At a glance: A local model can keep code closer to your own infrastructure, but using an open-source agent does not automatically make a workflow private, secure or faster than a hosted coding assistant.
What is a private coding-agent stack?
A coding agent differs from autocomplete because it can read files, propose a plan, edit several modules, execute commands and inspect test failures. A private stack combines an agent harness such as OpenHands, Aider or Continue with a model served through software such as Ollama or another local inference runtime. It also needs a sandbox, source-control isolation and an approval policy for tool calls. The word local describes where inference runs, not necessarily where logs, telemetry or model downloads travel.
A common starting architecture is editor → agent → local model server → temporary repository worktree → test runner. Network egress is separately controlled. Developers should verify the actual model backend configured in the agent: an open-source agent pointed at a cloud API is still sending prompts to a remote service.
Local vs cloud: the comparison that matters
| Decision factor | Local open-weight model | Cloud coding copilot |
|---|---|---|
| Code residency | Inference may stay on-device if all services, retrieval and logging are local. | Prompts and context are sent to the provider under its product and enterprise policies. |
| Upfront cost | Hardware, setup and maintenance; electricity and operator time continue. | Subscription or usage-based costs; less local infrastructure. |
| Capability | Highly variable by model size, context and quantization. | Often stronger on long, complex, tool-intensive tasks, depending on model and tool. |
| Latency | Can be quick on small models with sufficient VRAM; slower on CPU or larger models. | Affected by internet, queueing, rate limits and remote inference. |
| Control | Fine-grained sandbox, model and network controls are possible. | Vendor configuration and data terms determine controls. |
How much GPU memory do you actually need?
As a rough planning illustration—not a universal benchmark—a quantized 7–8B-parameter model may fit in roughly 6–10 GB of VRAM after accounting for weights and runtime overhead. Larger models, long contexts and concurrent agent sessions can require substantially more. KV cache, model architecture and offloaded layers matter. A machine with a 16–24 GB GPU is a more flexible experimentation platform, but it is not proof that it can run any particular model at an acceptable speed.
Record tokens per second, first-token latency, peak VRAM, accuracy of code changes and elapsed time to passing tests. Report the exact GPU, quantization, prompt length, runtime version and agent settings. Comparing a single local GPU run with a cloud vendor’s published leaderboard score is not a fair head-to-head.
A reproducible five-step evaluation
- Pick 10–20 representative repository tasks: a failing test, a refactor, a dependency update and a new feature; freeze the code revision.
- Use the same issue prompt, test suite, time budget and permitted tools for every model–agent pair.
- Isolate each trial in a disposable worktree or container; restrict credentials, outbound traffic and package installation.
- Score tests passed, unintended edits, security regressions, engineer review time and total cost; record failures as well as wins.
- Repeat runs to expose variance instead of declaring a winner after one successful demo.
The privacy traps in a supposedly local setup
Secrets can leak through terminal output, diagnostic logs, telemetry, retrieval services, package registries and commits. The agent may also encounter prompt-injection instructions hidden in repository files. Limit file permissions; rotate exposed tokens; review diffs before merge; require confirmation for destructive commands; keep a separate dependency and vulnerability scan. A local model reduces one data-transfer pathway rather than eliminating the security model.
What the benchmarks do—and do not—prove
SWE-bench Verified evaluates issue-solving on curated Python repositories. That is useful evidence, but not a measurement of proprietary enterprise repos, UI work, secure operation or total cost. The OpenHands Index broadens evaluation to tasks such as greenfield development. Benchmark entries can pair an open-source harness with a paid cloud model, so separate the agent licence from the inference backend before comparing.
Who should choose which stack?
Local inference is attractive for sensitive source code, intermittent connectivity, highly customized pipelines and workloads with predictable scale. Cloud copilots may win when complex reasoning quality, huge model capacity and zero-maintenance deployment matter more. A hybrid setup can keep confidential code review local and send only approved generic tasks to a cloud model.
Related on BCC: Mistral Large 4 and open-weight AI.
Related on BCC: AI pricing and benchmark comparisons.
Frequently asked questions
Does an open-source agent guarantee privacy?
No. Model endpoints, logging, plugins and outbound access must also be reviewed.
Is a faster tokens-per-second result a better agent?
Not necessarily. End-to-end task success, rework and security are more useful measures.