrate Mistral LLMs, optimize compute, and monitor performance through Cake’s automated workflows.

OpenAI

Foundation Models

Connect OpenAI’s GPT, DALL-E, and Whisper models securely with managed access, versioning, and analytics from Cake.

Phi-3

Foundation Models

Deploy and scale lightweight Phi-3 language models for efficient, high-performance AI apps using Cake’s platform.

Stable Diffusion

Foundation Models

Generate and scale AI-powered images in real time with Stable Diffusion endpoints managed and monitored by Cake.

BGE

Embedding Models

Create and serve high-speed text embeddings for search and retrieval with Cake’s BGE model integration.

Flux

Embedding Models

Deploy, scale, and monitor any ML model in production using Cake’s flexible Flux integration and unified endpoints.

Global Text Embedding

Embedding Models

Generate universal multilingual text embeddings for global search and analysis with Cake’s GTE integration.

Qwen

Foundation Models

Deploy, fine-tune, and track Qwen LLMs for research or enterprise with Cake’s secure, audit-ready orchestration.

Molmo

Embedding Models

Run molecular AI models at scale, automate workflows, and manage experiments with Cake’s streamlined Molmo integration.

Nomic

Embedding Models

Visualize, organize, and search high-dimensional data using Nomic’s interactive tools, powered by Cake’s platform.

NvEmbed

Embedding Models

Serve ultra-fast, GPU-accelerated embeddings for large-scale AI applications using Cake’s NvEmbed orchestration.

NVLM

Foundation Models

Launch and operate NVIDIA’s large language models for enterprise use with Cake’s automated NVLM integration.

Pixtral

Foundation Models

Deploy multimodal AI and vision models with Pixtral and manage end-to-end workflows and monitoring through Cake.

Cerebras

AI Accelerators & Hardware

Cerebras is a specialized AI hardware platform built around the Wafer-Scale Engine (WSE) for accelerating deep learning workloads. Cake integrates Cerebras to simplify model deployment and leverage high-performance compute in AI pipelines.

CoreWeave

AI Accelerators & Hardware

CoreWeave is a cloud provider optimized for high-performance compute, including GPU and AI workloads. Cake connects CoreWeave infrastructure to scalable, automated AI pipelines while applying governance and cost management.

CUDA

AI Accelerators & Hardware

CUDA is NVIDIA’s parallel computing platform and programming model for GPU acceleration. Cake integrates CUDA-driven compute into AI training and inference workflows while managing resource scaling and orchestration.

GCP TPU

AI Accelerators & Hardware

Google Cloud TPU is a purpose-built accelerator for machine learning workloads on Google Cloud. Cake integrates TPU resources into AI pipelines, automating deployment, scaling, and cost controls for high-performance model training.

Trainium

AI Accelerators & Hardware

AWS Trainium is a custom chip designed for training machine learning models at scale on AWS. Cake simplifies the use of Trainium by automating provisioning, orchestration, and policy management for AI infrastructure.

Intel Gaudi

AI Accelerators & Hardware

Intel Gaudi processors provide AI acceleration optimized for deep learning training and inference. Cake integrates Gaudi-powered compute into AI pipelines with governance, automation, and scalable resource management.

NVIDIA RAPIDS

AI Accelerators & Hardware

NVIDIA RAPIDS is an open-source GPU-accelerated data science and machine learning library. Cake operationalizes RAPIDS to accelerate feature engineering, model training, and data processing in secure, governed AI workflows.

AMD

AI Accelerators & Hardware

AMD provides high-performance CPUs and GPUs used for compute-intensive workloads, including AI and machine learning. Cake supports AMD hardware as part of cloud-agnostic AI infrastructure with automated deployment and scaling.

CloudWatch

Cloud Compute & Storage

Amazon CloudWatch is a monitoring and observability service for AWS resources and applications. Cake integrates CloudWatch into AI pipelines to track performance, resource usage, and compliance metrics in real time.

EC2

Cloud Compute & Storage

Amazon EC2 is a foundational AWS service providing scalable virtual compute instances. Cake automates EC2 provisioning, monitoring, and governance for AI training, inference, and application hosting.

EFS

Cloud Compute & Storage

Amazon EFS is a fully managed, scalable file storage service for use with AWS compute services. Cake integrates EFS into AI pipelines to enable shared storage for distributed training, data ingestion, and model outputs.

Google File System (GFS)

Cloud Compute & Storage

Google File System (GFS) is a scalable distributed file system designed to support large data-intensive applications. Cake integrates GFS-compatible storage layers into AI pipelines, enabling scalable, secure data handling for training and analytics.

Google Kubernetes Engine (GKE)

Cloud Compute & Storage

Google Kubernetes Engine (GKE) is a managed Kubernetes service for deploying, managing, and scaling containerized applications. Cake connects GKE to AI pipelines, automating orchestration, scaling, and governance of AI workloads.

Slurm

Cloud Compute & Storage

Slurm is an open-source job scheduler for high-performance computing environments. Cake integrates Slurm-managed compute clusters into AI workflows, automating resource allocation, scaling, and observability.

Amazon EKS

Cloud Compute & Storage

Amazon Elastic Kubernetes Service (EKS) is AWS’s managed Kubernetes platform. Cake automates the deployment and governance of AI workloads on EKS, streamlining orchestration, scaling, and security.

Okta

Identity & Access Management

Okta is an identity and access management platform providing secure authentication and user management. Cake integrates Okta to enforce authentication, authorization, and compliance across AI infrastructure and pipelines.

Kubeflow

Orchestration & Pipelines

Kubeflow is an open-source machine learning platform built on Kubernetes. Cake operationalizes Kubeflow deployments, automating model training, tuning, and serving while adding governance and observability.

Argo Workflows

Orchestration & Pipelines

Argo Workflows is an open-source container-native workflow engine for orchestrating parallel jobs on Kubernetes. Cake integrates Argo to automate AI pipeline execution, manage dependencies, and ensure policy enforcement.

Airbyte

Data Ingestion & Movement

Airbyte is an open-source data integration platform used to build and manage ELT pipelines. Cake integrates Airbyte into AI workflows for scalable, governed data ingestion feeding downstream model development.

Airflow

Orchestration & Pipelines

Apache Airflow is an open-source workflow orchestration tool used to programmatically author, schedule, and monitor data pipelines. Cake automates Airflow deployments within AI workflows, ensuring compliance, scalability, and observability.

Apache Beam

Orchestration & Pipelines

Apache Beam is an open-source unified model for defining batch and streaming data processing jobs. Cake integrates Beam into AI pipelines to simplify deployment, scaling, and governance of data transformations.

dbt

Orchestration & Pipelines

dbt (data build tool) is an open-source analytics engineering tool that helps teams transform data in warehouses. Cake connects dbt models to AI workflows, automating feature engineering and transformation within governed pipelines.

Docling

Orchestration & Pipelines

Docling is an open-source document intelligence tool for extracting structured information from unstructured text. Cake integrates Docling into AI workflows to automate document classification, extraction, and downstream analysis.

Prefect

Orchestration & Pipelines

Prefect is a modern workflow orchestration system for data engineering and ML pipelines. Cake integrates Prefect to automate pipeline execution, monitoring, and governance across AI development environments.

Metaflow

Orchestration & Pipelines

Metaflow is a human-centric framework for managing real-life data science projects. Cake integrates Metaflow to streamline pipeline orchestration, versioning, and scaling for AI workloads.

Feast

Orchestration & Pipelines

Feast is an open-source feature store for managing and serving machine learning features. Cake integrates Feast into AI pipelines, automating feature storage, retrieval, and governance.

Label Studio

Data Labeling & Annotation

Label Studio is an open-source data labeling tool for supervised machine learning projects. Cake connects Label Studio to AI pipelines for scalable annotation, human feedback, and active learning governance.

V7

Data Labeling & Annotation

V7 is a data annotation and model training platform for visual AI applications. Cake integrates V7 into AI pipelines to streamline image labeling, dataset management, and active learning loops.

Evidently

Model Evaluation Tools

Evidently is an open-source tool for monitoring machine learning models in production. Cake operationalizes Evidently to automate drift detection, performance monitoring, and reporting within AI workflows.

Grafana

Cloud Compute & Storage

Grafana is an open-source analytics and monitoring platform used for observability and dashboarding. Cake integrates Grafana into AI pipelines to visualize model performance, infrastructure metrics, and system health.

NannyML

Model Evaluation Tools

NannyML is an open-source library for monitoring post-deployment data and model performance. Cake integrates NannyML to automate AI drift detection, quality monitoring, and compliance reporting.

OpenCost

Observability & Monitoring

OpenCost is an open-source cost monitoring tool for Kubernetes environments. Cake integrates OpenCost to track, optimize, and govern AI infrastructure spending across cloud-native deployments.

Prometheus

Observability & Monitoring

Prometheus is an open-source systems monitoring and alerting toolkit. Cake integrates Prometheus into AI pipelines for real-time monitoring, metric collection, and governance of AI infrastructure.

Jaeger

Observability & Monitoring

Jaeger is an open-source distributed tracing system used for monitoring and troubleshooting microservices. Cake integrates Jaeger into AI pipelines to provide observability, trace model execution, and ensure compliance across complex workflows.

Jupyter

Interactive Notebooks

Jupyter is an open-source web-based interface for interactive computing and data science. Cake integrates Jupyter notebooks into AI workflows to enable experimentation, collaboration, and reproducible research within governed environments.

PyCharm

Development Environments

PyCharm is a popular integrated development environment (IDE) for Python development. Cake supports PyCharm as part of AI development workflows, allowing teams to build, test, and deploy models with integrated governance and automation.

RStudio

Development Environments

RStudio is an integrated development environment for R programming used in data science and statistical analysis. Cake integrates RStudio into AI workflows, automating deployment, resource scaling, and compliance monitoring.

Visual Studio (VS) Code

Development Environments

Visual Studio Code (VS Code) is a lightweight, open-source code editor with support for development across multiple languages and frameworks. Cake integrates VS Code into AI pipelines for seamless model development, testing, and deployment.

Cline

Development Environments

Cline is a command-line interface for managing AI and ML workloads. Cake supports Cline-based tooling to automate infrastructure provisioning, model orchestration, and policy enforcement.

Cursor

Development Environments

Cursor is an AI-powered code editor designed to assist developers with intelligent code suggestions and automation. Cake integrates Cursor into AI workflows to accelerate development while maintaining security and governance.

Google Colab

Interactive Notebooks

Google Colab is a free cloud-based Jupyter notebook environment for interactive computing. Cake integrates Colab into AI pipelines for rapid prototyping, collaboration, and deployment of models within governed environments.

Flowise

Agent Frameworks & Orchestration

Flowise is an open-source visual builder for creating LLM apps and agents using modular components.

GraphRAG

Agent Frameworks & Orchestration,

Retrieval-Augmented Generation

GraphRAG is a Retrieval-Augmented Generation framework that enhances reasoning by structuring knowledge as graphs.

LangChain

Agent Frameworks & Orchestration

LangChain is a framework for developing LLM-powered applications using tools, chains, and agent workflows.

LightRAG

Retrieval-Augmented Generation

LightRAG is a lightweight RAG framework optimized for low-latency inference and minimal resource usage.

Pydantic

Data Validation

Pydantic is a data validation and settings management library for Python that uses type hints to enforce correctness.

TrustCall

Agent Frameworks & Orchestration

TrustCall is a LangChain-compatible agent framework designed for reliable, structured data extraction from untrusted sources.

Apache Spark

Distributed Computing Frameworks

Apache Spark is a distributed computing engine for large-scale data processing, analytics, and machine learning.

Dask

Distributed Computing Frameworks

Dask is a flexible Python library for parallel computing, enabling scalable data processing and machine learning.

Ray

Distributed Computing Frameworks

Ray is a distributed execution framework for building scalable AI and Python applications across clusters.

SkyPilot

Distributed Computing Frameworks

SkyPilot is a framework for running AI workloads across multiple cloud platforms using intelligent scheduling and cost optimization.

Horovod

Distributed Computing Frameworks

Horovod is an open-source distributed training framework for deep learning, designed to scale models across multiple GPUs or nodes.

Gemini

Foundation Models

Integrate Google Gemini multimodal AI for text, vision, and code tasks, managed and governed by Cake.

Weaviate

Vector Databases

Weaviate is an open-source vector database and search engine built for AI-powered semantic search. Cake integrates Weaviate to support scalable retrieval, recommendation, and question answering systems.

Langflow

Agent Frameworks & Orchestration

Langflow is a visual drag-and-drop interface for building LangChain apps, enabling rapid prototyping of LLM workflows.

LlamaIndex

Retrieval-Augmented Generation

LlamaIndex is a data framework that connects LLMs to external data sources using indexing, retrieval, and query engines.

DSPy

LLM Optimization

DSPy is a framework for optimizing LLM pipelines using declarative programming, enabling dynamic tool selection, self-refinement, and multi-step reasoning.

Promptfoo

LLM Observability,

LLM Optimization

Promptfoo is an open-source testing and evaluation framework for prompts and LLM apps, helping teams benchmark, compare, and improve outputs.

Ray Serve

Inference Servers

Ray Serve is a scalable model serving library built on Ray, designed to deploy, scale, and manage machine learning models in production.

KServe

Inference Servers

KServe is an open-source model inference platform for Kubernetes, enabling standard, scalable deployment of ML models across frameworks.

NVIDIA Triton Inference Server

Inference Servers

Triton is NVIDIA’s open-source server for running high-performance inference across multiple models, backends, and hardware accelerators.

Ollama

Inference Servers

Ollama is a local runtime for large language models, allowing developers to run and customize open-source LLMs on their own machines.

Amazon SageMaker

General Platform

Amazon SageMaker is AWS’s end-to-end machine learning platform for building, training, and deploying models at enterprise scale.

TGI (Text Generation Inference)

Inference Servers

TGI is Hugging Face’s optimized serving solution for fast, scalable deployment of large language models like LLaMA and Falcon.

Vertex AI

General Platform

Vertex AI is Google Cloud’s managed platform for training, deploying, and managing machine learning models in a unified ecosystem.

vLLM

Inference Servers

vLLM is an open-source library for high-throughput, low-latency LLM serving with paged attention and efficient GPU memory usage.

Arize Phoenix

LLM Observability

Arize Phoenix is an open-source observability platform for LLMs, providing tools for debugging, tracing, and improving AI applications.

Langfuse

LLM Observability

Langfuse is an open-source observability and analytics platform for LLM apps, capturing traces, user feedback, and performance metrics.

Darts

Forecasting Libraries

Darts is a Python library for time series forecasting, offering a unified API for statistical, deep learning, and ensemble models.

LightGBM

ML Model Libraries

LightGBM is a high-performance gradient boosting framework that’s optimized for speed, efficiency, and large-scale datasets.

NeuralProphet

Forecasting Libraries

NeuralProphet is an open-source forecasting tool built on PyTorch, combining classic time-series models with deep learning components.

ONNX

Inference Servers

ONNX (Open Neural Network Exchange) is an open standard format for representing machine learning models across frameworks.

PyTorch

ML Model Libraries

PyTorch is a widely used open-source machine learning framework known for its flexibility, dynamic computation, and deep learning support.

PyTorch Lightning

ML Model Libraries

PyTorch Lightning is a lightweight PyTorch wrapper that helps structure ML code and scale deep learning experiments more easily.

scikit-learn

ML Model Libraries

scikit-learn is a foundational Python library for machine learning that includes tools for classification, regression, clustering, and more.

Surprise

ML Model Libraries

Surprise is a Python scikit for building and analyzing recommender systems using collaborative filtering and matrix factorization.

TensorFlow

ML Model Libraries

TensorFlow is an open-source machine learning framework developed by Google for building, training, and deploying deep learning models.

XGBoost

ML Model Libraries

XGBoost is a scalable and efficient gradient boosting library widely used for structured data and tabular ML tasks.

ADTK

ML Model Libraries

ADTK (Anomaly Detection ToolKit) is a Python library designed for rule-based and machine learning–based anomaly detection in time series data.

Ray Train

Forecasting Libraries

Ray Train is a scalable library built on Ray for distributed training of deep learning models across multiple nodes and GPUs.

OHIF

Visualization & Dashboards

OHIF is an open-source web-based DICOM viewer and imaging platform commonly used in medical imaging AI workflows.

Open WebUI

Visualization & Dashboards

Open WebUI is a user-friendly, open-source interface for managing and interacting with local or remote LLMs and AI agents.

TensorBoard

Visualization & Dashboards

TensorBoard is a visualization toolkit for TensorFlow that provides dashboards for metrics, graphs, profiling, and model analysis.

Streamlit

Visualization & Dashboards

Streamlit is an open-source Python framework for rapidly building and sharing interactive data and machine learning web apps.

Vercel

Frontend & Deployment Platforms

Vercel is a cloud platform for frontend development and deployment, commonly used to host AI dashboards, UIs, and model endpoints.

Qlora

Model Training & Optimization,

ML Model Libraries

QLoRA is a method for fine-tuning large language models efficiently by combining quantization and low-rank adapters. It reduces hardware requirements without compromising model performance.

LiteLLM

Observability & Monitoring,

Inference Servers,

Data Catalogs & Lineage

LiteLLM provides a lightweight wrapper for calling OpenAI, Anthropic, Mistral, and other LLMs through a common interface. It supports token metering, caching, and governance tools.

Pandas

ML Model Libraries,

Pipelines and Workflows

Pandas brings powerful, expressive data structures to Python, making it easy to clean, reshape, and analyze tabular data. It's foundational to modern data science workflows.

OpenTelemetry

Observability & Monitoring,

Infrastructure & Resource Optimization,

Orchestration & Pipelines

OpenTelemetry provides standardized tools for tracing, metrics, and logging in distributed systems. It enables deep visibility into applications, services, and infrastructure.

Kedro

Pipelines and Workflows,

Orchestration & Pipelines

Kedro helps teams standardize pipeline construction and reproducibility in machine learning projects. It integrates seamlessly with data engineering, modeling, and deployment workflows.

OpenNMS

Observability & Monitoring,

Infrastructure & Resource Optimization,

Orchestration & Pipelines,

Agent Frameworks & Orchestration

OpenNMS provides comprehensive fault, performance, and topology monitoring for networks of all sizes. It supports SNMP, flow data, and custom integrations.

NumPy

ML Model Libraries

NumPy delivers fast, flexible operations on n-dimensional arrays and matrices. It powers countless tools across the Python data science and ML ecosystem.