Traceloop offers two deployment models tailored to meet diverse customer needs while maintaining robust support for generative AI application workflows.Documentation Index
Fetch the complete documentation index at: https://www.traceloop.com/docs/llms.txt
Use this file to discover all available pages before exploring further.
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Deployment Options
Hybrid Deployment
Perfect for organizations seeking to:- Maintain control over data storage while outsourcing compute
- Minimize operational overhead
- Comply with data residency requirements
- Leverage Traceloop’s managed services for processing and monitoring
- Processing Pipelines: Managed by Traceloop
- Dashboard: Hosted and managed by Traceloop
- Data Storage: Remains entirely on your infrastructure
- Metadata Storage: Managed by Traceloop
- Monitoring Models: Managed by Traceloop
Full Platform Self-Hosting
Ideal for organizations requiring:- Complete control over all components
- Air-gapped environments
- Maximum security and compliance
- Custom infrastructure requirements
- Processing Pipelines: On your infrastructure
- Dashboard: Hosted by Traceloop
- Data Storage: Fully within your infrastructure
- Metadata Storage: Fully within your infrastructure
- Monitoring Models: Fully within your infrastructure
Comparison
| Feature | Hybrid Deployment | Full Platform |
|---|---|---|
| Processing Pipelines | Traceloop-managed | Customer-managed |
| Dashboard | Traceloop-hosted | Traceloop-hosted |
| Data Storage | Customer infrastructure | Customer infrastructure |
| Metadata Storage | Traceloop-managed | Customer-managed |
| Monitoring Models | Traceloop-managed | Customer-managed |
| Data in Motion | Handled by Traceloop | Handled entirely by customer |
| Security | Shared responsibility | Fully customer-controlled |
| Deployment Complexity | Lower | Higher |
Next Steps
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