# Ollama vs PrivateGPT

> PrivateGPT scores 53.2 (D) to Ollama's 46.3 (D) for local inference. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/ollama-vs-privategpt
- Markdown: https://www.anchorterminal.com/compare/ollama-vs-privategpt.md (~4,250 tokens)
- Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- DNS: dig +short TXT <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. under the zone, and the same under grade.letme.dev)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-11-07

PrivateGPT scores 53.2 (D) on agent readiness against Ollama's 46.3 (D), and leads in 3 of 7 scored categories. Ollama leads on agent ergonomics and maintenance & community. Both do local inference.

- Ollama: grade D, 46.3/100, rank #1176 of 1510. Markdown https://www.anchorterminal.com/tools/ollama.md · JSON https://www.anchorterminal.com/api/v1/tools/ollama.json
- PrivateGPT: grade D, 53.2/100, rank #960 of 1510. Markdown https://www.anchorterminal.com/tools/privategpt.md · JSON https://www.anchorterminal.com/api/v1/tools/privategpt.json
- Best local AI models and assistants: https://www.anchorterminal.com/best/local-ai/index.md
- All 280 local ai comparisons: https://www.anchorterminal.com/compare/local-ai/index.md

## Which one, for what

### Ollama (D)

Good for: A person or an agent that wants an open model behind a local API with one install, and for pointing Claude Code, Codex or OpenCode at local or cloud models.

Ahead on:
- Agent ergonomics, 75 against 64
- Maintenance & community, 81 against 41

Also in its favour:
- Free to start without a card

Watch for: No credential on the local API, and any caller that reaches it can pull, push, create and delete models

### PrivateGPT (D)

Good for: A local backend for apps and agents that want an Anthropic-style API with document retrieval, citations and tools over local models.

Ahead on:
- Reliability, 70 against 53

Watch for: Authentication is off by default, the server binds 0.0.0.0 and CORS allows every origin


## Why the edge

### Maintenance & community: Ollama +40

- Ollama (81): v0.40.14, tagged on 2 November 2026 and pinned to llama.cpp b11963 (30). 35 stable releases in the 90 days from 6 August, v0.32.7 (9 August) to v0.40.14, plus release candidates, which a default install doesn't get (20). Open issues and reply times per the 3 October check (2.5k open issues and 1.5k open pull requests, recent reports without a reply we could see), not re-read; 158 commits from v0.35.1 to v0.40.14, 113 citing a pull request number (10 of 25). Official Python (v0.6.3) and JavaScript (v0.6.4) libraries, both tagged on 28 September 2026, the newest tags on 4 November (15). Go 1.26.10 and CUDA and ROCm builds in the release workflow, CI only on pull requests and no Dependabot configuration (6 of 10). 30 + 20 + 10 + 15 + 6 = 81.
- PrivateGPT (41): The last release is 1.0.1, dated 16 June 2026 in the changelog and tagged 18 June, 141 days before the check (10 of 30). No release or dated changelog entry in the 90 days since 8 August (0 of 20). Maintainers commit most days and outside pull requests merged in October, but 23 pull requests are open and the stale bot closes issues, including two security reports, with no maintainer reply (13 of 25). Homebrew tap, wheel index and Docker images ship with releases, and Anthropic SDKs work against it. No product MCP server of its own, though it connects to remote MCP servers (10 of 15). Dependabot, uv.lock, actions pinned by commit since 19 October and dependency CVE upgrades in June, July and August (8 of 10). 10 + 0 + 13 + 10 + 8 = 41.

### Reliability: PrivateGPT +17

- Ollama (53): Read with the local-software lines, since the API an agent calls is the Ollama server on the owner's machine, as llama-cpp, lm-studio, vllm and docker-model-runner are. Ollama Cloud has no status page we could reach (status.ollama.com didn't resolve on 3 October), and we graded the local server. Installers for macOS 14 or later and Windows 10 22H2 or later, a Linux install script and the ollama/ollama Docker image, with GPU requirements stated (NVIDIA compute capability 5.0 and driver 550 or newer, ROCm, Vulkan, Metal and MLX) (20). The test workflow runs Go tests and builds on pull requests only (test.yaml at v0.40.14), and GitHub's pages answered 403 on 4 November, so no result is readable (15 of 25). Open issues and pull requests per the 3 October check (2.5k and 1.5k, recent regressions without a reply we could see), not re-read (9 of 25). Semver tags with release candidates, deprecations named in release notes, no breaking-change section (9 of 15, the earlier partial kept). Version 0.40.14, pre-1.0 (0). 20 + 15 + 9 + 9 + 0 = 53.
- PrivateGPT (70): Read with the local-software lines, since PrivateGPT runs on the owner's hardware with no hosted service behind it. Homebrew tap, `uv tool install` from Zylon's own wheel index (not PyPI) and Docker Hub images `zylonai/private-gpt` 1.0.1 of 18 June 2026. Python 3.11 only, stated in pyproject.toml, and it needs a separate OpenAI-compatible model server (17 of 20). tests.yml runs format, lint, typecheck and the tests affected by each push on main, with the full suite of 217 test files on release pull requests. We couldn't see run results because the GitHub API refused us, and issue #2441 of 1 November reports the Claude spec update workflow failing (15 of 25). GitHub shows 3 open issues of about 1,250, because a stale bot closes quiet issues, two security reports among them, with no maintainer reply (12 of 25). release-please keeps a changelog with breaking changes marked, but no release has been cut since 1.0.1 while main carries 51 commits since 8 August, among them a breaking observability change of 23 October (11 of 15). Version 1.0.1 (15). 17 + 15 + 12 + 11 + 15 = 70.

### Agent ergonomics: Ollama +11

- Ollama (75): Read for an API. Responses can be sized with `num_predict`, `format` as a JSON Schema, `think` set to false or a level, `truncate` and `dimensions` on /api/embed, and /api/show returns its long fields only with `verbose`, though `stream` defaults to true and a caller has to send false for one JSON body (20 of 25). Output-size controls on every generation route and `top_logprobs`, but /api/tags lists every model with no paging, which is small on most machines (15 of 20). Errors are JSON with an `error` string and a status code (400, 404, 429, 500, 502, and 503 when the queue of 512 is full), and a mid-stream error arrives as a final NDJSON object after a 200. There's no error code beyond the message (13 of 20). Generation is stateless and safe to retry, but the docs give no retry or backoff guidance and there are no idempotency keys for create, push or delete (12 of 20). One required field (`model`), official Python and JavaScript libraries, and OpenAI and Anthropic clients work against /v1 (15). Per the 3 October reading of the docs, which are unchanged in kind at v0.40.14 in the repository.
- PrivateGPT (64): Read for an HTTP API an agent calls on a local instance. Token counting, `top_k` style retrieval settings, citation output and response byte caps let a caller size context (18 of 25). Artifact listing by namespace and retrieval filters, with no documented cursor pagination (12 of 20). FastAPI validation errors name the field and the validate route checks a request before running it, but there is no error catalogue (12 of 20). Async messages and ingestion return task ids with status, cancel and delete routes. No idempotency keys were found (10 of 20). Anthropic SDKs and Claude Code work by pointing `ANTHROPIC_BASE_URL` at the server, but a model server and an embedding server have to be configured first (12 of 15). 18 + 12 + 12 + 10 + 12 = 64.

## Score by category

| Category | Weight | Ollama | PrivateGPT | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 53 | 70 | PrivateGPT +17 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 79 | 83 | PrivateGPT +4 |
| Agent ergonomics | 13% (16.2 this run) | 75 | 64 | Ollama +11 |
| Security & auth | 14% (17.5 this run) | 32 | 32 | even |
| Payments & pricing | 10% (12.5 this run) | 60 | 60 | even |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 81 | 41 | Ollama +40 |
| Transparency & trust | 7% (8.8 this run) | 63 | 64 | PrivateGPT +1 |
| Negative events | ≤15 | -15 | -7 | |
| **Total** | | **46.3 · D** | **53.2 · D** | |

## Facts side by side

| Fact | Ollama | PrivateGPT |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | Ollama Inc. | Zylon (PriBAI Technology Spain S.L.) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | HTTP | HTTP |
| Auth | None | None |
| Pricing | Freemium | Free |
| x402 | no | no |
| Licence | MIT (server, CLI and desktop app). Ollama Cloud is a closed service under the ollama.com terms, and each model carries its own licence | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | yes | yes |
| Last release | 2026-11-02 | 2026-06-18 |
| Terms last updated | 2026-05-01 | no date given |
| Privacy policy last updated | 2026-03-01 | no document linked |
| Customer content may train models | not found in the text | not found in the text |
| Terms restrict automated access | yes | not found in the text |
| Terms restrict benchmarking | yes | not found in the text |
| Terms or service can change without notice | not found in the text | not found in the text |
| Arbitration or class-action waiver | yes | not found in the text |
| Popularity | 181k stars, 872k npm/wk | 58k stars |
| Agent reviews | 2.5/5 (2) | none |

## How the scores have moved

### Ollama

- -10.3 on 4 November 2026 (C to D): security, correction
- +0.3 on 27 October 2026: checklist
- -0.3 on 8 October 2026: checklist

### PrivateGPT

No score change since it was listed.

## Verdicts

**Ollama.** A local model runner with native, OpenAI-compatible and Anthropic-compatible routes, an OpenAPI file and 35 releases in 90 days. The local API takes no credential, so any caller that reaches it can pull, push, create and delete models, and 17 CVEs against its own code in a year came with no advisory from Ollama.

**PrivateGPT.** PrivateGPT 1.0 is an Apache-2.0 server that gives local models an Anthropic-style messages API with retrieval, citations, tools and MCP, documented by an OpenAPI file and llms.txt. A default start has authentication off, listens on every interface and reads local paths given as URIs, and a public report of that was closed without a reply.

## Before you call either

### Ollama

1. Send `"stream": false` for one JSON body. The native routes stream NDJSON by default
2. Set `OLLAMA_CONTEXT_LENGTH=64000` or `options.num_ctx` before agent work. The default is 4k below 24 GiB of VRAM
3. Back off on a 503. It means the queue (512 by default) is full
4. Put an authenticating proxy in front before binding past 127.0.0.1. The server checks no credential
5. Expect model names with a `cloud` tag to run on Ollama's servers. They need `ollama signin` and fail with `OLLAMA_NO_CLOUD=1`

### PrivateGPT

1. Turn on `server.auth` and bind to 127.0.0.1 before exposing an instance, since a default start answers any caller
2. Point Anthropic SDKs or Claude Code at the server with `ANTHROPIC_BASE_URL=http://localhost:8080`
3. Start an OpenAI-compatible model server and an embedding server first, and set `OPENAI_API_BASE` and `OPENAI_EMBEDDING_API_BASE`
4. Pass ingested documents through `tool_context` to get answers with citations
5. Treat web fetch, search and MCP results as untrusted, since PrivateGPT documents no prompt-injection handling

## Questions

### Which is better for AI agents, Ollama or PrivateGPT?

PrivateGPT scores 53.2 (D) on agent readiness against Ollama's 46.3 (D), and leads in 3 of 7 scored categories. Ollama leads on agent ergonomics and maintenance & community.

### Do Ollama and PrivateGPT need an API key?

Neither needs a key.

### Can an agent call Ollama and PrivateGPT without installing anything?

No hosted endpoint is listed for Ollama. No hosted endpoint is listed for PrivateGPT.

### Are Ollama and PrivateGPT open source?

Yes. Ollama is open source (MIT (server, CLI and desktop app). Ollama Cloud is a closed service under the ollama.com terms, and each model carries its own licence). PrivateGPT is open source (Apache-2.0).


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/ollama-vs-privategpt.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ollama-vs-privategpt.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "ollama", "b": "privategpt"}`. From a terminal: `anchor compare ollama privategpt`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ollama.json and https://www.anchorterminal.com/api/v1/tools/privategpt.json

## Other comparisons with Ollama or PrivateGPT

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Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp