Vellum

Create, version, and organize multiple prompt variants within a centralized workspace. Allows teams to maintain a library of different prompt formulations for the same task without external version control systems.

Run controlled A/B tests comparing different prompt variants against the same input data to measure performance differences. Provides statistical analysis and comparison metrics to identify the best-performing prompt.

Generate API endpoints for deployed models and prompts with automatic documentation and SDKs. Enables seamless integration of AI capabilities into external applications.

Track and analyze costs associated with API calls, model inference, and fine-tuning operations. Provides insights into performance metrics like latency and token usage to optimize spending.

Provide shared workspace for teams to collaborate on prompts, models, and experiments with inline commenting and feedback capabilities. Enables asynchronous collaboration without context switching.

Provide an interactive interface to execute prompts in real-time with different inputs and model configurations. Enables rapid iteration and manual testing without coding.

Prepare training data, configure fine-tuning parameters, and train custom LLM models within the platform. Streamlines the end-to-end process of creating domain-specific or task-specific model variants without external ML infrastructure.

Deploy trained models and prompt variants to production endpoints with version control and rollback capabilities. Manages model lifecycle from development through production with audit trails.

Automatically capture and log all API requests and responses for deployed models. Provides visibility into production behavior with detailed request/response data for debugging and analysis.

Define granular permissions and access levels for team members based on roles. Controls who can view, edit, deploy, and manage prompts, models, and production systems.

Maintain detailed audit logs of all actions taken within the platform including prompt changes, deployments, and access events. Supports compliance requirements with immutable records of system activity.

Test and compare outputs from different LLM models (e.g., GPT-4, Claude, Llama) against the same prompts and inputs. Helps teams select the best model for their use case based on performance, cost, and latency.

Execute prompts against predefined test datasets to evaluate performance across multiple inputs. Provides batch evaluation capabilities to assess prompt quality before deployment.

Prepare, format, and organize training data for fine-tuning workflows. Supports data validation and transformation to ensure data quality before model training.