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//Tabnine vs.
Sourcegraph Cody//

Unlike Cody, Tabnine gives you full control over our AI code assistant by letting you choose what data it uses to add context from your environment. You can also set the privacy and protection trade-offs that fit your security and compliance policies.
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Why enterprises and individual developers choose Tabnine over Sourcegraph Cody

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Privacy

Maintain complete control over your data. Your code isn’t stored, isn’t shared with third parties, and isn’t used to train our models.

Control the deployment location. Use Tabnine as a secure SaaS offering (in a multitenant or a single-tenant environment) or do a fully private installation (on-premises or on VPC) to ensure that your code stays in the boundaries of your corporate network. 

Get industry-standard compliance. Tabnine is compliant with SOC 2 Type 2, GDPR, and ISO 9001.

IP protection

Tabnine eliminates IP infringement worries by giving you the option to use license-compliant models.

Personalization

Get highly personalized recommendations. You control what data Tabnine uses to add context from your environment. Tabnine leverages locally available data in the developer’s IDE and lets users connect Tabnine to their organizational code repos to gain global context.

Tabnine also offers model customization, which is extremely valuable when you have code in a bespoke programming language or a language that is underrepresented in the training dataset (like System Verilog). 

Portability

Use Tabnine on any SCM platform. That includes GitHub as well as GitLab, BitBucket, or any Git-based platform.

Use new state-of-the-art LLMs. Tabnine admins have full control over choosing specific models and can connect Tabnine to an LLM endpoint inside their corporate network if needed.

Tabnine integrates with all the major IDEs and supports more than 80 programming languages and frameworks.

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Tabnine recognized as a Luminary in Everest Group’s assessment of AI code assistants

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Learn more
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Tabnine: A more mature product

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Tabnine is the originator of the AI code assistant category, having introduced our first AI-based code completion tool for Java in the IDE in June 2018. Tabnine is now the leading AI code assistant on the market with one million monthly active users. Cody is a relatively new product and this is reflected in lack of support for popular IDEs and significantly low user adoption.
IDE support (in GA)
Visual Studio Code
JetBrains IDEs
Neovim
Visual Studio
Eclipse
Number of downloads
(as of August 7, 2024)
Visual Studio Code
7,644,097
326,423
JetBrains IDEs
3,695,731
155,327
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Side-by-side comparison  

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Check out this detailed comparison of the key capabilities of Tabnine and Sourcegraph Cody.
Code completion
Autogenerates code snippets and full functions as you type
Generates code automatically from written comments
AI chat assistance
Ask general coding questions and get relevant recommendations
Use natural language to generate code based on your specifications
Automatic generation of documentation for selected code
Recommend fixes to selected code with error(s)
Automatic generation of comprehensive test plans with detailed test cases for a specific function or code in your project
Plain language explanation of the purpose and behavior of selected code
Ability to update or refactor selected code
Dedicated onboarding agent to enable developers to quickly onboard to a new project
Tabnine's Code Explorer (a built-in command in Tabnine Chat) enables developers to onboard quickly by returning a high-level summary of the key elements of the project, including things like runnable scripts, key dependencies, and overall structure. It also suggests possible follow-up questions about this summary, allowing the developer to dive further into the relevant information for their current task.
Privacy
Ability to control the deployment location (SaaS, VPC, on-premises)
Customers can consume Tabnine as a secure SaaS offering (in a multitenant or a single-tenant environment) or do a fully private installation (on-premises or on VPC) to ensure that their code stays in the boundaries of their corporate network and isn’t shared with any external party.
Sourcegraph Cody is offered only as a SaaS product.
Support for fully air-gapped deployments
SOC 2 Type 2 compliance
Tabnine offers SOC 2 Type 2 compliance, which is a critical compliance that examines how well the product’s security controls perform over a prolonged period of time.
GDPR compliance
ISO 9001 compliance
Tabnine offers ISO 9001 compliance, which is a globally recognized standard for quality management that demonstrates a company's commitment to maintaining high quality, meeting customer expectations, and improving performance.
Sourcegraph Cody does not offer ISO 9001 compliance.
Zero data retention policy (both for code and usage metrics)
Tabnine offers a zero data retention policy. When using Tabnine’s proprietary models, we don’t store customer code, don’t share customer code or usage data with third parties, and don’t use customer code to train our models.
Sourcegraph Cody does not have its own proprietary models and relies solely on third-party LLMs (e.g., models from Anthropic and Open AI) to generate responses to user prompts, both for its chat and code autocomplete. Due to this inherent limitation, Cody sends your LLM prompts (a combination of your prompt and relevant code snippets from your codebase) to these third-party LLM providers. Additionally, if you enable Cody to generate embeddings for your repository to get personalized results, Cody shares a copy of the entire content in your repository with the third-party LLM provider. Even on the Cody Enterprise tier, Sourcegraph retains user prompts (your submissions to Cody, such as a query or request) and responses (outputs returned to you by Cody) for an unspecified duration.
Protection
Enterprise-grade security, confirmed by industry certifications
Availability of license-compliant models
Tabnine eliminates concerns around IP infringement. We’ve trained our proprietary models (i.e., Tabnine Protected model for Chat, and the universal model for code completion) exclusively on permissively licensed code. This ensures that the recommendations from Tabnine never match any proprietary code and removes any concerns around legal risks associated with accepting the code suggestions. Unlike Sourcegraph Cody, we’re transparent about the data used to train our proprietary model and share it with customers under NDA.
The third-party models used by Sourcegraph Cody are trained using a diverse range of publicly available data, which may include copyrighted code. It's possible for Sourcegraph Cody to return code suggestions that match publicly available copyrighted code. If a code suggestion matches proprietary code, there’s a risk that using that suggestion could trigger claims of copyright infringement.
Indemnification against IP violations for any and all generated code
Models
Proprietary models for code completions
Tabnine has its own proprietary model that is purpose-built for software development teams. The proprietary model is fully private and protected and delivers high performance without the risk of intellectual property violations or exposing your code and data to others.
Sourcegraph Cody does not have its own proprietary models and relies solely on third-party LLMs (e.g., models from Anthropic and Open AI) to generate responses to user prompts.
Proprietary models for chat
Tabnine has its own proprietary model that is purpose-built for software development teams. The proprietary model is fully private and protected and delivers high performance without the risk of intellectual property violations or exposing your code and data to others. Tabnine also allows customers to choose the model that underpins its chat. Customers can choose from eight models, including two fully private models from Tabnine and six popular models from third parties.
Sourcegraph Cody does not have its own proprietary models and relies solely on third-party LLMs (e.g., models from Anthropic and Open AI) to generate responses to user prompts.
Switchable models for chat
Tabnine currently offers users 8 different model choices for Tabnine Chat: two custom-built, fully private models from Tabnine, plus popular models from third parties such as OpenAI, Cohere, Anthropic and Mistral. This flexibility enables users to pick the right model based on their use case or a project. Tabnine admins at enterprises have complete control and can choose any specific models for their teams. They can also connect Tabnine to an LLM endpoint inside their corporate network if needed. Tabnine is committed to adding support for new, state-of-the-art LLMs as they become available. This prevents LLM lock in, future-proofs your AI strategy, and enables you to take advantage of all the innovation happening in this space.
Personalization
Uses local code awareness to create more relevant recommendations
Uses access to your company codebase(s) to create more relevant recommendations
Ability to train custom models against your organization’s code
Tabnine offers model customization: you can fine-tune our proprietary model using your own code to create a custom model. Model customization is extremely valuable when you have code in a bespoke programming language or a language that’s underrepresented in the training dataset (such as System Verilog).
Model fine-tuning is not currently available in Sourcegraph Cody.
Product tiers and pricing
Free tier
Pro tier (for individual developers and small teams)
Enterprise tier
IDEs
Support for the development environments your teams use
Tabnine includes support for VS Code, Visual Studio, the JetBrains family of IDEs, Eclipse, and Neovim.
Eclipse, Neovim, and Visual Studio IDEs are not supported.
Programming languages
Support for the programming languages your teams use
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Resources

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Tabnine vs. Sourcegraph Cody blog post >
Product Docs >
Tabnine Plans and Pricing >
AI Code Assistant Buyer’s Guide >