AWS re:Invent in Las Vegas was a blast. The Tabnine booth had a huge crowd each day, and we gave away three pairs of Meta Ray-Ban Smart Glasses along with free 1-year subscriptions to Tabnine Dev.
In our conversations with customers and attendees, several things stood out that got people excited about Tabnine and conveyed what makes us different from any other AI code assistant vendor in this space today — and why enterprise-grade engineering organizations choose us over vendors like GitHub Copilot and countless others in POC evaluations.
Tabnine’s performance is proven
Tabnine’s AI agents and retrieval-augmented generation (RAG) models have been evaluated by multiple analyst firms and are proven to generate best-in-class performance for AI code assistants. When compared against all 12 leading vendors in this space, Tabnine was top-ranked by Gartner© in all 5 use cases in the 2024 AI Code Assistant Critical Capabilities Report.
Tabnine is the only AI code assistant with complete data privacy and protection for sensitive IP
Tabnine’s AI code assistant is the best choice for enterprise companies in highly regulated and privacy-conscious industries. Unlike other vendors in the AI code assistant space, Tabnine can be deployed on-premises on your own servers and via VPC through AWS, GCP, or Azure. You can also run Tabnine in a completely air-gapped environment.
With an on-premises installation, the solution is completely self-contained and lives entirely within your architecture. If you opt for VPC, the entire solution lives on your private cloud. This completely eliminates any risk of exposing your sensitive IP or data to third-party vendors. You can learn more about our private deployments and how we meet all enterprise security and compliance requirements.
Tabnine is the category leader in AI code assistants
We originated the AI code assistant category launching our first AI in 2018. Since then, we’ve led the market in delivering innovative capabilities in this space. We were the first to bring private deployments, local context awareness, model flexibility, codebase connection, Jira integration, AI agents for every step of the SDLC, model fine-tuning, and customized Code Review agents. When you’re thinking about selecting an AI code assistant vendor, think about who you think is going to deliver the most innovation for the enterprise the fastest — and on that metric, Tabnine continues to be the category leader.
We also have a unique perspective on how to maximize the performance of an AI code assistant by leveraging three factors:: LLMs, AI agents, and personalization through RAG.
Tabnine offers total model flexibility
Tabnine was the first AI code assistant to offer model flexibility. Our agentic and RAG enhancements are fully model-agnostic and significantly improve the relevance of AI-generated code, leading to significant improvements on code acceptance rates.
With our model flexibility, you can connect any LLM or SLM model to Tabnine with an API call. Additionally, if you’re running Tabnine on-premises or on VPC and have your own LLM endpoints available in your architecture, it’s possible to use Tabnine with the best LLMs available while keeping the entire solution entirely self-contained in your architecture, giving you the benefit of the best possible performance while also completely eliminating the risk of IP leakage of your company’s data and competitive advantage. Our internal testing and data have shown that Claude 3.5 Sonnet is the most performant LLM for software development tasks and — when combined with our agentic and RAG enhancements — significantly increases the speed at which engineers can complete every task in the SDLC and simultaneously boosts code quality, performance, maintainability, and security.
Tabnine has AI agents for every step of the SDLC, from Jira issue through code review
We’ve built a set of AI agents that work alongside your developers at every step of their development process. The benefit of these agents is that your developers don’t spend time writing 100-word prompts to get a meaningful response out of your AI code assistant. Our agents take the prompt engineering load off your engineers, enhancing and modifying the prompt entered by your engineers, layering on context from our RAG indexing, and sending the right query to the LLM with the right context, at the right time, for the task at hand.
In contrast with other AI code assistants or out-the-box LLMs, you’ll find that getting usable code from Tabnine is far easier due to our application layer of agentic and RAG enhancements that we feed into the query sent to the LLM.
The agents that generated the most excitement at AWS re:Invent were our Jira Implementation and Validation Agents, Testing Agent, and Code Review Agent.
Jira Implementation and Validation Agents
Tabnine integrates with Jira with two button clicks. From there, each engineer can reference the Jira issues assigned to them by typing @Jira into the chat panel in their IDE or clicking the Jira button in the IDE chat. From here, executing Jira tickets is as simple as typing in “implement” and clicking two buttons. Tabnine reads the description and acceptance criteria from the Jira ticket, considers the local context from workspace indexing in the developer’s workspace, global codebase context from integrated repositories (like Github, Gitlab, or Bitbucket), and executes the Jira ticket.
And with our new Apply button, the workflow becomes even more seamless. Simply click Apply and Tabnine automatically inserts the generated code into the file exactly where it’s supposed to go. Of course, as with all our agents, the process is fully human in the loop with Tabnine explaining the changes it’s made, what the code does, and showing the diff in the code that your engineers can review inline or in the chat before accepting the change.
Testing Agent
Tabnine is the only AI code assistant with a sophisticated testing agent, accessed through the beaker icon in your IDE chat. This agent automates the generation of comprehensive test plans considering the context of your code. If you have a test file already, you can point the agent at this file and it will read it and identify gaps in your test coverage as well as updates that need to be made to tests due to changes in your code. It returns a clear list of test cases with clearly understandable plain language descriptions.
From there, simply click a few buttons and Tabnine generates tests for each test case. These can also be modified with natural language prompting if desired, and then applied using Insert or the Apply button as desired.
Our Testing Agent has significantly reduced the engineering workload required to keep test coverage up to date and at your test coverage target when working in enterprise engineering teams with codebases that constantly change as new features are added and legacy code is updated.
Code Review Agent
Our customers have found that by using our precode review agents, the speed and volume at which they shipped code into review was significantly increasing. If code review wasn’t already a bottleneck at their organization, it certainly became one. That’s why we built and released our Code Review Agent.
You may have seen research from CISO organizations and cybersecurity vendors analyzing LLM-generated code and highlighting security issues with the code. The Code Review Agent solves all of that. We’re the only AI code assistant with a Code Review Agent that’s fully customizable to your organization’s unique coding standards and policies. The agent comes prebuilt with hundreds of code review rules that you can add to at any time.
We also train the agent on your organization’s unique code security, performance, and compliance standards. This can be achieved in two ways. First, if you have natural language documentation of your rules, we can feed that into Tabnine, enhance it and convert it into rule sets organized by language, category, library, and severity. If you don’t have rules documented, simply point Tabnine toward a golden repo with your best code and it can extract and develop a rule set for you.
Rules can also be configured by the user and team to your desire. This agent acts inside the pull request and reviews the code like a fully onboarded engineer at your company. It flags deviations, sets severity ratings for violations, and goes a step further to suggest modifications to the code to bring the pull request into full compliance with your standards. The agent is completely human in the loop: your code reviewer can review all suggested fixes in the pull request, ask for modifications, and then implement them directly in the pull request.
But there are more even agents. Learn more about our Code Fix Agent, Code Explain and Onboarding Agent, and Documentation Agent.
Tabnine’s RAG models are the most sophisticated in the AI code assistant category
Our RAG models can intake context from more and more of your dev tools stack. We offer four levels of personalization that feed into our RAG model. The first level is local context awareness, which looks at 14 different data sources inside the IDE including variables, types, classes, chat history, cursor, highlights, @mentions, open files, dependencies, imported packages, and libraries, just to name a few. Just by turning on the first of four levels of personalization through our RAG model (local context awareness), our RAG produces a 40% increase in code acceptance rates.
That number climbs as you leverage our second level of personalization: codebase awareness (e.g., GitHub, Gitlab, Bitbucket) and noncode data source awareness (Jira/Confluence). The third (customized rule sets for your agents) and fourth level (model fine-tuning) offer the ultimate in customization.
These are all capabilities that we offer today. In plain English terms, unlike other AI code assistants, Tabnine actually produces code that fits your existing project, patterns, and company standards. We can do this because our RAG models are the most sophisticated models available on the market today.
Conclusion
Tabnine is the category leader and creator of the AI code assistant category. We’re willing to go head to head in any POC evaluation for enterprise engineering teams and we outperform all 12 of the major AI code assistant vendors in the space through our unique approach to maximizing AI performance and focus on delivering AI code recommendations that meet the unique engineering requirements relevant to your team, products, and codebase.
Tabnine supports over 600 languages libraries and frameworks; can be installed as a plugin for every major IDE; integrate with GitHub, Gitlab, and Bitbucket repos; can be deployed fully air-gapped or VPC; and are completely model agnostic.
If you’d like to get started evaluating Tabnine, simply contact us or chat with one of SDRs in our website chat. We’re well experienced in servicing the needs of large engineering teams at enterprise-level organizations. We can guide you through security and legal review, support you with an evaluation of Tabnine to get it into your engineers’ hands so they can experience the difference themselves, and have Customer Success and Solutions Engineering teams distributed across North America and Europe that can accommodate any time zone.
If you’d like an end-to-end demo of Tabnine, you can register for our weekly Office Hours sessions every Wednesday. If you’d like to take Tabnine for a spin yourself first, download our free plugin or sign up for a 30-day trial of our individual Tabnine Dev plan. These plans will not have the full set of agents, deployment, and RAG capabilities explained in this post, so if you’d like to try it all, doing an evaluation of Tannine Enterprise is the best way forward.
Thanks for joining us at AWS re:Invent!
