Technology Radar
In previous editions of the Radar, we put local coding assistants in Caution because the available models were too limited for complex software engineering contexts and weren’t a compelling solution to the concerns they were intended to address. With coding agents now being adopted much more widely than 18 months ago and local open-weight models maturing rapidly, our teams have found local coding models effective in addressing blanket frontier model usage.
Models that can run locally, such as Qwen3.8-27B, have proved particularly useful for workloads that don’t require a frontier model, including codebase indexing, structured log parsing and summarization, initial coding tasks and automated reviews of Git diffs and commits. Running these tasks locally can avoid incremental cloud token costs while also enabling long-running background processing and providing an offline fallback.
Organizations remain wary of third-party AI coding assistants, particularly due to concerns about code confidentiality. As a result, many developers are considering using local coding assistants — AI that runs entirely on their machines — eliminating the need to send code to external servers. However, local assistants still lag behind their cloud-based counterparts, which rely on larger, more capable models. Even on high-end developer machines, smaller models remain limited in their capabilities. We've found that they struggle with complex prompts, lack the necessary context window for larger problems and often cannot trigger tool integrations or function calls. These capabilities are especially essential to agentic workflows, which is the cutting edge in coding assistance right now.
So while we recommend to proceed with low expectations, there are some capabilities that are valid locally. Some popular IDEs do now embed smaller models into their core features, such as Xcode's predictive code completion and JetBrains' full-line code completion. And locally runnable LLMs like Qwen Coder are a step forward for local inline suggestions and handling simple coding queries. You can test these capabilities with Continue, which supports the integration of local models via runtimes like Ollama.
