JetBrains
Subscription software for developers, engineering teams and DevOps workflows.
Available information varies by company and source.
Profile record updated:
Company facts
- Official name
- JetBrains s.r.o.
- Entity type
- COMPANY
- Founded
- 2000
- Headquarters
- Netherlands
- Company size
- 1,001–5,000
- Market role
- B2B SaaS Provider
- Official website
- jetbrains.com
What JetBrains does
JetBrains operates a multi-product B2B software model centred on developer productivity and engineering workflow tooling. It builds proprietary desktop, cloud-enabled and team software products, distributes them directly under recurring licences, and expands account value by selling multiple tools into the same engineering organisation. The model combines single-product subscriptions, bundled suites, enterprise features and adjacent tooling for collaboration, automation, code quality and AI assistance. Product-led adoption by individual developers supports wider team and enterprise standardisation.
Category differentiation
JetBrains is a developer-tools software vendor, not an adtech, martech or media company. It sells engineering productivity software and related subscriptions rather than consumer apps or advertising inventory.
Strategic context
AI-supported assessment from the existing company research; distinguish interpretation from sourced facts.
JetBrains is a private software company that develops commercial tools for professional software development teams. Its portfolio includes integrated development environments for multiple programming languages, DevOps and team collaboration products such as TeamCity and YouTrack, code quality tooling such as Qodana, collaborative data science tooling such as Datalore, and AI-assisted developer products embedded across its ecosystem. The company sells primarily to developers, engineering teams, DevOps teams, data teams and enterprise software organisations. The business generates revenue through subscription-based software licensing across individual products and bundled plans, including the All Products Pack. Its value proposition is rooted in tightly integrated developer workflows, strong language-specific IDEs, and cross-sell across adjacent engineering use cases such as project management, CI/CD and AI-assisted coding. JetBrains remains privately held and has not raised external capital according to the provided evidence.
Company news briefing
Briefing updated:
JetBrains has furthered its AI-driven ecosystem with the release of Mellum2, a 12-billion-parameter Mixture-of-Experts model licensed under Apache 2.0 and optimised for multi-model collaboration. To support agentic development, the company introduced the Kotlin Agent Development Kit (ADK) and Model Context Protocol (MCP) Kotlin SDK. Enterprise functionality has also expanded through GitHub Copilot telemetry streaming from JetBrains IDEs and the integration of AWS Kiro Crew for orchestrated multi-agent code reviews, while the potential incorporation of Anthropic’s Claude Opus 5 continues to diversify high-performance model options within its suite.
Business model & monetisation
JetBrains monetises through recurring software subscriptions sold per user, per tool and via bundled suites. The company offers individual product subscriptions for IDEs and team tools, higher-value bundle pricing through the All Products Pack, enterprise-grade paid tiers for governance and collaboration features, and paid AI capabilities embedded into its tooling stack. Free trials, community editions and renewal discounts support acquisition and retention, while cross-selling across the portfolio increases lifetime value.
- IDE subscriptions
- Software Subscription
- Team tools and DevOps products
- Software Subscription
- Bundled suite subscriptions
- Software Subscription
- AI tooling add-ons and premium tiers
- Software Subscription
Products & capabilities
No products with linked sources are available in this view.
Products & market categories
Competitors & alternatives
- Atlassian
Enterprise software for collaboration, workflows and team productivity.
- CodeRabbit
AI code review software for pull requests, IDE and CLI.
Side-by-side comparisons
Recent recorded signals
Dates refer to the source publication. Older entries are historical context, not evidence of a new event.
AI Agents Redefine Software Developer Role Toward Context
AI · Recorded impact score: 1/5
This opinion piece examines how AI coding agents are reshaping software development, shifting developers' value from implementation to understanding business context and making architectural decisions. JetBrains' survey shows 90% of professional developers use such tools weekly (68% daily), with about a third now 'agentic coders' who let AI generate an average of 84% of their code. This transformation has popularized the 'Forward Deployed Engineer' role at OpenAI and Anthropic, which focuses on client collaboration. The author argues that while AI simplifies coding, experienced developers' true worth lies in contextual understanding, risk identification, and solution validation. The piece advises clients to prioritize contextual insight over mere technical capacity.
- 90% of professional developers use coding agents at least weekly, 68% daily (JetBrains Developer Ecosystem Survey).
- About one-third of developers are 'Agentic Coders', with AI generating 84% of their code on average.
How AGI Became a Marketing Department Case
AI · Recorded impact score: 2/5
The article discusses how AI, particularly coding agents, is transforming the role of software developers. It highlights that 90% of professional developers use such tools weekly, with 68% daily, and about a third are 'agentic coders' who generate 84% of their code via AI. This shift is changing the developer's role from generalist to specialist, and now to a 'Forward Deployed Engineer' who works closely with clients. The article emphasizes that the real value of developers is shifting from pure coding capacity to understanding business context, identifying problems, and making sound technical decisions. It draws on examples from Google Research and the hiring practices of OpenAI and Anthropic, and suggests that businesses should focus on teams that can quickly build domain context rather than just counting developers.
- 90% of professional developers use coding agents weekly, 68% daily (JetBrains survey).
- One third of developers are 'agentic coders' generating 84% of their code via AI.
On-Device LLM Chatbot with Kotlin and TensorFlow Lite
Conversational AI · Recorded impact score: 2/5
This technical tutorial describes how to build an on-device large language model (LLM) chatbot for Android using Kotlin and TensorFlow Lite. It outlines a simple architecture (Chat UI -> ViewModel -> LLM repository -> Tokenizer -> TensorFlow Lite interpreter -> Local model), project setup, model loading, tokenization, background inference with Kotlin coroutines, incremental token handling, conversation-history management, quantization options (FP16, INT8, weight-only) and mobile performance metrics to benchmark (load time, first-token latency, tokens/sec, RAM, battery, thermal). The guide also covers error handling and security considerations (prompts stay on device but APK/model extraction risk), and links to example SDK repos and a Discord community.
- Tutorial outlines an on-device chatbot architecture using a Kotlin Android UI, tokenizer, TensorFlow Lite interpreter, and a local model.
- It shows how to load a .tflite model from app/src/main/assets and initialize a TensorFlow Lite Interpreter in a background runner.
Building AI Agents with Kotlin ADK
Large Language Models (LLM) & AI · Recorded impact score: 4/5
This tutorial demonstrates a starter "Hello World" AI agent built with the Kotlin Agent Development Kit (ADK). The sample project (on GitHub) shows a Kotlin LlmAgent configured to use Google's Gemini model and to discover and call a local "greet" tool via a Model Context Protocol (MCP) Ktor server. The repo includes two Gradle modules (agent and server), uses Kotlin 2.3.0, Kotlin ADK SDK v0.6.0, MCP Kotlin SDK v0.8.1, Ktor 3.0.0, Java 25, and Gradle 9.2.1. The tutorial covers local runs, unit tests, an MCP smoke test that does not require a Gemini API key, and instructions to containerize and deploy the MCP server to Google Cloud Run.
- Tutorial demonstrates a Kotlin ADK LlmAgent that uses Gemini and discovers a local 'greet' tool via Model Context Protocol (MCP).
- Kotlin ADK SDK version referenced: com.google.adk:google-adk-kotlin-core v0.6.0.
JetBrains Releases Mellum2 12B Mixture-of-Experts Model
Large Language Models (LLM) & AI · Recorded impact score: 2/5
JetBrains announced Mellum2, an open-source 12-billion-parameter model based on a Mixture-of-Experts (MoE) architecture released June 1, 2026. Mellum2 activates only ~2.5 billion parameters per inference, which the team says yields inference speeds over twice as fast than equivalent-scale models and reduces deployment costs. The model is optimized for text and code (no multimodal inputs), positioned as a "focused" model for multi-model collaboration systems handling tasks like prompt classification, tool selection, context compression for RAG pipelines, sub-agent planning validation, and code completion. Mellum2 is released under the Apache 2.0 license; a technical report is on arXiv (ID 2605.31268) and model weights are available on HuggingFace. Benchmarks reportedly show competitive performance among open-source models of similar scale.
- JetBrains released Mellum2 on June 1, 2026.
- Mellum2 is a 12-billion-parameter open-source model using a Mixture-of-Experts (MoE) architecture, activating ~2.5 billion parameters per inference.
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Questions about JetBrains
What is JetBrains?
JetBrains is a private software company that builds IDEs, developer tools, CI/CD software, project management tools and AI-assisted coding products for engineering teams.
Who uses JetBrains?
Professional developers, DevOps teams, project managers, database specialists, data teams and enterprise software organisations use JetBrains products.
How does JetBrains make money?
JetBrains makes money through recurring subscriptions for individual developer tools, team products, bundled suites and premium AI-enabled capabilities.
Sources & coverage
This profile uses public, official and technically observable information. Missing information does not prove that a product or relationship does not exist. The list below does not imply that every profile statement has been verified.
17 publicly documented primary sources and citations linked across the market graph.
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