The marketing pages for AI coding tools tend to flatten those distinctions into the same reassuring language: enterprise-grade, secure by default, privacy-first. But those phrases can describe very different architectures.<\/p>\n
A vendor policy saying customer data is not used for training is not the same thing as an architecture where data never leaves customer infrastructure. Likewise, \u201cself-hosted\u201d does not automatically mean \u201csecure,\u201d and \u201clocal inference\u201d does not automatically mean \u201czero egress.\u201d<\/p>\n
This post compares four generative coding assistants and two static analysis tools commonly discussed by C++ teams working in Visual Studio environments:<\/p>\n
A quick category split before the comparison<\/h2>\n![\"Generative](\"https:\/\/i0.wp.com\/www.wholetomato.com\/blog\/wp-content\/uploads\/2026\/05\/3.png?resize=1024%2C683&ssl=1\")
<\/a><\/h2>\nTwo different jobs get conflated in most AI tool conversations. Generative<\/em> assistants (Copilot, Tabnine, Cody, Continue.dev) write or rewrite code for you. Defensive<\/em> scanners (Snyk Code, SonarQube for IDE) analyze code you’ve already written and flag problems. They sit at different moments in the workflow, and most teams end up using both. Treating them as competitors muddies the comparison, so this post takes each on its own terms.<\/p>\nWhat \u201cnothing leaves your machine\u201d actually means<\/h3>\n
This phrase appears constantly in AI tooling discussions, but it is often used imprecisely. There are multiple independent data paths involved in modern AI-assisted development environments such as:<\/p>\n
\n- inference requests,<\/li>\n
- repository indexing,<\/li>\n
- embeddings generation,<\/li>\n
- telemetry,<\/li>\n
- logging,<\/li>\n
- crash reporting,<\/li>\n
- licensing checks,<\/li>\n
- observability pipelines,<\/li>\n
- and model downloads or updates.<\/li>\n<\/ul>\n
A tool may run inference locally while still sending telemetry externally. Another may keep prompts out of model training while retaining logs temporarily for abuse monitoring or debugging. A self-hosted deployment may still expose data through misconfigured infrastructure, backups, or third-party monitoring systems.<\/p>\n
The goal is not to rank them universally. The goal is to examine where the code and context goes, the overhead maintenance and setup costs, and which tradeoffs appear once the marketing language is stripped away.<\/p>\n
\nWhat actually leaves your machine<\/h2>\n
![\"comparison](\"https:\/\/i0.wp.com\/www.wholetomato.com\/blog\/wp-content\/uploads\/2026\/05\/1.png?resize=1024%2C562&ssl=1\")
<\/a><\/p>\nGitHub Copilot<\/h3>\n
Cloud-only at inference. Every completion routes to GitHub’s servers. There is no local model, no offline mode, and consequently, no air-gap path. The recent semantic C++ tooling upgrade (April 2026) is genuinely useful for Visual Studio users, but it doesn’t change the data flow.<\/p>\n
On the Business ($19\/user\/month) and Enterprise ($39\/user\/month) tiers, GitHub explicitly prohibits using customer code for model training, and prompts and suggestions are not retained. Free-tier terms are looser; so read them before pointing Copilot at anything proprietary.<\/p>\n
The FedRAMP authorization GitHub announced for the Enterprise\/GovCloud path is still in progress as of May 2026. For regulated environments, that’s a gap with no current guarantee.<\/p>\n
Tabnine<\/h3>\n
Four deployment modes spanning cloud SaaS through fully air-gapped on-premises. The cloud SaaS path sends code per request with zero retention claimed. The VPC, on-prem, and air-gapped modes keep everything inside customer infrastructure \u2014 nothing leaves the cluster.<\/p>\n
It’s arguably the strongest story in this category. It also requires Kubernetes and NVIDIA GPU hardware; Tabnine’s reference deployment reports an A100 80GB for roughly 1,000 concurrent users. It’s no longer a developer install, it’s on the level of an IT infrastructure project. The free tier was discontinued in 2025 and the $39 per user\/month plan includes the self-hosted option.<\/p>\n
Tabnine’s developer reviews consistently note that C++ completions are competitive but not best-in-class. You choose it for governance, not raw suggestion quality.<\/p>\n
Sourcegraph Cody<\/h3>\n
Codebase-aware assistant: cloud for individuals, self-hosted with a local LLM for enterprise. On Free and Pro, context is sent to Sourcegraph’s cloud on GCP and then to an LLM provider. On Enterprise self-hosted with a local LLM, nothing leaves the network. Both the Sourcegraph indexer and the inference endpoint run inside customer infrastructure.<\/p>\n
A few things worth noting: The Visual Studio extension is actively maintained (~19K installs), not just the VS Code one. Sourcegraph is SOC 2 Type II certified. And Enterprise self-hosted assumes you’re prepared to stand up a full Sourcegraph instance via Kubernetes or Docker Compose, so there’s real infrastructure under the privacy story.<\/p>\n
Autocomplete latency lands around 300\u2013400ms, slower than Copilot or Cursor at 150\u2013200ms.<\/p>\n
Continue.dev<\/h3>\n
Open-source routing layer. You wire it to any model endpoint, including a local inference server. Pointed at Ollama or LM Studio on the same machine, and nothing leaves the device. The extension routes to localhost.<\/p>\n
Anonymous usage telemetry (not code) goes to PostHog by default. You can set CONTINUE_TELEMETRY_ENABLED=0<\/code> to disable it.<\/p>\nThe hard constraint: no Visual Studio support. VS Code and JetBrains only. If your C++ work lives in Visual Studio, Continue.dev is off the table unless the team changes editors. The trust model is also different from the enterprise vendors here. No SOC 2, no IP indemnification. The extension is Apache 2.0 and the source is auditable, and that’s the deal.<\/p>\n
In plain terms:<\/strong> Continue.dev is the closest thing in this list to “zero egress by default,” but the price of admission is leaving Visual Studio behind and accepting open-source-grade assurances instead of vendor contracts.<\/p><\/blockquote>\nSnyk Code<\/h3>\n
AI-assisted static analysis that flags security vulnerabilities as code is written. (Not meant to be used as a completion tool.)<\/p>\n
Files are sent to Snyk’s cloud for analysis, is cached for 24\u201348 hours, and then deleted. The on-prem local analysis engine was deprecated and is no longer available to new customers, so the cloud path is the only path.<\/p>\n
For Visual Studio C++ teams, the more important detail is coverage. C++ is listed as supported in the VS Code extension. The Visual Studio extension lists C#, JavaScript, TypeScript, Python, and Ruby \u2014 C++ is not on that list, and VS 2026 is not currently supported by the plugin at all. There is capability and the IDE gap that don’t line up yet.<\/p>\n
SonarQube for IDE<\/h3>\n
Rule-based static analysis for bugs and security issues. The C++ analyzer is one of the most mature available for Visual Studio.<\/p>\n
Standalone mode runs fully on-device: no account, no code egress, analysis in the IDE. Connected Mode links the IDE to a SonarQube server to sync rule configuration, but analysis still runs locally. The exception is full project scans run through the CLI scanner, which do push source to the Sonar server.<\/p>\n
The IDE plugin itself is free but the C++ analysis isn’t. Community Build doesn’t include C\/C++. Developer Edition starts at around a hefty $2,500\/year, and MISRA C++: 2023 compliance lives in Enterprise and Data Center tiers. For Visual Studio C++ work, this is the strongest local-by-default scanner, at paid-tier price.<\/p>\n
\nThe infrastructure cost of “local”<\/h2>\n
![\"convenience](\"https:\/\/i0.wp.com\/www.wholetomato.com\/blog\/wp-content\/uploads\/2026\/05\/2-revised.png?resize=817%2C545&ssl=1\")
<\/a><\/p>\nA pattern shows up across the strongest no-egress stories: each of them costs something other than the subscription fee. Tabnine air-gapped needs Kubernetes and an NVIDIA GPU. Cody self-hosted needs a full Sourcegraph deployment plus a local LLM. Continue.dev is genuinely local on the developer’s machine, but only in VS Code, and without the vendor-grade compliance posture some teams need.<\/p>\n
A real “nothing leaves the machine” answer usually means accepting infrastructure overhead, accepting platform constraints, or both. There’s no easy version where you get cloud convenience, vendor compliance certifications, and on-device inference all at once.<\/p>\n
Scanners sit alongside that picture rather than competing with it. Snyk and SonarQube for IDE catch problems in code that already exists. Generative assistants help write the code in the first place. A C++ team with confidentiality requirements often ends up using one of each \u2014 they answer different questions at different moments, and there’s no reason to pick.<\/p>\n
In plain terms:<\/strong> “Local AI” isn’t one thing. It can mean a dev-laptop install with a small model, or a self-hosted enterprise stack with a GPU cluster. The right version depends on whether you’re optimizing for individual developer setup or for organization-wide governance.<\/p><\/blockquote>\n
\nA bonus third category: deterministic tools<\/h2>\n
Generative AI and scanners both share one trait: their output varies. The same prompt or the same file can produce different suggestions on different runs. That’s intrinsic to how the models work.<\/p>\n
Deterministic tools sit in a different lane. For instance, Visual Assist’s code generation, refactoring, and code inspections all operate on a parsed C++ AST. Rename a symbol and every reference moves with it. Extract a method and the new signature is derived mechanically from the selection. Run an inspection and the same code produces the same warning every time. No variance, no “mostly right.”<\/p>\n
The tradeoff is scope. A parser can rename, restructure, and flag known patterns; it can’t write novel logic from a natural-language prompt. For the work that fits what the parser knows, deterministic tools are the safer bet. For the work that doesn’t, that’s where an invocation-based tools is a more convenient bet.<\/p>\n
Where VA Intelligence fits<\/h2>\n
Visual Assist’s AI-integration, VA Intelligence, is one more data point in the invocation-based, on-device column. The model fires when you press Shift+Alt+Q<\/code> on a selection. There’s no ambient suggestion stream and user limited background context collection. Inference runs locally through Ollama against a Gemma model on the developer’s hardware (has a minimum of 12 GB VRAM), and two lightweight try-it-first features (Explain with AI and Change Code with AI). These are basic, selection-scoped AI processes that come with diff-review on every change before it’s applied\u2014meant to reconnect the developer with the work they’re doing.<\/p>\nOn C++ codebases where “mostly right” isn’t acceptable, the engineer staying in the loop is the point, not a limitation to apologize for. If you want to read more on how that thinking shaped the product, the VA Intelligence page<\/a> goes deeper.<\/p>\n
\n
![\"Generative](\"https:\/\/i0.wp.com\/www.wholetomato.com\/blog\/wp-content\/uploads\/2026\/05\/3.png?ssl=1\")
<\/a><\/h2>\nTwo different jobs get conflated in most AI tool conversations. Generative<\/em> assistants (Copilot, Tabnine, Cody, Continue.dev) write or rewrite code for you. Defensive<\/em> scanners (Snyk Code, SonarQube for IDE) analyze code you’ve already written and flag problems. They sit at different moments in the workflow, and most teams end up using both. Treating them as competitors muddies the comparison, so this post takes each on its own terms.<\/p>\n This phrase appears constantly in AI tooling discussions, but it is often used imprecisely. There are multiple independent data paths involved in modern AI-assisted development environments such as:<\/p>\n A tool may run inference locally while still sending telemetry externally. Another may keep prompts out of model training while retaining logs temporarily for abuse monitoring or debugging. A self-hosted deployment may still expose data through misconfigured infrastructure, backups, or third-party monitoring systems.<\/p>\n The goal is not to rank them universally. The goal is to examine where the code and context goes, the overhead maintenance and setup costs, and which tradeoffs appear once the marketing language is stripped away.<\/p>\n Cloud-only at inference. Every completion routes to GitHub’s servers. There is no local model, no offline mode, and consequently, no air-gap path. The recent semantic C++ tooling upgrade (April 2026) is genuinely useful for Visual Studio users, but it doesn’t change the data flow.<\/p>\n On the Business ($19\/user\/month) and Enterprise ($39\/user\/month) tiers, GitHub explicitly prohibits using customer code for model training, and prompts and suggestions are not retained. Free-tier terms are looser; so read them before pointing Copilot at anything proprietary.<\/p>\n The FedRAMP authorization GitHub announced for the Enterprise\/GovCloud path is still in progress as of May 2026. For regulated environments, that’s a gap with no current guarantee.<\/p>\n Four deployment modes spanning cloud SaaS through fully air-gapped on-premises. The cloud SaaS path sends code per request with zero retention claimed. The VPC, on-prem, and air-gapped modes keep everything inside customer infrastructure \u2014 nothing leaves the cluster.<\/p>\n It’s arguably the strongest story in this category. It also requires Kubernetes and NVIDIA GPU hardware; Tabnine’s reference deployment reports an A100 80GB for roughly 1,000 concurrent users. It’s no longer a developer install, it’s on the level of an IT infrastructure project. The free tier was discontinued in 2025 and the $39 per user\/month plan includes the self-hosted option.<\/p>\n Tabnine’s developer reviews consistently note that C++ completions are competitive but not best-in-class. You choose it for governance, not raw suggestion quality.<\/p>\n Codebase-aware assistant: cloud for individuals, self-hosted with a local LLM for enterprise. On Free and Pro, context is sent to Sourcegraph’s cloud on GCP and then to an LLM provider. On Enterprise self-hosted with a local LLM, nothing leaves the network. Both the Sourcegraph indexer and the inference endpoint run inside customer infrastructure.<\/p>\n A few things worth noting: The Visual Studio extension is actively maintained (~19K installs), not just the VS Code one. Sourcegraph is SOC 2 Type II certified. And Enterprise self-hosted assumes you’re prepared to stand up a full Sourcegraph instance via Kubernetes or Docker Compose, so there’s real infrastructure under the privacy story.<\/p>\n Autocomplete latency lands around 300\u2013400ms, slower than Copilot or Cursor at 150\u2013200ms.<\/p>\n Open-source routing layer. You wire it to any model endpoint, including a local inference server. Pointed at Ollama or LM Studio on the same machine, and nothing leaves the device. The extension routes to localhost.<\/p>\n Anonymous usage telemetry (not code) goes to PostHog by default. You can set The hard constraint: no Visual Studio support. VS Code and JetBrains only. If your C++ work lives in Visual Studio, Continue.dev is off the table unless the team changes editors. The trust model is also different from the enterprise vendors here. No SOC 2, no IP indemnification. The extension is Apache 2.0 and the source is auditable, and that’s the deal.<\/p>\n In plain terms:<\/strong> Continue.dev is the closest thing in this list to “zero egress by default,” but the price of admission is leaving Visual Studio behind and accepting open-source-grade assurances instead of vendor contracts.<\/p><\/blockquote>\n AI-assisted static analysis that flags security vulnerabilities as code is written. (Not meant to be used as a completion tool.)<\/p>\n Files are sent to Snyk’s cloud for analysis, is cached for 24\u201348 hours, and then deleted. The on-prem local analysis engine was deprecated and is no longer available to new customers, so the cloud path is the only path.<\/p>\n For Visual Studio C++ teams, the more important detail is coverage. C++ is listed as supported in the VS Code extension. The Visual Studio extension lists C#, JavaScript, TypeScript, Python, and Ruby \u2014 C++ is not on that list, and VS 2026 is not currently supported by the plugin at all. There is capability and the IDE gap that don’t line up yet.<\/p>\n Rule-based static analysis for bugs and security issues. The C++ analyzer is one of the most mature available for Visual Studio.<\/p>\n Standalone mode runs fully on-device: no account, no code egress, analysis in the IDE. Connected Mode links the IDE to a SonarQube server to sync rule configuration, but analysis still runs locally. The exception is full project scans run through the CLI scanner, which do push source to the Sonar server.<\/p>\n The IDE plugin itself is free but the C++ analysis isn’t. Community Build doesn’t include C\/C++. Developer Edition starts at around a hefty $2,500\/year, and MISRA C++: 2023 compliance lives in Enterprise and Data Center tiers. For Visual Studio C++ work, this is the strongest local-by-default scanner, at paid-tier price.<\/p>\n A pattern shows up across the strongest no-egress stories: each of them costs something other than the subscription fee. Tabnine air-gapped needs Kubernetes and an NVIDIA GPU. Cody self-hosted needs a full Sourcegraph deployment plus a local LLM. Continue.dev is genuinely local on the developer’s machine, but only in VS Code, and without the vendor-grade compliance posture some teams need.<\/p>\n A real “nothing leaves the machine” answer usually means accepting infrastructure overhead, accepting platform constraints, or both. There’s no easy version where you get cloud convenience, vendor compliance certifications, and on-device inference all at once.<\/p>\n Scanners sit alongside that picture rather than competing with it. Snyk and SonarQube for IDE catch problems in code that already exists. Generative assistants help write the code in the first place. A C++ team with confidentiality requirements often ends up using one of each \u2014 they answer different questions at different moments, and there’s no reason to pick.<\/p>\n In plain terms:<\/strong> “Local AI” isn’t one thing. It can mean a dev-laptop install with a small model, or a self-hosted enterprise stack with a GPU cluster. The right version depends on whether you’re optimizing for individual developer setup or for organization-wide governance.<\/p><\/blockquote>\n Generative AI and scanners both share one trait: their output varies. The same prompt or the same file can produce different suggestions on different runs. That’s intrinsic to how the models work.<\/p>\n Deterministic tools sit in a different lane. For instance, Visual Assist’s code generation, refactoring, and code inspections all operate on a parsed C++ AST. Rename a symbol and every reference moves with it. Extract a method and the new signature is derived mechanically from the selection. Run an inspection and the same code produces the same warning every time. No variance, no “mostly right.”<\/p>\n The tradeoff is scope. A parser can rename, restructure, and flag known patterns; it can’t write novel logic from a natural-language prompt. For the work that fits what the parser knows, deterministic tools are the safer bet. For the work that doesn’t, that’s where an invocation-based tools is a more convenient bet.<\/p>\n Visual Assist’s AI-integration, VA Intelligence, is one more data point in the invocation-based, on-device column. The model fires when you press On C++ codebases where “mostly right” isn’t acceptable, the engineer staying in the loop is the point, not a limitation to apologize for. If you want to read more on how that thinking shaped the product, the VA Intelligence page<\/a> goes deeper.<\/p>\nWhat \u201cnothing leaves your machine\u201d actually means<\/h3>\n
\n
\nWhat actually leaves your machine<\/h2>\n
<\/a><\/p>\nGitHub Copilot<\/h3>\n
Tabnine<\/h3>\n
Sourcegraph Cody<\/h3>\n
Continue.dev<\/h3>\n
CONTINUE_TELEMETRY_ENABLED=0<\/code> to disable it.<\/p>\nSnyk Code<\/h3>\n
SonarQube for IDE<\/h3>\n
\nThe infrastructure cost of “local”<\/h2>\n
<\/a><\/p>\n
\nA bonus third category: deterministic tools<\/h2>\n
Where VA Intelligence fits<\/h2>\n
Shift+Alt+Q<\/code> on a selection. There’s no ambient suggestion stream and user limited background context collection. Inference runs locally through Ollama against a Gemma model on the developer’s hardware (has a minimum of 12 GB VRAM), and two lightweight try-it-first features (Explain with AI and Change Code with AI). These are basic, selection-scoped AI processes that come with diff-review on every change before it’s applied\u2014meant to reconnect the developer with the work they’re doing.<\/p>\n
\n