Geti
Free, open-source computer vision platform for rapid AI model development with OpenVINO.
Geti is a rare find: a genuinely free, open-source CV platform with serious model depth. If you're on Intel hardware and want an end-to-end pipeline without cloud lock-in, it's a smart bet. But if you need a fully managed service or non-Intel deployment, look elsewhere.
Verified 3d ago · liveness 67/100 · cite: rightaichoice.com/tools/geti
- Domain experts building computer vision models without deep coding
- Data scientists seeking rapid prototyping and deployment on Intel hardware
- Organizations needing a free, open-source end-to-end CV pipeline
- Teams deploying AI on Intel edge devices with OpenVINO optimization
- Users requiring NLP or generative AI capabilities
- Teams needing a fully managed cloud SaaS platform
- Projects targeting non-Intel hardware without OpenVINO support
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Skip Geti if you need a fully managed cloud platform or if your deployment targets are non-Intel and you can't leverage OpenVINO optimizations.
Since Geti is free, the main cost is your own infrastructure: you need to host it yourself, so factor in compute resources and maintenance time.
Geti is free forever, making it a strong value compared to cloud services like Roboflow (which charges per user and usage) or Azure Custom Vision. It's ideal for teams that can self-host and want to avoid per-seat costs.
In short
Geti — Free, open-source computer vision platform for rapid AI model development with OpenVINO. Best for Domain experts building computer vision models without deep coding, Data scientists seeking rapid prototyping and deployment on Intel hardware, Organizations needing a free, open-source end-to-end CV pipeline. Free to use.
What people actually say about Geti — 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.
39 mentions across 4 sources (Hacker News, YouTube, GitHub, Lemmy) · researched Aug 31, 2026.
- +Completely free and open source under Apache 2.0, no license costs.
- +67+ pre-trained models covering detection, segmentation, and classification.
- +End-to-end workflow: annotation, training, optimization, and inference in one.
- +Automatic export to OpenVINO IR, ONNX, and PyTorch for flexible deployment.
- +Supports Intel Arc GPU for local model training (beta).
- −Self-hosted setup is complex; not for beginners without technical skills.
- −Community is small and quiet, with limited real-world user feedback.
- −Documentation is sparse, forcing users to rely on GitHub issues.
- −Optimized for Intel hardware, limiting benefits on other platforms.
- −Setup can take days, especially for non-Docker-savvy users.
- • No subscription fees, but you pay in setup time and infrastructure (GPU, storage, networking).
- • Hardware costs if you need Intel Arc GPU for training performance.
Viability Score
How well maintained and how widely used is Geti? 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: September 2026
How we score →Key Features
- 67+ pre-trained models for object detection, instance segmentation, image classification
- Model catalog includes YOLO11, YOLO12, YOLO26, D-FINE, RF-DETR, RT-DETR, DINOv3, Mask R-CNN
- End-to-end workflow: data upload, annotation, training, optimization, inference
- Smart annotation assistants
- Model export to OpenVINO IR, ONNX, PyTorch
- Precision support: INT8, FP16, FP32
- Inference pipelines to MQTT, webhooks, local folders
- Data import from Datumaro, YOLO, COCO, Pascal VOC
- Live camera stream ingestion
- Docker images for Intel XPU and NVIDIA CUDA
- Windows native app via MSIX installer
- Run from source
- Open source under Apache 2.0
- REST API + OpenAPI specs (v2 and v3)
- Part of Intel Open Edge Platform
About Geti
Geti™ is Intel's free, open-source platform for building production-ready computer vision models. It's designed for domain experts and data scientists who want to move from raw data to deployed models quickly, without deep coding. The platform covers the entire workflow—data upload, annotation, training, optimization, and inference—in one tool. The platform ships with a curated catalog of 67+ state-of-the-art models across object detection, instance segmentation, and image classification. You get a wide range of choices: from mobile-friendly YOLO variants (YOLOX, YOLO11, YOLO12, YOLO26) to transformer-based DETRs (D-FINE, RT-DETR, DINOv3) and classic Mask R-CNN backbones. Every model exports automatically to OpenVINO IR, Intel's inference runtime, so you can optimize for speed, accuracy, or footprint. Geti supports smart annotation assistants to speed up labeling, and it ingests data from Datumaro, YOLO, COCO, and Pascal VOC formats. Once trained, you can wire predictions to MQTT, webhooks, or local folders through built-in inference pipelines. Deployment is flexible: use Docker images for Intel XPU or NVIDIA CUDA, install the Windows native app via MSIX, or run from source. The whole thing's under Apache 2.0, so you're never locked in. Compared to cloud SaaS platforms like Roboflow or Azure Custom Vision, Geti gives you full control and zero licensing cost, but it's self-hosted and local-first. That makes it a strong fit for teams already in the Intel ecosystem or those who need an open, customizable pipeline for edge deployment.
Behind the Verdict
Geti stands out as a genuinely free, open-source computer vision platform with an unusually deep model catalog. The 67+ models span object detection, instance segmentation, and classification, including YOLO11/12/26, D-FINE, and DINOv3, all auto-exported to OpenVINO IR. That means you can go from raw data to optimized edge inference without leaving the platform, which is a big time-saver. Strengths: The end-to-end workflow—data import, smart annotation, training, optimization, and inference pipelines—is well integrated. Smart annotation assistants cut labeling time, and the option to export to ONNX or PyTorch gives you portability. The Apache 2.0 license and local-first deployment (Docker, Windows app, source) mean no lock-in and full control. For teams already on Intel hardware, OpenVINO optimization (INT8, FP16, FP32) is a clear win. Weaknesses: It's self-hosted, so you need some technical setup—Docker or source—and there's no cloud-hosted version. It's computer-vision only; no NLP or generative AI support. The focus on OpenVINO means if you're not on Intel, you may lose optimization benefits, though ONNX export helps. Beginners may find the setup and workflow steep without some ML background. Where it fits: Teams that want free, open-source CV development with edge deployment on Intel. Good for domain experts who need to train models without heavy coding, and for organizations that want to avoid per-seat cloud costs. Where it doesn't: If you need a fully managed cloud platform, or if your deployment targets are non-Intel and you can't leverage OpenVINO optimizations, you might be better off with Roboflow or Azure Custom Vision.
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Real-world workflow fit
Concrete scenarios for the personas Geti actually fits — and what changes day-one when you adopt it.
You have a few hundred labeled images of defective parts on a production line. You want a model to flag defects in real time.
Outcome: Import images into Geti, use smart annotation to speed up labeling, train a YOLO11 model, optimize with OpenVINO, and deploy via Docker to an edge device that sends MQTT alerts on defect detection.
You need to prototype a real-time instance segmentation model for autonomous checkout, but you're evaluating edge hardware.
Outcome: Use Geti to test RF-DETR or YOLO11-Seg, export to OpenVINO IR for Intel NUC or CUDA for NVIDIA Jetson, and compare performance before committing to a platform.
You want to deploy a vision system for safety monitoring, but you're wary of cloud dependency.
Outcome: Install Geti on-prem via Docker, train a classifier for helmet detection, and run inference locally with webhook alerts to your internal systems.
Use Cases
- Build a custom object detection model for warehouse inventory tracking with minimal labeled data.
- Deploy a real-time instance segmentation pipeline on edge devices using OpenVINO-optimized models.
- Rapidly prototype an image classifier for quality inspection on a manufacturing line.
- Integrate live camera streams to detect anomalies in smart city infrastructure.
- Export a trained model to ONNX for use in a cross-platform application.
- Create an end-to-end vision pipeline with MQTT notifications for automated sorting.
Models Under the Hood
as of 2026-08-27
Limitations
- Geti is a self-hosted platform: you must deploy it yourself via Docker, Windows app, or from source; there is no cloud-hosted offering noted in the evidence.
- It is focused exclusively on computer vision (object detection, instance segmentation, image classification) and does not mention support for NLP or generative AI.
- Model optimization and deployment are built around OpenVINO, with the homepage emphasizing Intel XPU and NVIDIA CUDA as supported acceleration targets.
as of 2026-08-25
Verification history
We have re-verified Geti 7 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-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-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
Showing the 6 most recent of 7 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 Geti tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source
$0/mo
Ideal for
Anyone who wants a free, self-hosted computer vision platform with full model access and no licensing costs.
What this tier adds
This is the only tier: free and open source under Apache 2.0, with all features included.
Where the pricing makes sense
The company stage and team size where Geti's pricing actually pencils out — and where peers do it cheaper.
Geti is free forever, making it a strong value compared to cloud services like Roboflow (which charges per user and usage) or Azure Custom Vision. It's ideal for teams that can self-host and want to avoid per-seat costs.
Setup time & first value
How long it actually takes to get something useful out of Geti — broken out by persona, not the marketing-page minute.
For a data scientist familiar with Docker: you can have Geti running in about 30 minutes and start training with a basic dataset within a few hours. Domain experts with less technical background may take half a day to get the environment set up and learn the workflow.
Switching to or from Geti
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Roboflow: Export your dataset in COCO or YOLO format and import it into Geti via its data import feature.
- →From Azure Custom Vision: Export your model and labels, then retrain using Geti's model catalog; import your images and labels manually via COCO format.
- ↗To Roboflow: Export your trained model to ONNX or PyTorch and re-upload your dataset to Roboflow if you need a cloud-hosted option.
- ↗To a custom deployment: Export to OpenVINO IR or ONNX and integrate into your own inference server, since Geti is open source.
Integrations
Resources & Guides
Tutorials & Learning
![getの使い方① 英会話でよく使うget+形容詞（〜になる）の表現を練習しよう [#166]](https://img.youtube.com/vi/bHGnOdgCe3I/mqdefault.jpg)
Official links
Tools that pair well with Geti
Common stack mates teams adopt alongside Geti, with the specific reason each pairing earns its keep.
Opendatacam
Open source computer vision to count and track moving objects from cameras or video on your own hardware.
Unrealcv
Open-source Unreal Engine plugin for synthetic data generation and computer vision research.
Dlib
Open-source C++ toolkit for machine learning, computer vision, and deep learning.
Featured Head-to-Head Comparisons
Geti vs Temporal Ai
Temporal and Geti serve completely different domains: Temporal is for building resilient, long-running workflows—perfect for AI agents and microservices orchestration—while Geti is a specialized open-source tool for computer vision model development with OpenVINO. Your choice hinges on whether you need durable execution (pick Temporal) or CV prototyping on Intel hardware (pick Geti). For most users, this isn't a direct comparison; it's about your workload type.
Geti vs Spider Cloud
These tools are not substitutes—they solve completely different problems. Choose Spider Cloud if you need to scrape or crawl web pages at scale for AI agents or RAG, especially with natural language commands via the new Browser AI. Choose Geti if you are building computer vision models (object detection, segmentation) on a tight budget and plan to deploy on Intel hardware. Your decision hinges on your data source: web text vs. images.
Geti vs Voyage Ai
Choose Voyage AI if you need high-accuracy retrieval for enterprise RAG on specialized domains like finance or legal, and your budget allows custom pricing. Choose Geti if you're building computer vision models for free on Intel edge hardware, and you want an open-source end-to-end pipeline. They solve completely different problems.
Alternatives to Geti
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Open source computer vision to count and track moving objects from cameras or video on your own hardware.
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