There’s been a lot of noise recently around ChatGPT’s ability to write code. But when it comes down to it, is it really an effective AI code assistant for developers and R&D enterprise teams?
To fully understand the main differences between Tabnine Enterprise vs. ChatGPT Plus, we’ve put together a list of parameters that, as developers with years of experience serving the dev community, best reflect the needs and challenges of R&D orgs:
- Main use case: The use cases for which the tool was designed and is most useful
- Code privacy: Privacy controls offered by each solution
- Open source compliance: Each company’s practices regarding the code that the AI models are trained on
- Context awareness: What level of context can the different AI models take into account when providing suggestions?
- Ability to train AI models on private code: The code that the AI models can be trained on
- Centralized configuration: The type of centralized configuration and management offered to customers
- Price: Price point for each user in the organization
- User management: The types of user management available
- Payment methods: The methods of payment available
| Parameter | Tabnine Enterprise | ChatGPT Plus |
|---|---|---|
| Main use case | Edit in context, inline within the IDE as the developer types (no need to copy and paste the code to your project) Aware of the org’s coding practices, styles, and standards Code suggestions are prompted as the developer types, or from natural language requests Code completion as developer types No need to copy/paste the code to your project | Start from scratch Mainly generates code from natural language requests - Web - Developer asks a question and ChapGPT provides a response - Need to copy/paste generated code into compiler and save file names correctly Replaces search and knowledge bases such as Google and StackOverflow |
| Code privacy | Self Hosting - ability to install on your VPC or on-premise Your code is never sent to Tabnine, other than info needed to manage your licenses and get updates | User interactions are used to train the NLP model Generated code may be used to train machine-learning models |
| Open source compliance | Totally secure - Tabnine doesn’t train its AI models on code with non-permissive licenses Full transparency regarding the code that the models are trained on Provides full attribution, allowing users to view references and avoid future legal issues | Based on GPT-3 Training data used to develop the models is from a variety of sources, including open source software with permissive or non-permissive licenses like GPL No guarantee that code won’t include snippets from non-permissive open source like GPL |
| Context awareness | Understands and leverages relevant context from your project Code suggestions use org’s specific naming conventions and coding styles | The user needs to provide detailed context to ChatGPT in order to generate relevant, usable code. This is almost impossible to do correctly, and can also have an impact on your code privacy (user interaction). Generated code is boilerplate and needs to be adapted to the org’s environment. |
| Ability to train AI models on private code | Supported Tabnine also allows the organization to choose which code the models should be trained on Connect AI models to different repos for different teams | Not supported - AI models are trained only on OpenAI |
| Centralized configuration | Supported, allowing you to configure several features and capabilities from one place, including security and privacy requirements, connecting AI models to private code repos, manage access roles and permissions, get advanced reporting on usage, and manage subscriptions | Not supported |
| Price | $20 per user per month Additional cost for deployment on your VPC or on-premise | $20 per user per month |
| User management | Supported, allowing you to manage access roles and permissions | Not supported |
| Payment methods | Credit card + invoice | Credit card |
Drilling down further into Tabnine Enterprise vs. ChatGPT Plus
This section takes a more in-depth look at how the two solutions compare.
Main use case
Tabnine’s code suggestions are context-sensitive and inline within the IDE, prompted as the developer types, or from natural language requests. There’s no need to copy and paste the code to your project. In addition, Tabnine’s AI models are aware of the organizations coding practices, styles, and standards, which is reflected in the accuracy of the code suggestions.
ChatGPT, on the other hand, can only code from scratch, and generates this code mainly from natural language requests, which requires providing detailed instructions and context, and then, obviously, adaption to the customer’s environment. Essentially, ChatGPT functions as a replacement for search and knowledge bases, such as Google and StackOverflow.
Code privacy
Tabnine Enterprise ensures full and complete privacy for its enterprise customers’ code:
- Customer code and training data are never sent to Tabnine
- Tabnine’s general AI models are never trained on customer code
- Tabnine Enterprise customers can install Tabnine Enterprise on a VPC or on-premise
ChatGPT Plus, however uses user interaction data to train its models. It also may use the code it generates to train its AI models.
Code suggestion format
Tabnine’s code completion works directly within the developer’s IDE, offering whole-line and full-function suggestions as the user codes (or via natural language hints).
On the other hand, ChatGPT Plus only works on the dedicated ChatGPT website, generating code in response to request. In order for the generated code to relevant, the developer needs to provide multiple directions and instructions. Additionally, the generated code then needs to be copy/pasted into the IDE. This requires changing names, paths, etc. where required, and can lead to bugs and other issues.
Open source compliance
The code on which a solution’s AI models are trained can have legal implications for the companies that use the solutions.
Tabnine’s AI models are never trained on code with non-permissive licenses, and offers full transparency and attribution. This ensures that Tabnine isn’t restricted by the copyleft provisions of GPL license’s, and protects our users and customers from possible related consequences. This policy is also inline with Tabnine’s goal to honor the intent of code authors and maintain good faith with the rest of the developer community.
However, ChatGPT trains its models on OpenAI, which could result in possible legal implications for its customers. There’s also evidence that ChatGPT has copied whole sections of non-permissive coding, creating additional possible legal liabilities for it’s users.
Context-awareness
The ability of the AI models to understand and account for context has a major impact on the amount of effort required by both the entire developer and the entire R&D team to generate high-quality code that aligns with the org’s own best practices, conventions, and styles.
Tabnine has the ability to understand relevant context from your project’s existing code as well as the organization’s private code repositories that our AI models are trained on.
When using ChatGPT Plus, the developer interaction is far more complex, and providing the relevant context when composing a code request is practically impossible. Since the code provided is boilerplate, it requires the context to be provided in detailed, natural language, often needing multiple iterations. Even after being generated, considerable effort is required to copy/paste the code and adapt it to the relevant environments.
Ability to train AI models on private code
ChatGPT Plus is trained only on OpenAI, while Tabnine Enterprise gives our customers the ability connect our AI models to their own code repositories. It’s also possible to connect different models to different repos specific to certain teams. This enables the models to learn the org’s best practices, styles, naming conventions, and more, providing code suggestions that are both context-sensitive and relevant. In addition, this helps companies onboard and train new team members and junior developers way faster, while removing the burden from senior devs.
Centralized configuration
ChatGPT Plus doesn’t offer any type of centralized configuration or management.
Tabnine Enterprise’s centralized configuration allows organizations to:
- Configure the platform for your org’s security and privacy requirements
- Connect AI models to different repos for different teams
- Manage access roles and permissions
- Advanced reporting to monitor usage
- Manage subscriptions
User management
Tabnine allows enterprise customers to configure and manage user roles and permissions. ChatpGPT Plus doesn’t offer any user management capabilities.
About Tabnine AI for enterprise
Tabnine is an AI assistant tool used by over 1 million developers from thousands of companies worldwide. Tabnine Enterprise has been built to help software engineering teams write high-quality code faster and more efficiently, accelerating the entire SDLC. Designed for use in enterprise software development environments, Tabnine Enterprise offers a range of features and benefits, including the highest security and compliance standards and features, as well as support for a variety of programming languages and IDEs.
