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Dagster

✓ verifiedFreemium

Open-source asset-based data orchestrator, with Dagster+ cloud, for building, observing and delivering reliable data and AI pipelines.

152K visits/mo

StackGen

✓ verifiedPaid

Agentic AI platform ('Aiden') that automates incident response, infrastructure-as-code and observability tasks with policy-based governance.

13K visits/mo

Pump

✓ verifiedFree

Free cloud cost-optimization platform that pools buying power to give startups enterprise-level AWS, GCP, and Azure discounts.

64K visits/mo3.2K saves

K8sGPT

✓ verified

Diagnoses Kubernetes issues in plain English; well-known open-source developer tool.

8.7K visits/mo510 saves

Brainboard

✓ verifiedFreemium

AI-driven platform to visually design multi-cloud infrastructure and auto-generate Terraform code with built-in CI/CD.

17K visits/mo

Pricing

Solo: $10/mo + $0.040/credit (1 user)

Starter: $100/mo + $0.035/credit (up to 3 users)

Free trial available

No public pricing

Free: $0 (unlimited architectures and code generation)

Pro: $99/user/mo (Git, CI/CD, RBAC, remote backend)

Free trial available

Core features

✦Asset-based pipeline orchestration
✦Built-in lineage and data-quality checks
✦Data catalog with asset metadata
✦Native dbt, Snowflake and Fivetran integrations
✦Branch deployments and hybrid deployment
✦Open-source core plus managed Dagster+ cloud

✦Automated service discovery and dependency topology mapping
✦SLO-based alert triage and prioritization
✦AI-driven root cause analysis with pre-built workflows
✦Human-approved remediation with full audit trails
✦Works alongside existing tools like Datadog, Grafana, New Relic
✦Governance and policy enforcement layer for agent actions

✦Automated cloud spend optimization
✦Group buying for enterprise discounts
✦Cost visibility and insights dashboards
✦Coverage across AWS, GCP, and Azure
✦No-cost service model

✦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

✦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

Use cases

→Orchestrate ETL/ELT and dbt pipelines
→Monitor data health and lineage
→Build AI/ML data pipelines
→Run reliable, observable data platforms

→SRE teams reducing mean-time-to-resolution during incidents
→Platform engineers wanting policy-governed AI infrastructure management
→Enterprises needing SOC 2 / PCI / HIPAA-compliant AI operations

→Reducing startup cloud bills
→Automating reserved-capacity savings
→Gaining visibility into multi-cloud spend
→Accessing enterprise pricing without scale

→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

→Designing and deploying cloud infrastructure visually
→Migrating to Infrastructure as Code
→Standardizing Terraform modules and naming
→Detecting drift between design and live cloud

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