Local vs cloud coding agents: choose by environment, cost and review

Updated 2026-10-23

Choose local execution when a task needs your machine’s tools or state. Choose hosted execution when work must continue without that machine. Compare model access, billing and review separately: a cloud interface does not tell you where the code runs.

Denly is a control plane for coding agents (Claude Code, Codex, Cursor, Grok Build, Antigravity) that run on your own machines using your vendor subscription or API key. It manages tasks, plans and reviews across projects; Git-backed tasks can open pull requests, while no-Git projects end with a review of local changes. Denly is one way to coordinate local execution. Use the comparison below to decide whether that arrangement fits your work.

Our trade-off, first.Work runs only while your machine is awake and online: queued work waits for an available runner. Your Git host receives pushed code and your AI provider receives the context its CLI sends. Denly receives selected uploads that can contain source; see the data boundary and optional encryption coverage.

Compare three separate decisions

Decision	Local execution	Hosted execution
Where files and tools live	Your runner's checkout, installed tools and reachable services. An isolated task workspace may still need dependency setup.	A remote workspace with its own checkout, dependencies, credentials and network settings. Some services reuse prepared environments.
Where model inference runs	Often at an AI provider even though tools run locally. Local execution is not the same as a local model.	According to the service and model provider. Inspect retention and data policies independently of workspace isolation.
How you pay and approve work	A supported subscription or API key, plus any coordination service and machine costs. Define plan, check and review gates.	The service's subscription, credits or usage billing. Check included limits and the task's review and merge controls.

Denly's Git-backed setup grants scoped repository access so it can supply runner credentials. The working checkout stays on your machine, but selected uploads such as READMEs, docs, plans, transcripts and no-Git review diffs can contain source. Read the data boundary and choose recording and optional encryption settings before using sensitive projects.

What the current options actually do

Option	Execution and coordination	What to check
Claude Code Remote Control	Phone and browser interfaces connect to Claude Code running on your machine. Native remote access may be enough for your workflow.	The machine and local process must remain available; account eligibility and session behavior are documented by Anthropic.
Codex cloud environments	Each task starts in an isolated workspace from a published environment. Preparation can be reused, and tasks can continue while your computer sleeps.	Prepare repository and service access, then inspect the diff and test results. Consult Codex pricing and usage for your plan.
Cursor Cloud Agents	Hosted VMs with prepared development environments; multi-repository work and review artifacts are supported.	Environment, secrets and network access still need setup. Cursor Self-Hosted Machines moves tool execution to your hardware while its agent loop remains in Cursor's cloud.
Denly with a supported runtime CLI	A shared task and approval workflow across projects, with execution on your own machines and runtime choices per project.	Runner availability, local capacity, project checks and review policy. Denly adds a coordination service; AI usage remains with the runtime provider.

Product descriptions were read from the official documentation linked above on 23 October 2026. These products change; check their own pages before deciding.

First-party subscriptions can cover hosted work; for example, Codex access and usage are described in OpenAI's plan documentation. A third-party service may have a different billing model. Compare your actual entitlements and limits rather than treating subscription use as a local-only benefit.

Choose a cloud agent when

A task should run while your development machine is off.
The environment can be prepared remotely with the required repositories, tools and service access.
You want the provider to operate the execution infrastructure, within its plan and capacity limits.

For example, a dependency update with a reproducible build and no hardware requirement is a reasonable hosted-task candidate. A remote environment still needs its test setup; a runnable build is not automatic proof that the proposed update is safe.

Choose local execution when

The task needs a device, licensed toolchain, local service or other state you cannot readily reproduce remotely.
You can keep an appropriate machine online and maintain its environment.
Your repository-handling requirements fit the local tool and provider data flow.

A device-dependent regression may fit a local runner even if an independent documentation update fits a hosted task. This is a choice per workload, not a commitment to put every repository in the same environment.

Try the same bounded task in both environments

Use the cart-total worked example or a similarly small change in a disposable branch of your own project. The downloadable example runs without model access and demonstrates acceptance checks; it is not itself a benchmark of either execution model.

Fix the inputs. Record the starting commit, brief, runtime and model, acceptance checks and required services. Use the same model and settings where possible; record differences when you cannot.
Prepare each environment. Record setup commands, missing dependencies, permissions and secrets. Keep preparation separate from the task run.
Run the task independently. Give each environment the same starting files and brief. Record interruptions, manual interventions and the provider's reported usage.
Review against the same checks. Confirm the original bug fails and the correction passes. Inspect unrelated edits and rerun relevant integration checks.
Choose based on repeat use. Compare the environment you must maintain, review effort, availability and actual billed usage. One small task does not establish a general speed or quality ranking.

Record for each run	Why it matters
Starting commit, task brief, model and settings	Shows whether the comparison used equivalent inputs.
Preparation, queue, execution and review time separately	Exposes where waiting and human effort actually occur.
Acceptance results and unrelated changes	Measures the result you need, rather than only completion speed.
Reported usage, additional charges and missing data	A subscription allowance is not unlimited usage; an absent cost figure is not zero.

Where Denly fits

Use Denly when you want the same task queue, plan approval and PR-review workflow around the runtimes you already use across projects. The two-project walkthrough shows how a failed storefront task and a separate docs review stay organized.

If native remote control already covers your needs, it can be the simpler setup. If work must run without any of your machines, choose hosted execution or provision an appropriate always-on machine. Denly does not supply hosted execution in this workflow.

Questions people ask

Does a local coding agent keep all code on my machine?

Not necessarily. Local execution describes where tools run. A hosted model can still receive code context. Denly also receives selected coordination content, which can include source; its workspace end-to-end encryption is optional. Inspect the data flow, not just the word local.

Can a cloud coding agent use my existing subscription?

A first-party cloud service can include access in its own subscription, subject to that plan’s limits. A third-party service has its own supported billing and authentication model. Do not assume that local means subscription-only or that cloud always requires a separate API bill.

Do I need Denly to control Claude Code from my phone?

No. Claude Code has native Remote Control for local sessions. Denly is an option when you want task queues, project-specific runtime choices and review workflows across projects and machines.

Will a local task continue when my laptop sleeps?

It cannot execute while the machine is asleep. Denly can queue work for later. An interrupted active task may require recovery when the machine returns. For unattended execution, use an awake machine you manage or a hosted service.

Which option is faster or cheaper?

That depends on the task, model, environment setup, usage limits and amount of review. Compare the same starting commit and acceptance checks. Separate preparation time from execution and human review. This page makes no measured speed or cost claim.