Depot vs Vellum
Side-by-side comparison of AI visibility scores, market position, and capabilities
Remote Docker build cache service turning 10-minute CI builds into 2-minute builds; shared persistent layer cache across CI runners competing with Docker Build Cloud for container build acceleration.
About
Depot is a remote Docker build cache and layer storage service that dramatically accelerates Docker image builds in CI/CD pipelines — providing a shared, persistent build cache that allows consecutive builds to reuse unchanged layers across different machines and parallel runners, turning 10-minute Docker builds into 2-minute builds. Founded in 2022 and headquartered in the United States, Depot targets engineering teams running Docker-based CI/CD on GitHub Actions, CircleCI, or other cloud CI platforms where each build starts from scratch without access to previous build cache.\n\nDepot's shared remote cache stores Docker build layers in cloud infrastructure and makes them available to all CI runners across a team — when a build starts, it checks Depot's cache for previously built layers and only rebuilds what has changed. This is particularly impactful for large monorepos and multi-stage Dockerfiles where base dependency layers (npm install, pip install, Maven dependencies) represent significant build time but rarely change between commits. Depot also provides native ARM build support (building ARM64 images without slow emulation).\n\nIn 2025, Depot competes with Docker's own Build Cloud, Buildkite Depot, and engineering teams' self-managed BuildKit caching solutions for CI Docker build optimization. The Docker build performance market has grown as teams running microservices in containers experience significant CI cost and time from slow Docker builds. Depot's managed service eliminates the infrastructure management burden of self-hosted build cache. The 2025 strategy focuses on expanding GitHub Actions integration (native action available in GitHub Marketplace), growing ARM native build adoption as teams adopt Apple Silicon development, and building build analytics that help teams identify slow Dockerfile patterns.
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.
AI Visibility Head-to-Head
Key Details
Also Compare
Track AI Visibility in Real Time
Monitor how your brand performs across ChatGPT, Gemini, Perplexity, Claude, and Grok daily.