OptScale
Open-source FinOps and MLOps platform for cloud cost optimization and ML/AI experiment tracking
35%
Avg Cost Savings
2.1K
GitHub Stars
9
Cloud Providers
Overview
OptScale is an open-source FinOps and MLOps platform developed by Hystax that provides comprehensive cloud cost management, optimization recommendations, and ML/AI experiment tracking. It offers multi-cloud visibility across AWS, Azure, GCP, Alibaba Cloud, and Kubernetes environments, delivering cost anomaly detection, resource rightsizing recommendations, and automated savings calculations. The MLOps capabilities include experiment tracking, model versioning, and GPU/compute cost attribution for AI workloads. Being fully open-source under Apache 2.0 license, OptScale can be self-hosted for complete data control or used via managed cloud service.
The Verdict
Who Should Use OptScale?
Best For
- Teams needing multi-cloud cost visibility
- ML/AI teams tracking experiment costs
- Organizations wanting self-hosted FinOps
- Startups seeking free cost optimization
- Kubernetes-heavy environments
- Teams requiring GPU cost attribution
Not Ideal For
- Single cloud with native tools sufficient
- Non-technical finance teams (steep learning curve)
- Organizations needing enterprise SLA/support
- Teams without DevOps capacity to self-host
What's Great
- Fully open-source with Apache 2.0 license
- Unified FinOps + MLOps in single platform
- Multi-cloud support (AWS, Azure, GCP, Alibaba, K8s)
- Real-time cost anomaly detection
- ML experiment tracking with cost attribution
- TTL policies for automatic resource cleanup
- Reserved instance and spot recommendations
- No data leaves your infrastructure (self-hosted)
Watch Out For
- Self-hosted requires significant DevOps effort
- Smaller community compared to commercial tools
- Documentation could be more comprehensive
- UI/UX less polished than enterprise alternatives
- Limited integrations compared to established FinOps tools
Pricing
Open Source
Free
Self-hosted, full features, unlimited users
Cloud (Starter)
Free
Up to $5K/mo cloud spend, managed hosting
Cloud (Team)
$150/mo
Up to $50K/mo cloud spend, priority support
Enterprise
Custom
Unlimited spend, SLA, dedicated support
View all features & details
FinOps Features
- Multi-cloud cost dashboard
- Cost anomaly detection
- Budget alerts and forecasting
- Resource rightsizing recommendations
- Reserved instance optimization
- Spot instance recommendations
- TTL policies for resource cleanup
- Shared cost allocation
- Chargeback/showback reports
- Cost trend analysis
MLOps Features
- Experiment tracking
- Model versioning
- Hyperparameter logging
- Metric visualization
- GPU cost attribution
- Compute resource tracking
- Leaderboard comparisons
- Dataset versioning
- Pipeline cost tracking
Supported Platforms
- Amazon Web Services (AWS)
- Microsoft Azure
- Google Cloud Platform (GCP)
- Alibaba Cloud
- Kubernetes (any provider)
- Databricks
- VMware (on-premise)
- DigitalOcean
- Apache CloudStack
Integrations
- Slack notifications
- Jira ticketing
- REST API
- Python SDK (arcee.ai)
- Terraform provider
- Prometheus metrics
- SSO (SAML, OIDC)
- Webhook alerts
Deployment Options
- Self-hosted (Docker Compose)
- Kubernetes Helm chart
- Managed SaaS (OptScale Cloud)
- Air-gapped environments
Security & Compliance
- Apache 2.0 license
- Read-only cloud access
- RBAC (role-based access)
- Audit logging
- Data stays in your VPC (self-hosted)
How It Compares
| Feature | OptScale | CloudHealth | Kubecost | Infracost |
|---|---|---|---|---|
| Open Source | Yes (Apache 2.0) | No | Partial | Yes |
| Multi-Cloud | 9 providers | 3 providers | K8s only | Terraform |
| MLOps Tracking | Built-in | No | No | No |
| Self-Hosted | Full featured | No | Yes | Yes |
| Free Tier | Unlimited (OSS) | No | Limited | Limited |
| Anomaly Detection | Yes | Yes | Limited | No |
| RI Optimization | Yes | Yes | No | No |
| Best For | Multi-cloud + ML teams | Large enterprises | K8s-focused | IaC cost preview |
User Reviews
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