Instant Media Analysis, Clean Results.
Built by the team behind Encoding.com — billions of assets processed.
Probe.dev replaces ffprobe and mediaInfo with one simple API call.
Open Source + Machine Learning
Run your workflow's precise version of ffprobe or mediaInfo as a API service and/or leverage the Probe Media Report for a unified, normalized view of your media analysis. Our system consolidates and standardizes outputs from both tools, applies rigorous data normalization, and enhances accuracy using machine learning trained on over a billion media assets to deliver cleaner, more accurate media analysis.
Learn more about report types —>
File I/O Reengineered
Get results in milliseconds. Our purpose built file system architecture is optimized for media analysis, benchmarking up to 58% faster than ffprobe or mediaInfo executions. We use aggressive caching, prefetch reads, and smart request deduplication to minimize round trips to cloud storage and reduce I/O latency throughout the pipeline.
Deploy with one line of code
Ditch local shell scripts, container orchestration and runtime dependencies. Replace ffprobe or mediaInfo with a single HTTP request fully parameterized, instantly scalable. Use the exact same parameters you already pass without rewrites or surprises. Just faster, cleaner results from an infrastructure free, API driven service trained on over a billion media assets.
Cost Plus Cloud Pricing
Industry standard pricing models based on per minute or per GB rates suck because they bluntly address the complexity and variability of modern media processing. Our innovative cost plus model solves this by metering and reporting actual compute time per job, down to the second. Each job returns a transparent usage report, and our margin is not only visible but automatically scales down with volume. It’s a precise, fair pricing model—designed for engineering and finance teams that require clarity and control.
Real Workflow Use Cases
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Quickly verify file integrity, structure, and hundreds of key technical parameters (like duration, bitrate, resolution, and stream layout) before downstream processing or delivery.
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Automate the extraction of metadata across massive libraries to assess format consistency, detect legacy formats, and prioritize content for restoration or re-encoding.
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Pre-analyze incoming media to dynamically set encoding parameters—like resolution ladders, aspect ratios, and audio channel layouts—based on source characteristics.
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Validate media files against delivery specs or internal standards—checking for codec compliance, bitrates, frame rates, or missing streams—to reduce manual QA and avoid rejections. text goes here
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Generate consistent, structured technical metadata to enrich model inputs, improve tagging precision, and power smarter ML-driven workflows (e.g., content classification or scene detection).