Velvet
Centralize and optimize LLM operations for production AI systems
Target Audience
- AI Engineers
- MLOps Teams
- Data Scientists
Hashtags
Overview
Velvet helps engineering teams manage large language model (LLM) operations in production environments. It automatically logs all AI requests to your own database, enabling performance analysis, cost optimization through caching, and continuous monitoring of AI features. Teams can run experiments, compare models, and generate datasets for fine-tuning – all through SQL queries and developer-friendly workflows.
Key Features
SQL Log Analysis
Query LLM request logs directly in your database using SQL
Intelligent Caching
Reduce costs and latency by reusing identical AI responses
Continuous Monitoring
Track production AI features with automated alerts for failures
Dataset Generation
Create training datasets from production logs for fine-tuning
Model Experiments
Test multiple LLMs/settings against real-world usage metrics
Use Cases
Analyze LLM performance metrics
Optimize costs with request caching
Compare model versions through experiments
Generate fine-tuning datasets from logs
Pros & Cons
Pros
- Maintain full control over log data in your own database
- SQL-based analysis integrates with existing engineering workflows
- Significant cost reduction through intelligent caching
- Continuous production monitoring prevents feature degradation
Cons
- Free plan limited to PostgreSQL and major LLM providers
- Primarily designed for engineering teams (less no-code)
Frequently Asked Questions
Which models and databases does Velvet support?
Free plan supports OpenAI/Anthropic models with PostgreSQL storage. Paid plans add custom model support and flexible database options.
How does Velvet help with cost optimization?
Intelligent caching reduces duplicate requests, while SQL analysis helps identify cost patterns and inefficient models.
Integrations
Reviews for Velvet
Alternatives of Velvet
Monitor and optimize large language model workflows
Monitor, evaluate, and optimize large language model applications
Automate LLM evaluation to improve AI product reliability
Unify access to multiple large language models through a single API
Streamline prompt engineering and LLM performance management