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You can assemble this workflow from published parts, but three points need settling first. Pullfrog says it works with any LLM provider, yet its published pages do not describe an Ollama Cloud setup, so that pairing has to be verified rather than assumed. Ollama’s no-retention commitments cover its cloud model service and hosting partners. Pullfrog’s own policy allows brief transient retention for safety monitoring, and GitHub Actions falls outside both vendors’ statements. Costs come from two separate bills: a Pullfrog plan fee and Ollama token usage.
How the pieces fit together
A Pullfrog-based review workflow has three layers, and each one handles data differently.
- Pullfrog is the event and agent layer. Its product page describes an agent that listens for GitHub events, including new pull requests, review comments, and CI failures. It can run configured automations or respond when someone tags
@pullfrog. - GitHub Actions is where agent runs execute. Pullfrog describes runs happening in your repository’s Actions environment through a
pullfrog.ymlworkflow. - The model provider receives the code and context and returns the review. In this design that provider would be Ollama Cloud.
Does Pullfrog work with Ollama Cloud?
Pullfrog says it supports any LLM provider. Its product page names Anthropic, OpenAI, Google, xAI, Mistral, DeepSeek, and OpenRouter as examples. It does not show an Ollama Cloud provider setting, endpoint, or tested model configuration. The pairing is plausible, but it is not a documented integration, and it should not be described as one until it has been confirmed.
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Verify the pairing before building on it
- Check Pullfrog’s current provider instructions on its onboarding page. If Ollama Cloud is not listed there, treat the setup as unconfirmed and get Pullfrog’s confirmation before using it on production repositories.
- Take the endpoint and model name from Ollama’s own documentation rather than a third-party tutorial. Model names and rates change, so record the date you checked them.
- Install the Pullfrog GitHub App and choose Only select repositories, so the first test covers a single repository.
- In that repository, go to Settings > Secrets and variables > Actions > New repository secret and store the provider credential there. Use a key dedicated to this workflow so you can rotate it without affecting other tools.
- Open the
pullfrog.ymlworkflow and confirm which provider and model it references and which environment variables it passes to the agent. - Open a test pull request and check the Actions run log. The run should complete, and the provider call should go where your configuration says. If the run never starts, confirm that the repository is in the App’s selected repositories and that your trigger (a tag or a configured automation) matches what the workflow listens for.
What Ollama’s ZDR claim covers
Ollama’s pricing FAQ states: “Prompt or response data is never logged or trained on.” The same page says Ollama collaborates with NVIDIA Cloud Providers to host open models and requires those partner providers to have no-logging, no-training, and zero-data-retention policies. Hosting is primarily in the United States, with possible routing to Europe and Singapore for additional capacity. These are Ollama’s published commitments, and the sources reviewed do not include an independent audit of them. The full text is at https://www.ollama.com/pricing.
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Pullfrog’s position is different. Its Terms of Service state: “Pullfrog will not use Content to train, or allow any third party to train, any AI models.” The terms, effective September 10, 2026, are the source for that sentence. Its privacy policy says repository code may be sent to third-party agent providers to carry out the task you requested, that the code is not retained beyond that task, and that transient data may be held briefly for safety monitoring. No training and task-limited retention are meaningful protections, but Pullfrog is not a zero-retention service in the strictest literal sense.
Where each stage stands
| Stage | Published position | What still needs checking |
|---|---|---|
| Ollama Cloud model service | Prompts and responses are not logged or trained on; hosting partners must have no-logging, no-training, and zero-data-retention policies | Confirm the policy applies to the plan and account you buy. The sources reviewed do not independently audit it. |
| Ollama hosting locations | Primarily United States, with possible routing to Europe and Singapore for additional capacity | Whether that routing fits your data-location rules |
| Pullfrog | Does not train on content; sends content to third-party agent providers for the requested task; code is not retained beyond the task; transient data may be held briefly for safety monitoring | Length of the safety-monitoring hold: not stated in the pages reviewed |
| GitHub Actions | Not addressed by the Pullfrog or Ollama pages reviewed | GitHub’s terms and your plan’s log and artifact retention settings |
| Your workflow output | Not addressed by the vendor pages | What the workflow prints to logs or uploads as artifacts |
What the workflow costs
The bill has two parts paid to different vendors. Pullfrog charges a plan fee, and model usage is billed separately. Ollama charges for model usage through per-token rates and plan credits. The published figures are plan prices, not a total cost of ownership.
Pullfrog plan fees
| Account type | Published price | Source |
|---|---|---|
| Personal accounts and public repositories | Free | Pullfrog product page |
| Organization (not GitHub Enterprise Cloud) | $30/month | Pullfrog product page and Pullfrog terms |
| GitHub Enterprise Cloud organization | $80/month per organization | Pullfrog terms |
Prices and eligibility can change, so confirm the figure at checkout and in the current terms before you budget.
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Ollama plans
| Ollama plan | Monthly price | Monthly usage credits |
|---|---|---|
| Free | $0 | Not stated |
| Pro | $20 | $60 |
| Max | $100 | $300 |
| Team | $500 | $1,000 shared across the team |
These figures are from Ollama’s pricing page as checked on October 7, 2026. The sources reviewed do not say whether plan credits apply to automated calls made from a CI run, so confirm that before assuming a subscription covers the workflow.
Estimating model usage
Model usage is priced per token. Input, cached input, and output tokens carry separate rates, and the rates differ by model. For one model, the monthly cost is the sum over all reviews of:
(input tokens × input rate) + (cached input tokens × cached input rate) + (output tokens × output rate)
Take the rates from Ollama’s pricing page on the day you calculate, and record that date. Token counts must come from your own pull requests, because prompt size depends on the diff and on the surrounding context the agent loads. Figures from another repository will not transfer.
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A realistic monthly total has four parts:
- the Pullfrog plan fee for your account or organization;
- any Ollama subscription the workflow requires;
- Ollama token usage at the chosen model’s rates;
- GitHub Actions run time, which the sources reviewed do not price. Check your GitHub plan for that.
Scoping permissions and credentials
Pullfrog describes the following controls. They are vendor descriptions, not an independent security assessment.
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- The GitHub App can be limited to selected repositories, and its repository permissions can optionally be restricted during setup.
- Provider keys can be kept in Pullfrog’s encrypted secret store or in GitHub Actions secrets.
- Only the minimum necessary environment variables are passed to the agent.
- GitHub operations use short-lived installation tokens that are revoked after each run.
Start with the narrowest scope. Install on one repository, and widen the selection only after a test run shows the behaviour you expect. Keep the Ollama key in a repository secret, not a repository variable or the workflow file, because variables are readable without masking.
When to compare alternatives
The sources do not benchmark Ollama Cloud against other options for this workflow. If you are weighing it against a self-hosted Ollama deployment or another hosted provider, compare these points:
Quick Recap
- Where prompts and code are processed, and whether that fits your data-location rules. Ollama Cloud is primarily hosted in the United States, with possible routing to Europe and Singapore.
- The retention and training commitments at each stage, as set out in the table above.
- Per-token and per-model costs, plus the Pullfrog plan fee.
- Setup burden. Ollama Cloud needs the verification steps above before it is in use.
- Permission and secret handling, which follows the controls described above whichever provider you use.
- Operational work. A self-hosted deployment means maintaining your own infrastructure; a hosted service shifts that work to the vendor.
What is not yet established
- No independent benchmark of review accuracy, defect detection, or latency for this configuration is publicly available.
- No independently verified compliance certification covers the combined Pullfrog, GitHub Actions, and Ollama Cloud workflow. “ZDR-compliant” here describes Ollama’s published policy for its cloud service, not a certified system.
- This article does not report an implementation test of the combined setup. The steps above are a verification plan for your own repositories.
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