Choose on-premises AI coding agents when keeping inference inside your network and controlling the serving stack outweigh the work of operating it. Choose a cloud service when vendor-run infrastructure is more valuable and its data-handling, contractual, and geographic controls meet your requirements. A hybrid setup can apply different routes to different features.
There is no established universal cost or coding-quality winner. Compare the full data path and retention rules, then pilot representative work and account for infrastructure, usage, staffing, and rework—not just hardware or token charges.
What do “on-premises,” “cloud,” and “hybrid” actually mean?
“Self-hosted” can refer to the model, the AI gateway that routes requests, or both. A product may run some components in your infrastructure while sending other features to a vendor-hosted gateway. For that reason, evaluate each feature’s route rather than relying on a product’s overall deployment label.
| Deployment | Where inference runs | Who operates the serving stack | Key qualification |
|---|---|---|---|
| Fully self-hosted | Customer infrastructure, for models configured through the customer’s self-hosted gateway | Customer | GitLab says inference data—including code inputs, prompts, and responses—does not leave the customer network in its documented self-hosted configuration. GitLab-managed model features instead use GitLab’s hosted gateway. GitLab self-hosted models documentation |
| Hybrid | Some features use customer-hosted models; selected others use managed models | Customer for its gateway and models; vendor for managed features | Managed features require internet connectivity and are not isolated in GitLab’s documented arrangement. GitLab self-hosted models documentation |
| Managed cloud | Vendor or model-provider infrastructure, depending on the product and feature | Vendor | GitLab describes its default Duo offering as using a GitLab-hosted cloud AI Gateway connected to external model vendors. GitHub lists models hosted by providers and GitHub infrastructure. Check the applicable feature and plan terms. GitLab configuration documentation · GitHub model hosting documentation |
| Cloud with regional constraints | Cloud infrastructure in a designated region | Vendor | GitHub documents Copilot data residency for eligible GitHub Enterprise Cloud deployments, with the United States and European Union listed on the page reviewed. Requests are routed to endpoints in the designated region, and available models are limited to those certified and available there. Regional processing does not give the customer control of the serving hardware. GitHub data residency documentation |
GitHub’s documented regional availability and feature eligibility can change; confirm that the relevant plan and capability are currently available to your organization before making a residency commitment.
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How should you compare privacy and data control?
Privacy is not a single setting. A coding agent can process code and prompts in one place, retain conversation history elsewhere, and create telemetry or share session records under separate rules. “Not used for training” does not by itself mean data is never transmitted or stored.
- Inference data: Identify where prompts, code context, and model responses are processed for every feature.
- Logs and history: Ask what persists after a session, where it is stored, who can access it, and whether users or administrators can delete it or disable syncing.
- Retention and subprocessors: Check vendor and model-provider retention rules, including any limited retention that may apply to specific models.
- Telemetry: Determine what usage data is collected and whether it is aggregated or de-identified.
- Network and geography: Decide whether the requirement is to keep inference on your network, constrain processing to a region, or both. These controls are not interchangeable.
- Feature-specific routing: Verify that the route applies to the particular coding-agent capability employees will use, not only to the product’s gateway or one model.
Self-hosting controls the inference route, within its configured scope
GitLab says its documented self-hosted configuration keeps inference data—including code inputs, prompts, and responses—inside the customer network and can operate in fully isolated networks. That statement applies to models configured through the self-hosted gateway. If a feature uses GitLab-managed models, it goes to GitLab’s hosted gateway instead, making the setup hybrid rather than fully isolated. GitLab self-hosting documentation
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Cloud session records can outlast the session environment
GitHub says Copilot cloud-agent sessions run in a GitHub-hosted ephemeral environment that is destroyed when the session ends, but the session log remains on GitHub. By default, people with repository access can see that log. The documentation also says relevant prior session data may be sent to the model when a user asks about past interactions. Locally run sessions can be stored on a developer’s machine and synced to a GitHub account, subject to settings and policy. GitHub session data documentation
Training, retention, and telemetry are separate questions
GitLab’s Duo data-usage page says GitLab does not train generative models and that its model subprocessors are restricted from training on inputs and outputs. The same page separately describes chat and workflow history, possible limited vendor-side retention for some models, and aggregated or de-identified usage telemetry. Review the retention and telemetry details as well as the training policy. GitLab Duo data usage
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What does each option demand operationally?
Fully self-hosted: direct control, direct responsibility
In GitLab’s documented comparison, the customer sets up and maintains the infrastructure for a fully self-hosted deployment. Its setup instructions call for LLM serving infrastructure and checking supported models and hardware requirements. The organization must therefore plan for deployment and ongoing operation rather than treating “self-hosted” as a privacy toggle. GitLab self-hosting documentation
- Provision and maintain the serving environment and compatible hardware.
- Operate and troubleshoot the gateway and model-serving software.
- Plan for patching, monitoring, scaling, capacity, and hardware refresh.
- Ensure the team can support the chosen models and the required network configuration.
Managed cloud: less infrastructure work, service-specific dependencies
In GitLab’s comparison, GitLab sets up and maintains the managed cloud configuration. That shifts serving operations away from the customer, but makes the organization dependent on the service’s feature coverage, internet connectivity, hosting arrangements, and applicable data controls.
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Hybrid: split the workload and the operating burden
A hybrid deployment leaves the customer operating its own gateway and models for locally routed features while retaining managed-service dependencies for features sent to vendor-hosted models. It can fit cases where requirements differ by feature, but each route needs its own connectivity and data-handling review.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you compare cost without mistaking a case study for a forecast?
Neither hardware cost nor API usage alone captures the cost of running a coding agent. Compare total cost at expected utilization and include the labor spent reviewing and repairing generated work. A shared GPU pool and a dedicated reservation can produce different economics even for the same organization.
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- Hardware purchase or rental, refresh, power, and cooling.
- Utilization, idle capacity, serving software, and the engineering and security work needed to operate it.
- Model, API, or subscription charges, along with caching and usage-billing terms.
- Latency, availability, accepted work, and the time spent reviewing, correcting, or redoing output.
Commercial arrangements are vendor- and product-specific. GitLab’s current self-hosting documentation describes seat-based pricing for self-hosted Duo, while Agent Platform billing varies by online or offline license: it notes usage billing for online licenses and an Enterprise License Agreement/add-on requirement for offline licenses. These examples are not a market-wide pricing comparison. GitLab self-hosted models documentation
What one 2026 study can—and cannot—tell you
A July 2026 preprint by Sheng-Wei Peng, Yi-Hsun Lin, and Yi-Pei Lee reports a single-developer, non-randomized longitudinal study on a production monorepo. It compares two contiguous 28-day periods: one API-based Claude Code configuration and 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, but 43.8% higher modeled cost for a dedicated on-premises reservation than for the cached API configuration. They also report a 74.9% Fix Commit Ratio for the local configuration versus 45.9% for the API configuration, alongside a higher repair burden for the local configuration, and a 99.3% prompt-cache hit rate with an 88.6% reduction in realized API cost. These are results from that study’s tools, workload, hardware, market assumptions, and labor model—not typical enterprise outcomes or a general coding-quality ranking. Paper and abstract
Use the study as a reminder to test utilization and rework assumptions, not as a deployment forecast. No broad, representative multi-vendor cost statistic or independent controlled benchmark establishing a general coding-quality winner is available here.
Quick Recap
Which deployment fits your organization?
Consider fully self-hosted when
- Inference must stay inside your network or operate in an isolated environment.
- You need direct control over the supported models and the inference data path.
- You have the infrastructure and staff to deploy, maintain, and secure the serving stack.
- A pilot shows acceptable quality and total cost at realistic utilization, including operations and rework.
Consider managed cloud when
- Vendor-run infrastructure is a better operational fit than maintaining your own serving stack.
- The service’s plan-specific data handling, feature routes, retention, and geographic controls satisfy your requirements.
- The features and models your developers need are supported under the relevant plan and region.
Consider hybrid when
- Some features or code paths require local inference while others can use a managed model.
- You can define which features follow each route and communicate their distinct connectivity and data-handling rules.
- Your team can operate the local components while accepting managed-service dependencies for the remaining features.
A practical evaluation checklist
- Map the data path by feature. Record where prompts, code context, responses, logs, and telemetry go, including any model-provider route.
- Set retention and sharing requirements. Specify what may persist, who may see it, whether syncing is allowed, and how deletion or disabling is handled.
- Define the control boundary. State whether you require network isolation, regional processing, customer-selected models, or a combination; confirm that each relevant feature follows the required boundary.
- Confirm scope and availability. Check supported models, product versions, plans, regions, feature eligibility, and internet dependencies against current vendor documentation.
- Estimate full cost at realistic use. Include recurring service charges, utilization, idle capacity, staffing, power, refresh, caching, and review or repair work. Model shared infrastructure separately from dedicated capacity.
- Pilot representative coding tasks. Track accepted work, latency and availability, defects, rework, and total spend against your own review standards before committing to a deployment model.
- Assign operational ownership. Name who patches, monitors, scales, and troubleshoots each gateway, model server, or managed-service integration.
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