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AI DevSecOps platform running 32 parallel scanners with an AI engine that cuts false positives and auto-generates fix PRs.
Agentic AI SRE using dynamic code analysis to find, root-cause, and remediate code and infrastructure issues before production.
AI-driven platform to visually design multi-cloud infrastructure and auto-generate Terraform code with built-in CI/CD.
Diagnoses Kubernetes issues in plain English; well-known open-source developer tool.
Free cloud cost-optimization platform that pools buying power to give startups enterprise-level AWS, GCP, and Azure discounts.
Free trial available
Free trial available
Free trial available
No public pricing
No public pricing
- ✦32 scanners (SAST, SCA, DAST, IaC, secrets, container)
- ✦Securitron AI false-positive filtering
- ✦AI auto-remediation with fix PRs
- ✦ASPM and CSPM posture management
- ✦CI/CD, IDE and MCP integrations
- ✦On-premises deployment option
- ✦Dynamic Code Analysis engine
- ✦Automated root-cause analysis and remediation
- ✦Pull-request and config fix suggestions
- ✦MCP server for AI-assisted code review
- ✦Observability and data-source integrations
- ✦Runs locally or on-prem/private cloud
- ✦Visual multi-cloud architecture designer
- ✦Instant Terraform/OpenTofu code generation
- ✦Drift detection and remediation
- ✦Embedded visual CI/CD engine
- ✦GitOps workflow and RBAC
- ✦AI infrastructure generation from prompts
- ✦AI-Powered Analysis of Kubernetes clusters
- ✦Data Anonymization
- ✦Support for multiple AI providers (OpenAI, Azure, Google, etc.)
- ✦Auto Remediation of common Kubernetes issues
- ✦Claude Desktop Integration
- ✦Fine-Grained Control & Guardrails
- ✦Local AI Models support
- ✦Automated cloud spend optimization
- ✦Group buying for enterprise discounts
- ✦Cost visibility and insights dashboards
- ✦Coverage across AWS, GCP, and Azure
- ✦No-cost service model
- →Scanning code and cloud for vulnerabilities
- →Reducing false-positive triage
- →Auto-fixing findings via pull requests
- →Meeting compliance (ISO 27001, PCI DSS, SOC2)
- →Reducing incident resolution time
- →Catching performance issues pre-production
- →Enhancing AI code reviews with runtime data
- →Monitoring microservice performance
- →Designing and deploying cloud infrastructure visually
- →Migrating to Infrastructure as Code
- →Standardizing Terraform modules and naming
- →Detecting drift between design and live cloud
- →Diagnosing and fixing Kubernetes issues with AI-driven insights
- →Automated troubleshooting and remediation of cluster problems
- →Enhancing Kubernetes management with Claude Desktop integration
- →Analyzing cluster state and identifying potential problems
- →Improving Kubernetes workflows
- →Reducing startup cloud bills
- →Automating reserved-capacity savings
- →Gaining visibility into multi-cloud spend
- →Accessing enterprise pricing without scale