We think it's important for users to make a fully informed decision, so we're committed to maintaining a comparison between our software and our competitors' so you can make the best purchasing decision for your business.
Where does Roboflow fit in?
Roboflow is an end-to-end computer vision platform. We enable companies to turn their image data into actionable information by training custom AI models they can integrate directly into their processes, products, and services without having to hire teams of machine learning experts.
We are industry-agnostic and have Fortune 100 customers in retail, biotechnology, oil & gas, manufacturing, and more. But it's not just a tool for big companies; Roboflow is also used by thousands of students, hobbyists, and startups.
Build vs Buy
Our customers are most often evaluating whether they should use Roboflow or roll their own computer vision pipeline internally. Unless you need to do core machine learning research and development, it's usually faster, cheaper, and more effective to spend your time and resources on your domain problem instead of re-inventing the wheel by building your own tooling.
With Roboflow, your existing software engineering team can use computer vision to solve your business problems; you don't need to hire any expensive machine learning PhDs. You'll also benefit from our battle-tested tooling already used by tens of thousands of developers and your tooling will continue to improve over time without having to devote engineering resources to testing, scaling, and maintaining the infrastructure.
The total cost of ownership of an in-house pipeline is typically over 10x higher than the cost of Roboflow once you account for salaries, infrastructure, and maintenance. And that's not even considering that, with Roboflow, you can have a state of the art custom trained model providing value to your business deployed by next week, not next year.
End to End Platforms
There are some other end-to-end computer vision platforms you may consider. The core difference between Roboflow and these other platforms is that:
- Roboflow's is focused exclusively on building the best computer vision platform. We don't have hundreds of products spanning from quantum ledgers to managing satellites. We do one thing, and we do it well.
- Roboflow is inter-operable with other tools. Our goal is to help you solve your business problems; not to lock you into our platform. If you choose to annotate elsewhere, Roboflow will happily import your existing annotations. And if you choose to experiment with training your own models, Roboflow is built to export your datasets to all of the major machine learning frameworks.
We're even happy to help you use our competitors' point solutions for things like outsourced labeling and AutoML model training if you decide they're better fits for your business than our own solutions in those areas.
- Roboflow is deployment-agnostic. Since our objective is to solve your business problems (not to increase your cloud compute usage) we facilitate using your models in the cloud, on the web, or on device. The choice is yours to make based on the nuances of your particular project.
- Roboflow is dedicated to providing excellent support not just for our software but also to ensure that your project is successful. We're the experts in computer vision and our enterprise tier offers direct assistance at every step along the way from ideation to planning, design, deployment, and beyond.
That said, Amazon, Google, and Microsoft all have their own suite of products that could be considered as alternatives to Roboflow for your end-to-end computer vision workflows.
- Amazon SageMaker - an advanced suite of tools for data science teams to prepare, build, train, and deploy machine learning models. Its scope is much broader than computer vision, but for teams of machine learning experts it can be quite powerful (and expensive).
- Google Cloud Vertex AI - a unified MLOps platform that provides advanced tooling for machine learning workflows across domains including vision, video, natural language, and tabular data for machine learning teams engrained in Google's ecosystem.
- Microsoft Azure Machine Learning - enterprise-grade end-to-end MLOps service to build and deploy models faster. A suite of products built on top of Azure for machine learning tasks ranging from reinforcement learning to natural language processing.
Point Solutions
Roboflow encompasses the entire flow of upload, annotate, organize, train, deploy, improve. There are entire products that focus on each of these steps in the computer vision process that you could use instead of the Roboflow solution (either in conjunction with Roboflow's other tools or combined together with other point solutions to create your own end-to-end pipeline).
We encourage our customers to substitute competing point solutions for our own built-ins when it makes sense for their needs and are committed to making Roboflow interoperable with other labeling, training, and deployment options so you can choose the best software for your needs at each stage of the process.
Upload
Having a single source of truth for your training data is incredibly important once you expand beyond a single person team. You want to make sure everyone is on the same page, working from the same datasets. Coordinating this can be a pain.
Roboflow is the source of record for your project's images and annotations and creates versioned, point-in-time snapshots that you can use to run reproducible experiments.
Faulting Roboflow, the alternative is to roll your own solution and manage the files and sharing yourself using tools like
Unfortunately, most other places to store your datasets emulate a file system when what you really need for computer vision is a database. If you use a filesystem metaphor, you'll need to make sure you're keeping track of which images have been annotated, verified, and processed. This means storing and keeping multiple different files per quantum of training data aligned and up to date.
Annotate
Roboflow Annotate is an ultra-fast self-serve labeling tool for computer vision. It's built for teams to be able to annotate their images in a streamlined way and integrates directly with Roboflow Train models to provide model assisted labeling so your custom trained models' output is your starting point for annotating new images. Since it's integrated directly into Roboflow, your whole team can divide and conquer to annotate your datasets in one shared workspace.
If Roboflow Annotate doesn't serve your needs, Roboflow supports importing annotations from all of the major open source and commercial annotation tools including:
And, if you want to outsource your annotation, it's easy to integrate with whichever third party vendor meets your needs including:
- SageMaker Ground Truth - a popular choice for startups that have free AWS credits to burn; normally their annotations can only be used within the Amazon SageMaker ecosystem but Roboflow Pro can import them for use elsewhere.
- Scale - built their business on annotating images for self-driving cars and now employs thousands of human annotators that they abstract as an API to label your images for you. Scale AI annotations can be dropped directly into Roboflow.
We've also seen teams add contractors from Upwork directly to their teams in Roboflow to closely manage the annotation process, especially when the task requires special training or domain expertise.
Organize
The Roboflow dataset health check is a godsend for improving your model. It automatically analyzes your images and annotations to give you information about potential problems like unlabeled images, class imbalance, and more at a glance.
This is a relatively new space, but some other tools are popping up as well:
- Google Know Your Data - analyzes open source datasets for bias by automatically detecting things like faces, analyzing EXIF data, and more. It can't currently work with custom datasets but they plan to expand it soon.
- Aquarium Learning - uses feature embeddings to help you find unlabeled images most similar to edge cases your model is performing most poorly on so that you can decide what to label next.
- Scale Nucleus - built primarily to assess your models' output to identify areas where your model can improve and to compare results across training runs.
Train
With Roboflow Train, you can get a state-of-the-art model custom trained on your dataset with a single click. Our machine learning engineers are experts in cutting edge machine learning techniques so you don't have to be. Give it a go; you may be surprised at the results it can achieve. Hundreds of developers have already trained a custom model on their dataset with Roboflow Train.
If our built-in models prove to be insufficient for your use-case, you can export and download your versioned dataset (or let us securely host it in the cloud) for training your own models in your framework of choice. We even host a library of open-source computer vision models that you can use as a starting point.
There are several other providers of one-click "AutoML" solutions that you can use with Roboflow Pro.
- Amazon Rekognition Custom Labels
- Google Cloud AutoML Vision
- Microsoft Azure Custom Vision
- IBM Watson Visual Recogntition (deprecated)
We've put them through their paces and published a thorough comparison of these other AutoML tools here.
Deploy
Once you've trained a model in Roboflow, you can deploy it to a wide range of targets. Each trained model gets a custom API endpoint that automatically scales up to handle even the most demanding workloads and scales down to zero when you're not using it. You can also deploy to your users' web browsers and mobile devices and edge devices like the NVIDIA Jetson and Luxonis OpenCV AI Kit. For enterprise users, we support seamless VPC and on-premise deployments of your models to run in firewalled and air-gapped environments.
There are some other options you may consider for deployment if you train your own models including:
- Fritz.ai - for Android and iOS devices, and Snapchat lenses.
- Streamlit - can be used in conjunction with your Roboflow models to create interactive front-end visualizations and user interfaces.
- Or you can roll your own deployment with frameworks like TFLite, OpenVINO, CoreML, and TensorRT (but here be dragons) and host them on your own hardware or virtual machines from the major cloud providers like PaperSpace Gradient.
Next Steps
We encourage you to try Roboflow for free and see if it meets your needs. Our sales team is happy to help you determine if Roboflow is a good fit for your company's computer vision project. Please don't hesitate to reach out!
Cite this Post
Use the following entry to cite this post in your research:
Brad Dwyer. (May 25, 2021). Roboflow Competitors and Alternatives. Roboflow Blog: https://blog.roboflow.com/competitors/
Discuss this Post
If you have any questions about this blog post, start a discussion on the Roboflow Forum.