Enterprise organizations need an AI that understands their codebase, respects their standards, protects their intellectual property, and operates within their security perimeter. Most generic AI solutions force them to choose between performance and protection, between innovation and control. This false dichotomy has created an innovation gap for enterprises in regulated industries, forcing engineering teams to either compromise on their needs or forfeit AI-driven productivity gains.
Mature engineering teams in highly regulated and privacy-conscious industries need full control over every aspect of their use of AI for software development: the sources of context the system is able to access, the behavior of agents, and the underlying set of models that power the AI platform.
However, generic AI solutions force engineering teams to trade-off between privacy, performance, and protection. Today, we’re changing that equation with a significant expansion of LLM support within Tabnine Enterprise, a continuation of our mission to deliver a tailored AI software development platform that fundamentally alters how enterprises can deploy and control their AI infrastructure.
We’re announcing native support for Llama 3.3 70b and Qwen 2.5 32b within our enterprise self-hosted offering, plus introducing a groundbreaking capability that enables you to integrate any LLM of your choice in self-hosted environments. Whether you need to deploy highly optimized open-source models, leverage models you’ve fine-tuned internally, or integrate specialized third-party models, Tabnine now allows your organization to seamlessly incorporate these models into our AI software development platform.
This expanded support gives enterprise teams unprecedented flexibility to select the optimal models for their specific development needs while maintaining complete control over deployment architecture, data sovereignty, and security protocols. In this blog, we’ll explore the transformative expansion of LLM support in Tabnine Enterprise Self Hosted deployments and the benefits for organizations in regulated industries with teams working on highly sensitive intellectual property.
The emergence of breakthrough open-source models in 2025 has shown that the LLM arms race is far from over. In addition to the rapidly growing set of options of models available on the market, many of our existing customers are developing their own fine-tuned large language models, proprietary small language models, and creating Mixture of Experts (MoE) systems for task-based model selection.
Our new capability gives Tabnine Enterprise self-hosted customers complete control over their model mix and gives them a streamlined path to harness the unique strengths of emerging models. Our platform makes integrating models into your SDLC and engineering workflows easy. Additionally, our customers benefit from the model agnostic enhancements offered by Tabnine’s context engine, AI agents, and deep set of integrations into IT systems. This agility ensures that your enterprise AI strategy evolves at the same rate as technology.
Open-source models like Llama, Qwen, and Deepseek are showing performance on par or greater than closed-source offerings. Our engineering team continually evaluates new models, and we’re happy to announce that Llama 3.3 and Qwen 2.5 are now available for Tabnine Enterprise Self Hosted customers.
Llama 3.3 delivers exceptional performance on complex enterprise programming tasks through its enhanced context window handling implemented via rotary positional embeddings. At the core of this is a more effective token prediction through advanced attention mechanisms, allowing Llama 3.3 to maintain coherence across the long sequences of code found in enterprise applications.
The Llama 3.3 model is able to make better use of Tabnine’s context engine, increasing the quality of AI recommendations with a more accurate understanding of complex, multi-file codebases with intricate dependencies and architectural patterns. In practical terms, Llama 3.3’s context capabilities result in a 42.5% success rate on challenging programming problems.
Llama 3.3 also offers significant efficiency advantages with a 40% lower memory footprint compared to similarly sized alternatives. This efficiency comes from optimized model parallelization, enabling more effective resource utilization in production environments. The model supports efficient 8-bit quantization with minimal performance loss, making it particularly well-suited for enterprise GPU infrastructure where resource optimization directly impacts the operational costs of AI-assisted software development. From a security perspective, Llama 3.3 demonstrates improved resistance to prompt injection attacks and enhanced ability to detect and flag potential security vulnerabilities in generated code.
Qwen 2.5 offers complementary capabilities and exceptional performance in maintaining consistent code style across complex enterprise projects. It has a 94% success rate in adhering to established patterns and strongly performs in generating idiomatic code across multiple programming languages. This capability is increasingly important as enterprise development teams work with heterogeneous technology stacks that span various languages, frameworks, and architectural patterns.
Our internal evaluation has also determined that Qwen is highly performant at refactoring complex enterprise codebases, making it particularly valuable for code modernization initiatives and technical debt reduction programs. Like Llama, Qwen offers efficient deployment in resource-constrained environments, with strong performance in containerized deployments and effective vertical scaling characteristics.
Beyond specific model support, Tabnine’s new capability gives our customers true architectural freedom. Use models from any provider—or models they’ve developed internally—while benefiting from the full set of personalization, privacy, and protection features offered by Tabnine.
The model flexibility capability includes standardized interfaces for model integration, deployment orchestration tools, and comprehensive monitoring that delivers consistent performance and protection for any models you’d like to use.
Model flexibility through Tabnine gives you the freedom to incorporate organization-specific data to improve the quality of AI-generated code while eliminating the risk of leakage of intellectual property. Maintaining data sovereignty throughout the development lifecycle creates a sustainable path to continuous improvement that doesn’t compromise security or compliance.
From an operational perspective, this level of architectural control enables you to maintain predictable costs as AI for software development enters the agentic era, regardless of usage patterns or scale across your development organization. By deploying models within your own infrastructure inside Tabnine’s enterprise AI software development platform, you eliminate unpredictable usage-based pricing – reducing the cost of running models to the cost of hardware.
Last and most important, Tabnine’s enterprise AI software development platform enables you to enforce consistent security and compliance across your entire AI footprint through the use of our code review agent, customized rule sets, and code provenance and attribution.
The future of AI in enterprise software development belongs to organizations that maintain control of their AI destiny—organizations that leverage advanced capabilities without compromising on security, compliance, or control. With Tabnine’s expanded LLM support, that future is now fully controlled by enterprise development teams across regulated industries.
Model flexibility is available exclusively to Tabnine Enterprise Self-Hosted customers. Contact your Customer Success Manager to get started.
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