Ferveret vs Vellum
Side-by-side comparison of AI visibility scores, market position, and capabilities
Two-phase immersion cooling using nuclear reactor-inspired subcooled nucleate boiling for AI data centers; $1M revenue with first 4MW contract delivered competing with GRC and LiquidStack.
About
Ferveret is a data center cooling technology company developing two-phase immersion cooling systems based on subcooled nucleate boiling — a heat transfer mechanism inspired by nuclear reactor cooling methods that achieves extremely high heat flux removal capability, enabling cooling of next-generation AI accelerators and high-performance computing chips that air cooling and single-phase liquid cooling cannot adequately handle. Founded in 2021, Ferveret raised $2.1 million from Y Combinator and E14 Fund, achieving $1 million in revenue in 2024 and successfully delivering its first 4 MW cooling contract from its El Paso, Texas manufacturing facility.\n\nFerveret's two-phase immersion approach works by submerging computing hardware in a dielectric fluid — when the chips generate heat, the fluid boils at precisely controlled temperatures, carrying heat away as vapor (the phase change enables far more heat transfer than single-phase liquid cooling). The subcooled nucleate boiling technology optimizes the boiling conditions for maximum heat transfer efficiency at controlled temperatures, enabling cooling of 300-1000W+ per chip that modern AI training accelerators (H100, B200) require. This approach addresses the fundamental limit that air cooling reaches at approximately 50W/chip.\n\nIn 2025, Ferveret competes in the data center thermal management market with GRC (Green Revolution Cooling, immersion cooling leader), LiquidStack, Submer, and traditional CRAC/CRAH air cooling for high-density AI compute installations. The data center cooling market has grown dramatically as AI training and inference workloads drive GPU density requirements beyond what air-cooled facilities can handle — NVIDIA H100 and B200 cards require 700W-1000W each, and the data centers being built for AI in 2024-2026 are designed for 40-80kW per rack, impossible with air. The successful 4MW delivery validates Ferveret's manufacturing capability. The 2025 strategy focuses on growing AI data center contracts with hyperscalers and colocation providers, scaling manufacturing capacity, and improving system density and heat reuse efficiency.
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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