Overview
Vellum is an LLM operations (LLM-Ops) platform that provides a suite of tools for the entire lifecycle of large language model application development. Founded in 2023, the platform is engineered to assist developers and technical buyers in moving LLM-powered features from ideation through to production. It addresses common challenges in LLM development, such as managing prompt versions, evaluating model outputs, and ensuring reliable deployment of RAG-based applications (Vellum documentation).
The platform is organized around several core products, including prompt management, comprehensive evaluations, observability features, deployment capabilities, and specific support for Retrieval Augmented Generation (RAG) applications. Vellum's prompt management system allows users to iterate on prompts, test different versions, and manage them within a structured environment. This is critical for maintaining consistency and performance as LLM applications evolve. For instance, developers can create multiple prompt templates for a specific task, such as summarization or question answering, and track their performance over time.
Evaluation tools within Vellum enable systematic testing of LLM outputs. This includes both automated metrics and human-in-the-loop review workflows. Users can define custom evaluation criteria and run tests against various models and prompt versions to compare their effectiveness. For example, a development team could evaluate different LLM providers (e.g., OpenAI's GPT-4 vs. Anthropic's Claude 3) for a specific task using Vellum's evaluation framework to determine which model performs best on their specific dataset and criteria.
Observability features provide insights into the performance and behavior of LLM applications in production. This includes monitoring usage, latency, and error rates, which helps in identifying issues and optimizing application performance. The platform's deployment capabilities facilitate taking tested LLM features to production environments, often integrating with existing application stacks via SDKs (Python and TypeScript) and a REST API (Vellum API reference). This integration aims to reduce the operational overhead associated with deploying and managing LLM services.
Vellum is particularly suited for rapid prototyping of LLM applications, allowing developers to quickly test different models and prompt strategies. Its capabilities also extend to managing complex RAG applications, where external knowledge bases are used to augment LLM responses. The platform offers tools to manage the retrieval process, index data, and evaluate the quality of retrieved information, which is a common challenge in building accurate and reliable RAG systems. For example, a company building a customer support chatbot could use Vellum to manage the retrieval of relevant knowledge base articles and evaluate the accuracy of the bot's responses based on those articles.
Enterprises and development teams focused on building and scaling AI-powered features benefit from Vellum's structured approach to LLM development. The platform's SOC 2 Type II compliance indicates adherence to security and availability standards, which can be a consideration for organizations handling sensitive data. While competitive platforms like LangChain offer programmatic control over LLM workflows, Vellum provides a more integrated platform experience that combines development, evaluation, and deployment tools (LangChain documentation). This holistic approach is designed to reduce the need for stitching together multiple disparate tools for LLM application development.
Key features
- Prompt Management: Centralized system for versioning, testing, and organizing LLM prompts. This includes A/B testing different prompt variations and tracking their performance metrics.
- Evaluations: Tools for systematic testing of LLM performance using both automated metrics (e.g., semantic similarity, factuality scores) and human feedback loops. Supports custom evaluation criteria and datasets.
- Observability: Monitoring of LLM application performance in production, including request tracing, latency tracking, error logging, and token usage analytics. Provides insights into application health and user interactions.
- Deployment: Features for deploying LLM-powered components as managed endpoints, allowing integration into existing applications without direct LLM API management. Supports versioning of deployed models and prompts.
- Retrieval Augmented Generation (RAG): Specific support for building and managing RAG applications, including data indexing, retriever configuration, and evaluation of retrieval quality and generated responses.
- Data Management: Tools to manage datasets used for prompt testing, fine-tuning, and evaluation, ensuring data quality and consistency across development cycles.
- Model Agnostic API: Provides a unified API layer to interact with various underlying LLM providers (e.g., OpenAI, Anthropic, Google), abstracting away provider-specific API calls.
- Playground Interface: An interactive environment for rapid prototyping, experimentation with different prompts, and immediate feedback on LLM outputs.
Pricing
Vellum offers a tiered pricing structure that includes a free developer plan and progressively scales for larger usage requirements. As of May 2026, the pricing details are as follows:
| Plan | Monthly Cost | Included Requests/Month | Key Features |
|---|---|---|---|
| Developer | Free | Up to 10,000 | Core platform features, suitable for personal projects and early prototyping. |
| Growth | $300 + usage-based | Up to 500,000 | All Developer features, increased limits, priority support, suitable for growing applications. Additional requests are priced per 10k units. |
| Enterprise | Custom pricing | Custom | All Growth features, dedicated support, advanced security, custom compliance, suitable for large-scale deployments and specific enterprise needs. |
Further details on usage-based pricing for the Growth plan and specific enterprise features are available on the official Vellum pricing page.
Common integrations
Vellum is designed to integrate with various components of an LLM application stack. Key integration points include:
- LLM Providers: Direct integration with major large language model providers such as OpenAI, Anthropic, Google's Gemini, and others, allowing users to switch between models seamlessly via Vellum's API (Vellum LLM provider overview).
- Vector Databases: Connectors for popular vector databases (e.g., Pinecone, Weaviate, Milvus) to support Retrieval Augmented Generation (RAG) workflows, enabling the indexing and retrieval of relevant data (Vellum RAG documentation).
- Application Frameworks: SDKs for Python and TypeScript facilitate integration into web applications, backend services, and data pipelines built with these languages.
- Observability & Monitoring Tools: While Vellum includes its own observability, it can be integrated with external monitoring and logging systems through its API endpoints for consolidated data analysis.
- Data Warehouses/Lakes: Capabilities to ingest data from various sources for evaluations, prompt testing, and RAG knowledge bases.
Alternatives
For developers and organizations considering Vellum, several alternative platforms and frameworks offer overlapping or complementary functionalities:
- LangChain: A framework for developing applications powered by language models, offering modular components for chaining LLM calls, agents, and RAG.
- Weights & Biases: Primarily an MLOps platform for experiment tracking, model versioning, and visualization, which can be adapted for LLM experiment tracking and evaluation.
- Humanloop: An LLM platform focused on prompt management, evaluation, and fine-tuning, with a strong emphasis on human feedback loops.
- Hugging Face: Offers a broad ecosystem of open-source models, datasets, and tools for building and deploying machine learning applications, including LLMs.
- Databricks LLM Ops: Provides tools and frameworks within the Databricks Lakehouse Platform for managing the full lifecycle of LLMs, from pre-training to deployment and monitoring.
Getting started
To begin using Vellum, developers typically install one of the SDKs and initialize the client with an API key. The following Python example demonstrates a basic interaction with the Vellum API to generate text using a deployed model.
import os
from vellum import Vellum
# Ensure your Vellum API key is set as an environment variable
# os.environ["VELLUM_API_KEY"] = "YOUR_VELLUM_API_KEY"
vellum = Vellum(api_key=os.environ.get("VELLUM_API_KEY"))
def generate_text_with_vellum(prompt_text: str):
"""
Generates text using a deployed Vellum model.
"""
try:
response = vellum.generate(prompt_text=prompt_text)
# Assuming a single completion for simplicity
if response.completions and response.completions[0].text:
print("Generated Text:", response.completions[0].text)
else:
print("No completion text received.")
except Exception as e:
print(f"An error occurred: {e}")
if __name__ == "__main__":
example_prompt = "Write a short, engaging slogan for a new AI directory platform."
generate_text_with_vellum(example_prompt)
This Python code snippet illustrates how to instantiate the Vellum client and make a call to a deployed model using a specific prompt. Developers would replace "YOUR_VELLUM_API_KEY" with their actual API key, ideally managed securely through environment variables. The vellum.generate() method sends the prompt to the Vellum platform, which then routes it to the configured LLM and returns the generated text. For more complex use cases, such as integrating with specific deployed models or managing prompt versions, further details are available in the Vellum Getting Started guide.