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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →On-premises AI coding agents give an organization more direct control over the model infrastructure and can keep inference data inside its network, but the organization must deploy and operate that stack. Cloud agents shift much of that work to a provider, while their data handling and controls depend on the product, plan, model, and feature. A hybrid setup can route different features in different ways.
There is no established universal winner on cost or coding quality. Compare the full cost at realistic usage—including operating effort and rework—and trace where each feature sends prompts, code, outputs, logs, and telemetry.
What do on-premises, cloud, and hybrid actually mean?
“Self-hosted” can refer to the model, the AI gateway that connects coding tools to models, or both. A product may therefore be partly self-hosted while some features still use a vendor’s hosted infrastructure. Check the route feature by feature, not just the deployment label.
| Deployment | Where inference runs | Who operates the serving stack | What to watch |
|---|---|---|---|
| Fully self-hosted | On customer infrastructure for models configured through the customer-operated gateway | The customer | Confirm every feature uses that gateway; managed-model features change the arrangement. |
| Hybrid | Some features use customer-hosted models; selected features use managed models | Customer for its gateway and models; vendor for its managed service | Managed features require internet connectivity and are not isolated. |
| Managed cloud | Vendor- or provider-hosted infrastructure, depending on product and model | The service provider | Check each feature’s route, applicable plan terms, retention, and telemetry. |
| Cloud with regional processing | Cloud model endpoints in an eligible designated region | The service provider | Regional processing is not customer operation of the hardware; eligibility and model availability can vary. |
GitLab’s self-hosted models documentation describes a customer-operated AI Gateway and supported LLMs. It says inference data—including code inputs, prompts, and responses—does not leave the customer network for models configured through that gateway, and describes operation in fully isolated networks. The same documentation warns that features using GitLab-managed models go through GitLab’s hosted gateway, making the deployment hybrid. GitLab’s Duo configuration documentation describes its default Duo offering as using a GitLab-hosted cloud AI Gateway connected to external model vendors.
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GitHub documents models hosted by providers and on GitHub infrastructure in its model-hosting reference. For either vendor—or another service—do not infer a feature’s data path from a general privacy statement alone.
Which privacy and control questions matter?
Privacy is more than whether prompts are used to train a model. Assess inference data, telemetry, retention, session history, sharing, and geographic processing separately. A local inference path can reduce exposure to external model services, but the organization still needs to govern its own logs, access, and infrastructure.
Trace the data path for every feature
- Identify what leaves the developer’s device: prompts, code context, outputs, and any session history or logs.
- Record whether the request goes to a customer-operated model, the product vendor, or an external model provider.
- Check whether telemetry or service diagnostics follow a different route from inference.
- Repeat the check for chat, code completion, agent workflows, and other features; a shared product name does not guarantee a shared route.
GitLab’s Duo data-usage documentation says it does not train generative models and that its model sub-processors are restricted from training on inputs and outputs. It also documents chat and workflow history, possible limited vendor-side retention for some models, and aggregated or de-identified usage telemetry. “Not used for training” therefore does not mean that no data is stored or transmitted.
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Separate inference location from session records
GitHub’s session-data documentation distinguishes where a cloud-agent session runs from what happens to its record. Cloud-agent sessions run in ephemeral environments hosted by GitHub, and the environment is destroyed when the session ends; the session log remains on GitHub and is visible by default to people with repository access. The documentation also says relevant prior session data may be sent to a model when a user asks about past interactions. For locally run sessions, it says records can be stored on a developer’s machine and synced to a GitHub account, subject to settings and policy.
Ask whether regional processing is sufficient
GitHub’s Copilot data-residency documentation describes inference processing in a designated region for eligible GitHub Enterprise Cloud deployments. The page reviewed lists the United States and European Union, says requests are routed to model endpoints within the enterprise’s designated region, and limits available models to those certified and available there. That is a geographic-processing control, not customer control of the serving hardware. Verify current feature eligibility and availability before relying on it.
How should you compare the cost?
Compare total cost for your expected workload, not just token charges or the price of a GPU. A cloud subscription or usage bill and a self-hosted server are different cost structures; each can look cheaper or more expensive depending on utilization, serving configuration, and the value of the work produced.
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- Infrastructure purchase or rental, refresh, power, and cooling.
- Utilization and idle capacity, evaluated separately for a shared GPU pool and a dedicated reservation.
- Serving software, engineering time, security operations, monitoring, and troubleshooting.
- Subscriptions or usage charges, including the effect of caching.
- Latency, coding-task quality, review effort, defects, and the cost of repairing generated work.
Commercial terms also vary by product. GitLab’s self-hosting documentation describes seat-based pricing for self-hosted Duo, while Agent Platform billing varies by online or offline license: online licenses use usage billing, and offline licenses require an Enterprise License Agreement and add-on. These are GitLab-specific arrangements, not a market-wide price comparison.
Use case studies as scenarios, not forecasts
A July 2026 preprint, Inference Economics of Enterprise Coding Agents: A Case Study of Cloud vs. On-Premise LLMs by Sheng-Wei Peng, Yi-Hsun Lin, and Yi-Pei Lee, reports a non-randomized longitudinal study by one developer over two contiguous 28-day periods on a production monorepo. It compared one API-based Claude Code configuration with one quantized on-premises configuration on NVIDIA Blackwell hardware. The authors report:
- 40.1% modeled total-cost savings for on-premises deployment under shared GPU allocation.
- 43.8% higher modeled cost for a dedicated on-premises reservation than for the cached API configuration.
- A 74.9% Fix Commit Ratio for the local configuration versus 45.9% for the API configuration.
- A 99.3% prompt-cache hit rate and an 88.6% reduction in realized API cost.
Those results describe the study’s specific tools, hardware, workload, market assumptions, and labor model; they do not establish typical enterprise savings or a general quality winner. The authors also report a higher repair burden for the local configuration and emphasize that the economics depend on utilization. Use the paper to identify assumptions for your own sensitivity analysis, not to forecast your bill.
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Run a representative pilot
Test both deployment approaches, where feasible, on coding tasks that reflect your repositories and review standards. Record accepted work, defects and repair effort, latency, feature availability, and total spend. Compare shared and dedicated capacity separately; a server that is inexpensive at high utilization may be a poor fit if it sits idle for much of the day.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Who takes on deployment and maintenance?
With fully self-hosted models, the customer sets up and maintains the infrastructure. GitLab’s setup documentation calls for LLM serving infrastructure and checking supported models and hardware requirements. That means the operating commitment includes more than acquiring a server: staff must be able to deploy, monitor, secure, troubleshoot, and refresh the serving stack.
In GitLab’s comparison, GitLab handles setup and maintenance for its managed cloud configuration. Hybrid leaves the customer operating its own gateway and models while keeping dependencies on the managed service for selected features. In practice, the cloud option reduces infrastructure work but does not remove the need to assess service availability, policy, and feature-specific data handling.
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- Self-hosting is a stronger fit when network isolation, direct control of supported models, or a customer-controlled inference path is a requirement—and the organization can staff ongoing operations.
- Managed cloud is a stronger fit when vendor-run infrastructure is preferable and the organization’s contractual and technical controls meet its requirements.
- Hybrid is a stronger fit when requirements differ by feature and the organization can manage both local infrastructure and selected managed-service dependencies.
How can you make the decision?
Use the same questions for each product and feature under consideration. Record the evidence and owner for each answer before choosing a deployment.
- Map the data path: identify where prompts, code context, responses, logs, and telemetry go.
- Set retention and sharing requirements: determine what persists, who can access it, and whether users or administrators can disable syncing or delete records.
- Define the required control boundary: decide whether inference must stay on your network, within a region, or on selected customer-operated models—and confirm every required feature follows that route.
- Check coverage and connectivity: list needed capabilities, supported models, regional or version limits, and internet dependencies.
- Estimate full cost at likely utilization: include staffing, infrastructure, usage, caching, and review or repair work.
- Test actual coding workflows: compare accepted work, defects, latency, and operational burden against your team’s standards.
A deployment comparison is only useful when the same feature set, workload, and cost assumptions are used on both sides. Keep exceptions visible: a regional cloud route is not the same as customer-hosted inference, and a self-hosted gateway does not prove every feature is local.
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