You can use Pullfrog as the GitHub event and workflow layer and Ollama Cloud as the model service, but the available documentation does not establish a specific, tested Pullfrog–Ollama Cloud configuration. Treat the pairing as conditional: confirm the current provider setup with Pullfrog before sending repository content. And do not call the entire workflow zero-data-retention (ZDR) compliant based only on Ollama’s commitments. Pullfrog, GitHub Actions, and any other service handling code also belong in the retention and contractual review.
How the proposed code-review workflow fits together
Pullfrog describes an agent that responds to GitHub events, including new pull requests, review comments, and CI failures. You can also tag @pullfrog or configure automations. Pullfrog says agent runs execute in the repository’s GitHub Actions through a pullfrog.yml workflow. That makes Pullfrog the event and workflow layer; the configured model provider is a separate part of the system. See Pullfrog’s product page.
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Pullfrog says it supports any LLM provider, but its public product and onboarding pages do not specify an Ollama Cloud endpoint, provider setting, or tested model configuration. Before relying on this combination, use Pullfrog’s current supported-provider instructions or get confirmation from Pullfrog. Do not infer a working integration from the phrase “any LLM provider.”
What “ZDR-compliant” means for this setup
Ollama’s pricing and privacy documentation says prompt and response data is not logged or used for training. It also says Ollama works with NVIDIA Cloud Providers to host open models and requires hosting partners to have no-logging, no-training, and zero-data-retention policies. Ollama says hosting is primarily in the United States, with possible routing to Europe and Singapore for additional capacity. These are Ollama’s published commitments, not an independent audit or certification of your complete workflow.
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Pullfrog makes separate claims about its handling of repository content. Its terms state: “Pullfrog will not use Content to train, or allow any third party to train, any AI models.” The terms are effective September 10, 2026. Its privacy policy says repository code and content may be sent to third-party agent providers for requested tasks, are not retained beyond the task, and may be held briefly as transient data for safety monitoring. That is a no-training commitment and task-limited retention, not literal zero retention by every part of Pullfrog. Read the Pullfrog terms and privacy policy alongside Ollama’s policy.
The combined system also involves GitHub Actions. The reviewed materials do not establish its retention, contractual, or compliance posture for your particular account and workflow. Check those terms and settings, plus any logging, storage, or other services you add. Whether the setup satisfies a specific legal, regulatory, or contractual ZDR requirement depends on those components and the requirement’s definition—not simply on Ollama’s provider-level statement.
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Build the workflow without assuming an undocumented integration
- Scope the GitHub App. Install Pullfrog and, where appropriate, limit its access to selected repositories. Pullfrog’s onboarding describes repository scoping.
- Choose review triggers. Decide whether the agent should run on pull requests, review comments, CI failures, or only when someone tags
@pullfrog. Use automations only for events that match your review policy. - Set up the repository workflow. Follow Pullfrog’s current setup for the repository’s
pullfrog.ymland GitHub Actions. The available documentation describes that workflow path but does not provide a specific Ollama Cloud configuration. - Verify provider support before adding credentials. Confirm the current Ollama Cloud endpoint, model selection, authentication method, and provider configuration against Pullfrog’s supported instructions or direct vendor guidance. The reviewed pages do not establish the values or steps, so do not copy guessed endpoint or environment-variable settings into a workflow.
- Store credentials narrowly. Pullfrog says credentials can be kept in its encrypted secret store or GitHub Actions secrets, and that only the minimum necessary environment variables are passed to the agent. Its documentation also describes short-lived GitHub installation tokens revoked after a run. Choose the supported option for your setup, restrict who can change secrets and workflows, and avoid placing provider keys in code or pull-request text.
- Review the data path and retention terms. Record which services receive prompts, diffs, comments, and generated results; check each service’s retention and training commitments, region handling, and applicable contract. Revisit this assessment when you change a provider, workflow, or repository scope.
Estimate the cost from both service fees and model usage
The total has two components: Pullfrog’s service plan and Ollama model usage. Ollama bills model usage at token-based rates that vary by model and by input, cached input, and output tokens. The reviewed sources do not provide a measured cost per pull request for this exact workflow, so a credible estimate needs your expected review volume, model, and context size rather than a generic per-review figure.
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|---|---|---|
| Pullfrog personal accounts and public repositories | Free, according to Pullfrog’s public page (2026) | Model usage is separate. Confirm current eligibility and terms at Pullfrog. |
| Pullfrog Organization | $30/month for a non-GitHub-Enterprise-Cloud organization, according to Pullfrog’s 2026 terms | Model usage is separate; confirm checkout pricing and eligibility in the current terms. |
| Pullfrog for GitHub Enterprise Cloud | $80/month per organization, according to Pullfrog’s 2026 terms | Model usage is separate; confirm current terms before budgeting. |
| Ollama Free | $0/month, according to Ollama’s 2026 pricing page | Model usage and included allowances depend on the current plan and pricing page. |
| Ollama Pro | $20/month, including $60 in monthly usage credits, according to Ollama’s 2026 pricing page | Credits are not a fixed cost-per-review; usage depends on model and tokens. |
| Ollama Max | $100/month, including $300 in monthly usage credits, according to Ollama’s 2026 pricing page | Credits are not a fixed cost-per-review; usage depends on model and tokens. |
| Ollama Team | $500/month, including $1,000 in shared monthly usage credits, according to Ollama’s 2026 pricing page | Credits are shared; usage depends on model and tokens. |
Plan prices and token rates can change. Check Ollama’s current pricing for the selected model’s input, cached-input, and output rates, then use a pilot’s actual token usage to estimate spend at your expected review volume. The listed plan figures are not a total-cost-of-ownership estimate; the sources do not benchmark cost per pull request for this configuration.
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This arrangement may suit a team that wants GitHub-triggered agent workflows and is willing to verify a provider setup that is not specifically documented in the reviewed Pullfrog materials. Cost effectiveness depends on the team’s Pullfrog plan, model, token usage, review volume, and any operational requirements; the published prices alone cannot establish that it will cost less than another workflow.
If comparing Ollama Cloud with a locally hosted Ollama deployment or another hosted provider, assess the same factors for each option:
- Where prompts and repository content are processed, and how long each service retains them.
- Training and no-logging commitments, including whether they cover hosting partners.
- Geographic routing and any regional requirements.
- Model-specific token prices and credits, plus Pullfrog plan fees.
- Whether Pullfrog documents the provider configuration and what setup verification is required.
- GitHub App access, secret handling, and the operational work of maintaining local infrastructure if you host a model yourself.
The available sources do not establish comparative review accuracy, defect detection, latency, or performance for this pairing, so those should be measured against your own review criteria rather than assumed.
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