Epsilla vs Vellum
Side-by-side comparison of AI visibility scores, market position, and capabilities
US AI agent platform for enterprise private knowledge with no-code and code-first tools generating $1.4M revenue 2024; YC S23 $500K ex-TigerGraph founder competing with Glean and Cohere for enterprise RAG and AI agent deployment.
About
Epsilla is a United States-based AI agent development platform — backed by Y Combinator (S23) with $500,000 raised from YC, Nivesha Ventures, and Ride Wave Ventures — providing domain professionals, AI engineers, and enterprise teams with an all-in-one platform for building, deploying, and managing production-ready AI agents powered by private organizational knowledge, generating $1.4 million in revenue in 2024 with a 9-person team and serving thousands of generative AI builders and dozens of enterprise and SMB customers. Founded by Richard Song (former Senior Director of Cloud Engineering at TigerGraph, a graph database company), Epsilla addresses the gap between AI prototyping environments (Jupyter Notebooks, LangChain scripts, OpenAI Playground) and production-grade enterprise AI agent deployment that requires security controls, scalability, and organizational knowledge integration.
LLM application development platform with prompt management, evaluation, and RAG workflows; structured AI feature development competing with LangSmith and Weights & Biases Prompts.
About
Vellum is an AI product development platform providing prompt management, model comparison, workflow orchestration, and production monitoring tools for engineering and product teams building LLM-powered applications — enabling teams to iterate on AI features with rigorous evaluation frameworks rather than ad-hoc prompt tweaking. Founded in 2023 by Andrew Kirima and Noa Flaherty in San Francisco, Vellum has raised approximately $12 million and targets AI-forward product teams at growth companies who need structured workflows for LLM feature development, testing, and deployment.\n\nVellum's platform covers the LLM application development lifecycle: Prompt Workshop for managing and versioning prompt templates with variable substitution, Evaluations for testing prompts against datasets to measure output quality before deployment, Document Index for building RAG (retrieval-augmented generation) pipelines with semantic search over enterprise documents, and Workflows for orchestrating multi-step AI pipelines with branching logic and human-in-the-loop review steps. The monitoring dashboard tracks production LLM performance, latency, and cost across models.\n\nIn 2025, Vellum competes in the rapidly growing LLM development tools market against LangSmith (LangChain's commercial platform), Weights & Biases Prompts, Helicone, Braintrust, and Humanloop for AI application observability and evaluation. The market has grown explosively as companies productionize LLM features and need rigorous quality control processes. Vellum's differentiation is its end-to-end workflow — from prompt development through evaluation to production monitoring — in a single platform rather than requiring separate tools for each stage. The 2025 strategy focuses on expanding workflow complexity support (longer multi-agent pipelines), growing enterprise adoption with SSO and access controls, and adding AI-powered evaluation that automatically judges output quality.
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