Backblaze vs Vellum
Side-by-side comparison of AI visibility scores, market position, and capabilities
NASDAQ-listed cloud storage with Backblaze B2 at $6/TB/month; $127.6M FY2024 revenue with 40x AI data storage growth competing with AWS S3 on price for object storage.
About
Backblaze is a cloud storage and backup company providing two primary services: Backblaze Computer Backup (unlimited personal computer backup for $99/year) and B2 Cloud Storage (S3-compatible object storage at $6/TB/month, significantly cheaper than AWS S3). Listed on NASDAQ (NASDAQ: BLZE) and headquartered in San Mateo, California, Backblaze generated $127.6 million in revenue in FY2024 (up 25% year-over-year) with B2 Cloud Storage contributing $63.3 million (up 36%) as the faster-growing segment driving the business.\n\nBackblaze B2 Cloud Storage is an S3-compatible API object storage service priced significantly below AWS S3, Google Cloud Storage, and Azure Blob Storage — making it the cost-preferred option for AI/ML data storage, media storage, and backup repositories where storage costs are significant. The AI tailwind has accelerated B2 growth: AI customers grew 70% year-over-year and AI data stored on B2 grew 40x year-over-year through 2025, as AI training datasets and model artifacts require large-scale, affordable object storage. The Computer Backup product serves 417,000+ consumers and small businesses backing up files.\n\nIn 2025, Backblaze's B2 ARR reached $81.8 million (up 26% YoY) with total ARR at $147.2 million, and the company targeted Q4 2025 free cash flow positivity as the business scales toward sustainable profitability. B2 Cloud Storage competes directly with AWS S3 (Backblaze charges no egress fees for data transferred to Cloudflare, a key differentiator), Wasabi, and DigitalOcean Spaces for the price-sensitive object storage market. The 2025-2026 strategy focuses on capturing AI infrastructure storage demand, expanding B2 as the storage backend for content delivery and media workflows, and continuing the transition from a backup-centric to a cloud storage platform company.
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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