Backblaze vs Modal
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.
Serverless GPU cloud platform for AI/ML with Python-native deployment and per-second billing; developer-favorite scaling from zero competing with Replicate and Beam for AI compute.
About
Modal is a serverless cloud computing platform purpose-built for AI and machine learning workloads — providing on-demand GPU compute that scales instantly from zero with per-second billing, container management, distributed training support, and a Python-native developer experience that makes running ML workloads in the cloud feel as simple as running code locally. Founded in 2021 in New York City and backed by Redpoint Ventures and other investors, Modal has grown rapidly as AI development has accelerated demand for flexible, developer-friendly GPU infrastructure.\n\nModal's developer experience is its primary differentiator — engineers write Python functions decorated with @modal.function() and deploy them to the cloud with a single command, with Modal handling container building, GPU provisioning, auto-scaling, and execution. The platform supports training jobs that need distributed compute across multiple GPUs, model serving endpoints that scale to zero when unused (eliminating idle GPU costs), and batch inference jobs that process large datasets. The per-second billing model means developers pay only for actual compute time, not provisioned instances.\n\nIn 2025, Modal competes in the AI infrastructure market with Replicate, Beam, Banana, and major cloud providers' managed ML services (AWS SageMaker, Google Vertex AI, Azure ML) for serverless GPU compute. The market for AI-specific cloud infrastructure has grown dramatically as the number of ML engineers deploying models to production has expanded — traditional cloud providers require significant DevOps expertise to use GPU instances effectively, while Modal's Python-native approach reduces the barrier to entry. Modal has attracted a strong developer following among AI researchers and ML engineers building production AI applications. The 2025 strategy focuses on growing the developer community, adding enterprise features (dedicated GPU capacity, private networking, compliance), and expanding the hardware options available (H100 GPUs, custom accelerators).
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