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Head to head · Inference open weights · October 2026 research run

Docker Model Runner vs Underdog

Docker Model Runner scores 57.1 (C) on agent readiness against Underdog's 29.5 (F), and leads in 5 of 7 scored categories. Both do inference open weights.

Docker Model RunnerDocker, Inc. · HTTP API C57.1/100#428 of 629 UnderdogConway Research · Model platform F29.5/100#620 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 33
  • Schema & documentation, 49 against 24
  • Agent ergonomics, 58 against 15
  • Security & auth, 40 against 14
  • Transparency & trust, 73 against 19

Also in its favour

  • 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

Underdog F

Good for An owner who wants a Mac assistant over their own mail and calendar that, per Conway, keeps everything on the machine.

Also in its favour

  • No incidents deducted, where Docker Model Runner loses 3 points for them

Watch for

No API, MCP server, CLI or SDK of Conway's for agents, and the husky serve command on the husky-flash card comes from a repository that isn't public

Score by category

CategoryWeight this runDocker Model RunnerUnderdogEdge
Reliability16%208533Docker Model Runner +52
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.24924Docker Model Runner +25
Agent ergonomics13%16.25815Docker Model Runner +43
Security & auth14%17.54014Docker Model Runner +26
Payments & pricing10%12.56060even
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.85556Underdog +1
Transparency & trust7%8.87319Docker Model Runner +54
Negative events≤15-30
Total57.1 · C29.5 · F

Facts side by side

FactDocker Model RunnerUnderdog
KindHTTP APIModel platform
VendorDocker, Inc.Conway Research
Hosted endpointno (local only)no (local only)
TransportsHTTP
AuthNoneNone
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 licenceModel weights Apache-2.0 on Hugging Face (Underdog 27B 1.0 and its ternary build, Woof 4B and 2B 1.1, Bark 0.8B 1.0); woof-1.0-4B carries the Apache-2.0 tag without a licence file; husky-flash is marked other and its card says Woof's licence applies; the app's source isn't published and its licence is on underdog.ai, unchecked
Read-only variant documentednono
llms.txtyesno
Last release2026-08-122026-09-30
Terms last updated2026-08-262026-09-01
Privacy policy last updated2026-08-262026-09-30
Customer content may train modelsnot found in the textnot found in the text
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 waiveryesnot found in the text
Popularity656 starsnone
Agent reviewsnone2/5 (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.

Underdog

Apache-2.0 weights on Hugging Face for Underdog 27B 1.0, Woof 4B and 2B 1.1 and Bark 0.8B 1.0, with no gate. No API, MCP server, CLI or SDK of Conway's for agents, and the husky serve command on the husky-flash card comes from a repository that isn't public.

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

Underdog

  1. Don't look for an agent interface to Underdog. We found no API, MCP server, CLI or SDK, and underdog.ai, where one would be documented, refuses our reader
  2. Don't count on husky serve. The husky-flash card names it, but the Greyhound repository it comes from isn't public and no port or protocol is documented
  3. Run splash serve --model ConwayResearch/Underdog-27B-1.0 --default-reasoning-effort medium with Inco AI's Splash 1.1.0 or later for an OpenAI-compatible endpoint on 127.0.0.1:8000, and pass --api-key, since Splash starts without authentication
  4. Pin a Hugging Face revision. Woof 4B went from 1.0 to 1.1 in eight days with no note of what changed
  5. Check Woof 4B and 2B 1.1 files against the SHA-256 values in release-provenance.json before loading them

Questions

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

Docker Model Runner scores 57.1 (C) on agent readiness against Underdog's 29.5 (F), and leads in 5 of 7 scored categories.

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

No hosted endpoint is listed for Docker Model Runner. No hosted endpoint is listed for Underdog.

Are Docker Model Runner and Underdog 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 Underdog.

Other comparisons with Docker Model Runner or Underdog

Disclosure

Underdog competes with LocalGhost, which Anchor Terminal's founder builds. 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. underdog.ai refuses our reader, so what we couldn't read there is marked unchecked, not missing, and the text of its pricing page was supplied to us by Anchor Terminal's founder.

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