⚖️Side-by-Side Comparison

HiddenLayer vs Decawork: Which is Better in 2026?

We've compared 2 tools across pricing, features, ratings, and reviews to help you make an informed decision. No paid placements — just facts.

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Feature
HiddenLayer logo
HiddenLayer

Cybersecurity

Decawork logo
Decawork

Cybersecurity

RatingNo reviews yetNo reviews yet
Pricing
  • Contact for Pricing:Contact Sales

as of Sep 7, 2026

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Pricing ModelContact SalesContact Sales
Free Plan
Free Trial
API Available
Verified
Founded—2026
HQAustin, Texas, United StatesBengaluru, India
Team SizeMedium Business, Large EnterprisesEnterprise
Platforms——
Top Integrations
CI/CDMLOpsData PipelinesSIEM/SOAR
ChatGPTClaude Coden8nMicrosoft Copilot+7 more
Key Features
  • AI Discovery
  • AI Supply Chain Security
  • AI Attack Simulation
  • AI Runtime Security
  • Model Scanning
  • +3 more
  • Unified agent inventory
  • Approval gates
  • Scoped credentials
  • Full audit trail
  • Monitoring and governance
Description

HiddenLayer is an enterprise security platform for AI and machine learning that scans model files in formats like pickle, ONNX, and safetensors to detect backdoors, malicious deserialization, and tampering before deployment. It also provides AI runtime detection and response along with automated adversarial red-teaming across predictive, generative, and agentic systems. It targets enterprises and regulated industries securing their ML pipelines, with model-file scanning as its main differentiator.

Decawork is an IT control plane that gives enterprise IT teams one place to approve, secure, and audit the AI agents their departments build across any tool or framework -- from ChatGPT, Claude Code, and Cursor to n8n, LangGraph, CrewAI, and the OpenAI Agents SDK. Each agent gets an owner, scoped short-lived credentials, approval gates for sensitive actions, and a complete attributable audit trail, so IT can say yes to team-built agents without forcing a rebuild on a single platform. It is aimed at IT, security, and compliance leaders at enterprises trying to safely scale internal AI adoption across marketing, HR, finance, legal, and engineering teams.