Apache MXNet
Apache MXNet is a retired deep learning framework (now in the Apache Attic) designed for both efficiency and flexibility. It provided a multi-language API for building and training deep neural networks with support for distributed training, the Gluon high-level API, and deployment on edge devices. MXNet supported Python, Scala, Java, C++, R, Julia, and Perl.
More than an index entry, but the surface is still mostly links rather than artifacts — the cohort most likely to move a full band from modest, well-targeted work.
API Evangelist profiles Apache MXNet the way a machine reads it — 26 machine-readable artifacts across 1 API, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Apache MXNet scores 24.5/100 (emerging), with a separate agent-readiness read of 7/100 (human only). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Apache MXNet
Each block below is one kind of artifact we hold for Apache MXNet. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 1
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Apache MXNet
MXNet provides APIs in Python, Scala, Java, C++, R, Julia, and Perl for deep learning model development, with the Gluon high-level API for imperative model building, Symbol/NDAr...
Pricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the six things the Kin Score reads for commercial clarity.
Published pricing tiers and plan structures.
Rate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Apache Mxnet Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.
Cost, billing, and metering signals for API financial operations.
Apache Mxnet Finops
FINOPSFeatures 8
The notable capabilities this provider advertises, captured as structured features so they can be searched and compared instead of read one landing page at a time.
Notable capabilities this provider offers.
Hybrid Front-End
Seamlessly transitions between Gluon eager imperative mode and symbolic execution for research flexibility and production efficiency.
Distributed Training
Supports Parameter Server and Horovod for scalable distributed training across multiple GPUs and nodes.
Multi-Language Bindings
Native APIs in Python, Scala, Java, C++, R, Julia, Clojure, and Perl for broad developer accessibility.
Gluon High-Level API
Intuitive Gluon API for imperative model building with automatic differentiation and dynamic computation graphs.
NDArray API
NumPy-like array operations for GPU-accelerated numerical computing as the foundation of MXNet computations.
Symbol API
Symbolic computation graph API for efficient inference and production deployment.
Model Zoo
Pre-trained models for computer vision, NLP, and other tasks accessible via the Gluon model zoo.
Edge Deployment
Lightweight deployment support for edge devices and mobile platforms via TVM and ONNX export.
Scroll within the panel for all 8 ·
Security Posture 2
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Use Cases 5
What developers actually build with this provider — captured so the catalogue answers 'what is this for', not just 'what does this expose'.
What developers build with this provider.
Computer Vision
Build and train image classification, object detection, and segmentation models using GluonCV toolkit.
Natural Language Processing
Develop NLP models for text classification, sentiment analysis, and language modeling using GluonNLP.
Time Series Forecasting
Build time series forecasting models using the GluonTS toolkit for probabilistic forecasting.
Distributed Deep Learning
Train large neural networks across multiple GPUs and nodes using Parameter Server or Horovod.
Research Prototyping
Rapid prototyping of novel deep learning architectures using the Gluon imperative API.
Integrations 7
Pre-built integrations with other platforms tell you where this provider already fits in a stack.
Pre-built integrations with other platforms and tools.
GluonCV
Computer vision toolkit built on MXNet providing pre-trained models and training utilities for vision tasks.
GluonNLP
NLP toolkit built on MXNet with pre-trained language models and text processing utilities.
GluonTS
Time series modeling toolkit built on MXNet for probabilistic forecasting.
ONNX
ONNX model format support for importing and exporting models to/from other frameworks.
TVM
Apache TVM deep learning compiler for optimizing MXNet model deployment on diverse hardware targets.
Horovod
Horovod distributed training framework integration for efficient multi-GPU and multi-node training.
D2L.ai
Dive into Deep Learning interactive textbook using MXNet for teaching deep learning concepts.
Scroll within the panel for all 7 ·
Resources
Every other property we hold for Apache MXNet — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 1
Portal, sign-up, and the first successful call
Build 2
SDKs, sample code, and the tooling you integrate with
Access & Security 2
Authentication, authorization, and security posture
Operate 1
Status, limits, changes, and where to get help
Commercial 1
Pricing, plans, and the legal terms of use
Company 1
The organization behind the API
Other 2
Properties that don't map to a standard resource type
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