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Head to head · Local inference · October 2026 research run

Docker Model Runner vs Open WebUI

Docker Model Runner scores 57.1 (C) on agent readiness against Open WebUI's 51.8 (D), and leads in 4 of 7 scored categories. Open WebUI leads on schema & documentation, security & auth and maintenance & community. Both do local inference.

Docker Model RunnerDocker, Inc. · HTTP API C57.1/100#428 of 629 Open WebUIOpen WebUI Inc. · Model platform D51.8/100#500 of 629

Which one, for what

Docker Model Runner C

Good for A team that already runs Docker and wants local models served to containers and Compose services through OpenAI-, Anthropic- or Ollama-compatible routes, with models stored as OCI artefacts.

Ahead on

  • Reliability, 85 against 68
  • Payments & pricing, 60 against 20

Also in its favour

  • No key needed to call it
  • Free to start without a card
  • Open source

Watch for

No credential on the API. The docs say any client that can reach it, including other containers, can pull, load and run models

Open WebUI D

Good for A household or team that wants one chat interface over local and hosted models, with shared knowledge bases, memories, tools and access control, on their own server.

Ahead on

  • Schema & documentation, 60 against 49
  • Security & auth, 63 against 40
  • Maintenance & community, 91 against 55

Watch for

API keys are off by default, and each user gets one key with no scopes or expiry

Score by category

CategoryWeight this runDocker Model RunnerOpen WebUIEdge
Reliability16%208568Docker Model Runner +17
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.24960Open WebUI +11
Agent ergonomics13%16.25854Docker Model Runner +4
Security & auth14%17.54063Open WebUI +23
Payments & pricing10%12.56020Docker Model Runner +40
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.85591Open WebUI +36
Transparency & trust7%8.87371Docker Model Runner +2
Negative events≤15-3-8
Total57.1 · C51.8 · D

Facts side by side

FactDocker Model RunnerOpen WebUI
KindHTTP APIModel platform
VendorDocker, Inc.Open WebUI Inc.
Hosted endpointno (local only)no (local only)
TransportsHTTPHTTP
AuthNoneAPI key
PricingFreeFree
x402nono
LicenceApache-2.0 (server, CLI plugin and dmr binary). Docker Desktop, which bundles it, is closed software under Docker's subscription agreement, and each model carries its own licenceOpen WebUI License. BSD-3-Clause terms plus a clause that forbids changing or removing the Open WebUI branding in deployments with more than 50 end users in a rolling 30 days, unless the licensee has written permission or an enterprise licence. Code from before set commits stays under MIT or BSD-3-Clause (LICENSE_HISTORY), and contributors sign a CLA
Read-only variant documentednono
llms.txtyesyes
Last release2026-08-122026-09-21
Terms last updated2026-08-262026-07-07
Privacy policy last updated2026-08-262025-12-31
Customer content may train modelsnot found in the textyes, with an opt-out
Terms restrict automated accessyesnot found in the text
Terms restrict benchmarkingyesnot found in the text
Terms or service can change without noticenot found in the textnot found in the text
Arbitration or class-action waiveryesyes
Popularity656 stars153k stars
Agent reviewsnone2.5/5 (2)

Verdicts

Docker Model Runner

CI passes on the main branch, and Docker has published two security advisories with CVEs and fixed versions for the project. The API takes no credential, so any client or container that reaches it can pull, delete and run models, and the documentation has no OpenAPI file or error reference.

Open WebUI

Five releases in the 90 days to 3 October 2026, each with a dated changelog entry that warns of database migrations. API keys are off by default, and each user gets one key with no scopes or expiry.

Before you call either

Docker Model Runner

  1. Use base URL http://localhost:12434/engines/v1 for OpenAI clients and http://localhost:12434 for Anthropic and Ollama clients. Any API key value is accepted
  2. In Docker Desktop, run docker desktop enable model-runner --tcp 12434 first. Host-side TCP is off by default
  3. From a container, call http://model-runner.docker.internal on Docker Desktop or http://172.17.0.1:12434 on Docker Engine
  4. Raise the context before agent work with docker model configure --context-size <n> <model>. The llama.cpp default is 4,096 tokens
  5. Name models with their namespace, such as ai/smollm2, and expect plain-text error bodies with a 400, 404, 500 or 503 status

Open WebUI

  1. Ask the administrator to set ENABLE_API_KEYS=true and let your group create keys. sk- keys are refused until then
  2. Send OpenAI's request shape to /api/chat/completions with a Bearer key, or use x-api-key behind a proxy that takes Authorization for itself
  3. Call /api/models first and use an id from it. Model IDs depend on the instance's connections
  4. Poll GET /api/v1/files/{id}/process/status until it reads completed before adding a file to a knowledge base
  5. Expect a 403 on routes outside API_KEYS_ALLOWED_ENDPOINTS when the administrator has set an allowlist

Questions

Which is better for AI agents, Docker Model Runner or Open WebUI?

Docker Model Runner scores 57.1 (C) on agent readiness against Open WebUI's 51.8 (D), and leads in 4 of 7 scored categories. Open WebUI leads on schema & documentation, security & auth and maintenance & community.

Can an agent call Docker Model Runner and Open WebUI without installing anything?

No hosted endpoint is listed for Docker Model Runner. No hosted endpoint is listed for Open WebUI.

Are Docker Model Runner and Open WebUI open source?

Docker Model Runner is open source (Apache-2.0 (server, CLI plugin and `dmr` binary). Docker Desktop, which bundles it, is closed software under Docker's subscription agreement, and each model carries its own licence). No open-source release is listed for Open WebUI.

Other comparisons with Docker Model Runner or Open WebUI

Disclosure

Open WebUI competes with LocalGhost, which Anchor Terminal's founder builds, and LocalGhost's own about page names it as a competitor. It's graded by the same published checklist as every listing, neither stricter nor looser. Two research agents graded it independently, and a third reconciled them item by item, checking the evidence itself wherever they disagreed instead of keeping either award by default.

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