Dragoneye
Turn plain-English descriptions into deployable zero-shot vision models in minutes — no training data required.
Dragoneye is the fastest route from an idea to a working vision model when you can't afford to label data — zero-shot detection plus a conversational Model Builder gets you categories live in minutes. The MCP server and 2026 native object tracking make it genuinely agent-friendly for coding-agent workflows. For serious production, factor in the 5 FPS billing floor and the absence of on-prem or dedicated SLAs on Pay-as-you-go; teams needing those should look at enterprise vision suites. Rapid prototypers and AI-agent builders should start with the $10 credit and see.
Verified 23d ago · liveness 71/100 · cite: rightaichoice.com/tools/dragoneye
- Developers prototyping vision features without labelled datasets
- Construction safety teams monitoring PPE compliance
- Startups needing fast vision AI for retail inventory or logistics
- Small engineering teams with limited ML expertise
- Teams requiring on-premise or air-gapped deployment
- High-accuracy needs on niche objects without finetuning
- Enterprises requiring dedicated SLAs on the base tier
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
Skip Dragoneye if you need on-prem or air-gapped deployment, a dedicated SLA on the entry tier, or you're running constant 24/7 high-FPS video where the per-minute meter becomes the dominant cost.
Video is billed at a minimum 5 FPS even if you sample below it, so low frame-rate workloads pay the same base rate.
Pay-as-you-go fits solo developers and small teams at $0.046/min video and $0.005/image with $10 free credit — cheaper to start than Google Cloud Vision or AWS Rekognition, but the 5 FPS billing floor makes high-volume 24/7 analysis pricier than flat-rate enterprise contracts. Scaled Volume with custom pricing is the right tier once you're pushing enough video to need discounts, finetuning and priority support.
In short
Dragoneye — Turn plain-English descriptions into deployable zero-shot vision models in minutes — no training data required. Best for Developers prototyping vision features without labelled datasets, Construction safety teams monitoring PPE compliance, Startups needing fast vision AI for retail inventory or logistics. Free to use.
What's new in Dragoneye
Checked 9 days agoAcross the latest 3 updates: 1 feature update, 1 launch and 1 community discussion.
Native Object Tracking in Dragoneye
Dragoneye's video API now tracks objects across an entire video, returning category and attribute classifications, timestamps and bounding boxes for instance counting and spatial-temporal reasoning.
Tutorial: Building a Vision Feature with a Coding Agent and the Dragoneye MCP
Walkthrough uses the Dragoneye MCP server to let a coding agent build a zero-shot object detection model and wire it into an app in about ten minutes.
Launching Dragoneye's MCP Server
Dragoneye released an MCP server that lets coding agents create Dragoneye models and wire image and video detection into an app.
What people actually say about Dragoneye — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
26 mentions across 3 sources (Hacker News, YouTube, Lemmy) · researched Aug 6, 2026.
Average across the 3 sources that answered — each source counts once, not each post.
- +Zero-shot detection from plain English eliminates labeled data.
- +Model deployment via managed API within minutes of description.
- +MCP server lets coding agents integrate detection quickly.
- +Python and Node.js SDKs support popular developer stacks.
- +Video processing up to full frame rate for real-time use.
- −No community feedback validates real-world accuracy or reliability.
- −Lacks on-premise deployment for privacy-sensitive workflows.
- −No enterprise SLAs, risky for production-critical applications.
- −Beta features may be unstable or change without notice.
- −Pricing model may surprise heavy video users.
- • Overage charges for unexpected high usage
- • Video processing costs add up quickly at full frame rate
Viability Score
How well maintained and how widely used is Dragoneye? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Zero-shot object detection from plain-text descriptions
- Custom vision models built in English, no training data required
- Model Studio conversational builder (AI Model Builder, beta)
- Attribute Detection (beta) for structured details on detected objects
- Native object tracking across full video clips (categories, attributes, timestamps, bounding boxes)
- Video processing at up to the video's full natural frame rate (base rate samples at 5 FPS)
- Image classification and object detection
- Instant model deployment via a managed API — no infrastructure to maintain
- Browser Playground with interactive predictions and confidence scores
- Python SDK (dragoneye) and Node.js SDK (dragoneye-node)
- MCP server for coding-agent integration
- Model Templates library for common verticals
- Bespoke model finetuning (beta, Scaled Volume tier)
- Usage-based pricing calculator for video minutes and images
About Dragoneye
Dragoneye is a vision AI platform from Y Combinator that converts plain-English descriptions into working object-detection and classification models. Instead of collecting and labelling a dataset, you describe what you want to find — e.g. "construction site safety items" — and the zero-shot engine generates a deployable model with categories like hardhat, safety vest and scaffolding. Models go live instantly through a managed API and can be tested in the browser Playground before you wire them in via the Python (dragoneye) or Node.js (dragoneye-node) SDKs in roughly ten lines of code. Beyond basic detection, Dragoneye now offers native object tracking across video clips (categories, attributes, timestamps and bounding boxes), Attribute Detection (beta) for structured details on detected objects, and an AI Model Builder (beta) that lets you define models conversationally. A Model Templates library speeds up common verticals, and an MCP server lets coding agents such as Claude Code build custom models and integrate detection into an app in around ten minutes. Pricing is usage-based: $10 in free credits on Playground signup, then $0.046 per minute of video (billed at a minimum 5 FPS) and $0.005 per image. A Scaled Volume tier adds volume discounts, bespoke finetuning (beta) and priority support. Dragoneye is built for developers and small teams that need vision features without deep ML expertise, and it removes the traditional data-prep bottleneck that slows down projects using heavier suites like Google Cloud Vision or AWS Rekognition.
Behind the Verdict
Dragoneye's core strength is removing the data-labelling step from vision AI. You describe an object or scene in English, and the zero-shot engine produces a model with named categories (hardhat, safety vest, traffic cone, and so on). That alone collapses weeks of dataset work into a Playground session. Where it goes beyond a thin wrapper: a small but real product surface. The Python and Node SDKs share a consistent classification.predictVideo() interface, the Playground lets you test before you commit spend, and Dragoneye has shipped genuinely new capabilities in 2026 — Attribute Detection (structured details on detected objects), a conversational AI Model Builder, native object tracking across full video clips, and an MCP server aimed at coding agents. That MCP layer is the standout: a Claude Code or similar agent can create a model and wire predictions into an app in roughly ten minutes, which is a different workflow from classic CV platforms. Strengths: zero-shot workflow that needs no labelled data, instant model deploy, drop-in Python and Node SDKs, browser Playground with interactive predictions, $10 free credit, a $0.046/min video and $0.005/image price that is easy to reason about, Model Templates, and a Scaled tier for volume. Backed by Y Combinator. Weaknesses: video is billed at a minimum 5 FPS even if you sample below that, so low-frame-rate or intermittent video still pays the base rate. Constant 24/7 analysis can add up quickly — the per-minute model rewards scoped, event-driven use. Dedicated SLAs and on-prem/air-gapped deployment aren't offered on the Pay-as-you-go tier. Finetuning is beta and Scaled-only, so high-accuracy needs on niche objects may require a conversation with sales. Credits are non-refundable (though they never expire). Where it fits: prototyping, verticals like construction safety and retail inventory, and agent workflows where a coding assistant needs to add vision quickly. Where it doesn't: regulated environments requiring on-prem or a contractual SLA on the entry tier, and 24/7 high-frame-rate monitoring where the per-minute meter is the wrong cost shape.
Researching Dragoneye? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Real-world workflow fit
Concrete scenarios for the personas Dragoneye actually fits — and what changes day-one when you adopt it.
Sign in to the Playground, spend the $10 credit, describe 'construction site safety items' in English, and let the zero-shot builder generate categories like hardhat and safety vest.
Outcome: A working model with a managed API endpoint in minutes, testable in the Playground before you write any SDK code.
Use the MCP server so a coding agent creates the Dragoneye model and drops in the Python or Node SDK call for classification.predict_video().
Outcome: Detection integrated into the app end-to-end in roughly ten minutes, with usage billed at $0.046/min video and $0.005/image.
Start from a Model Templates entry for product detection, add Attribute Detection (beta) to pull structured details, and run shelf footage through the video API at 5 FPS.
Outcome: Automated inventory signals from existing camera footage without collecting a labelled training set.
Use Cases
- Monitor construction sites for safety gear like hardhats, vests and scaffolding in real time.
- Track products across shelves for automated retail inventory counts.
- Moderate user-generated content by detecting prohibited items in images and video.
- Analyze traffic footage to count vehicles and flag incidents.
- Tag wildlife in camera-trap footage for ecological studies.
- Let a coding agent build a vision model and wire it into an app via the MCP server.
Models Under the Hood
as of 2026-09-23
Limitations
- Video is billed at a minimum 5 FPS rate even if you sample below that, so low-frame-rate use still pays the base rate — above 5 FPS you pay proportionally more (10 FPS = 2x, 15 FPS = 3x).
- Images are a flat $0.005 each, and new users get $10 in credits and then top up manually.
- Bespoke model finetuning is beta and limited to the Scaled Volume tier, which requires contacting sales for custom pricing.
as of 2026-09-15
Verification history
We have re-verified Dragoneye 9 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Dragoneye tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Pay-as-you-go
$0/mo + usage
Ideal for
Solo developers and small teams prototyping vision features without a labelled dataset, starting on the $10 signup credit.
What this tier adds
Starting tier: $10 free credits, Playground predictions, plain-English model building, Python/Node SDKs, billed at $0.046/min video (5 FPS floor) and $0.005/image.
Scaled Volume
Custom
Ideal for
Teams processing high volumes of video that need discounts, bespoke finetuning and priority support.
What this tier adds
Adds volume pricing discounts, bespoke model finetuning (beta) and priority support on top of everything in Pay-as-you-go.
Where the pricing makes sense
The company stage and team size where Dragoneye's pricing actually pencils out — and where peers do it cheaper.
Pay-as-you-go fits solo developers and small teams at $0.046/min video and $0.005/image with $10 free credit — cheaper to start than Google Cloud Vision or AWS Rekognition, but the 5 FPS billing floor makes high-volume 24/7 analysis pricier than flat-rate enterprise contracts. Scaled Volume with custom pricing is the right tier once you're pushing enough video to need discounts, finetuning and priority support.
Setup time & first value
How long it actually takes to get something useful out of Dragoneye — broken out by persona, not the marketing-page minute.
Solo developer: under 5 minutes to a live model via the Playground with $10 free credit. Team wiring into an app: about 10 minutes using the MCP server and the Python or Node SDK. Teams needing bespoke finetuning should budget extra time for the Scaled tier onboarding conversation.
Switching to or from Dragoneye
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Google Cloud Vision: Replace pre-trained label sets with English-defined zero-shot categories and call the Dragoneye Python or Node API instead.
- →From AWS Rekognition: Move off fixed detectors, build custom categories in the Model Builder, and route video through the Dragoneye video API.
- →From a hand-labelled YOLO pipeline: Skip the labelling loop by describing categories in Model Studio and deploying zero-shot models in minutes.
- →From Roboflow + custom training: Keep your vertical, drop the dataset collection — start with Model Templates and add Attribute Detection for structured output.
- ↗To self-hosted open models (e.g. a YOLO or DETR stack): Export project needs by re-implementing detection locally if you require on-prem or air-gapped deployment.
- ↗To Google Cloud Vision or AWS Rekognition: Move to an enterprise vision suite if you need dedicated SLAs and contractual support at the entry tier.
- ↗To a bespoke finetuned model elsewhere: If beta finetuning on Scaled isn't enough for niche accuracy, plan to train a custom model on your own labelled data.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Dragoneye”, and we withheld 6: 6 could not be judged, because “Dragoneye” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Dragoneye.
Official links
Tools that pair well with Dragoneye
Common stack mates teams adopt alongside Dragoneye, with the specific reason each pairing earns its keep.
Datagen
Synthetic data generation for computer vision that ships photorealistic, auto-annotated training sets on demand.
Restb.ai
Real estate computer vision AI that converts property photos into standardized condition, feature, and compliance data for MLSs, AVMs, and insurers.
Autodistill
Open-source Python toolkit that auto-labels images with big foundation models, then trains small fast vision models you own.
Featured Head-to-Head Comparisons
Dragoneye vs Spider Cloud
For AI agents and RAG pipelines that need real-time web data, Spider Cloud is the clear winner with its ultra-low cost, Rust-powered performance, deep AI integrations, and open-source flexibility. Dragoneye is an innovative zero-shot vision platform—ideal for rapid prototyping without training data—but its paid-only model, lack of integrations, and no on-premise option narrow its appeal. Unless your core need is custom object detection, Spider Cloud delivers broader value at a fraction of the cost.
Dragoneye vs Voyage Ai
If your need is text-based RAG with high accuracy on domain-specific documents (finance, legal), Voyage AI’s embedding and reranker stack is unmatched. For custom vision models that require zero training data and instant deployment, Dragoneye’s zero-shot detection and new Attribute Detection are game-changers. Choose based on your data type: text vs. images.
Dragoneye vs Temporal Ai
If you need durable, fault-tolerant orchestration for AI agents or microservices, Temporal AI is your pick. If you want instant custom computer vision models without training data, Dragoneye is the clear winner. Choose based on your domain: reliability vs. vision speed.
Alternatives to Dragoneye
View all
Datagen
Synthetic data generation for computer vision that ships photorealistic, auto-annotated training sets on demand.
Restb.ai
Real estate computer vision AI that converts property photos into standardized condition, feature, and compliance data for MLSs, AVMs, and insurers.
Autodistill
Open-source Python toolkit that auto-labels images with big foundation models, then trains small fast vision models you own.
Frequently Asked Questions
Categories
Used Dragoneye? Help shape our editorial sentiment research.