Centralize and optimize LLM operations for production AI systems
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.
Query LLM request logs directly in your database using SQL
Reduce costs and latency by reusing identical AI responses
Track production AI features with automated alerts for failures
Create training datasets from production logs for fine-tuning
Test multiple LLMs/settings against real-world usage metrics
Free plan supports OpenAI/Anthropic models with PostgreSQL storage. Paid plans add custom model support and flexible database options.
Intelligent caching reduces duplicate requests, while SQL analysis helps identify cost patterns and inefficient models.
This tool is best for:
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Velvet can be integrated with these apps and services:
Recent data shows that Velvet has 191 monthly visits (154.7% increase from the previous month), 37.0% bounce rate, and average 1.03 pages per visit.
Traffic is primarily driven by 6 different sources, with users from 1 countries worldwide, led by India contributing 100% of total traffic. SEO performance is shown by 5 tracked keywords, with "anthropic docs" being the top-performing keyword with 2.9K monthly searches. See below for more info.
Monthly Visits
191
Pages per Visit
1.03
Bounce Rate
37.0%
| SEO Keyword | Volume | CPC |
|---|---|---|
| anthropic docs | 2.9K | - |
| openai batch api | 2.0K | - |
| openai caching | 380 | - |
| anthropic batch api | 240 | - |
| batch openai | 220 | - |
| Source | Traffic Share |
|---|---|
| Direct | 39% |
| Search | 41% |
| Social | 4% |
| Referrals | 14% |
| Paid Referrals | 2% |
| Country Name | Traffic Share |
|---|---|
 | 100% |
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