Container Diet is live · free · last verified 24 Oct 2026 · viability monitor
Container Diet
Container Diet scans Docker images and Dockerfiles for size bloat and hardening gaps, then rewrites the Dockerfile with AI.
If you suspect an image is fat and want a straight answer plus a rewritten Dockerfile, Container Diet does that for nothing and no login. The MCP server is the part we'd keep — Dockerfile advice inside Cursor or Claude Desktop beats another scanner tab. Its depth is in hardening checks, not CVE breadth, so pair it with Trivy rather than swap it out.
Verdict generatedVerified 6d ago · liveness 67/100 · cite: rightaichoice.com/tools/container-diet
- DevOps engineers trimming image size and closing Dockerfile hardening gaps in a single pass
- Developers cutting registry storage cost and container startup time
- CI/CD pipelines that need automated Dockerfile scanning with JSON output
- Air-gapped or regulated teams running Ollama locally so image data never leaves the network
- Teams that need a GUI dashboard rather than a terminal CLI
- Anyone looking for real-time monitoring of running containers
- Compliance programs needing deep per-package CVE coverage instead of Dockerfile hygiene checks
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Skip Container Diet if you need runtime container monitoring, a managed CVE database, or a GUI dashboard — it is a CLI that reads static images and Dockerfiles only.
AI suggestions require your own API key for whichever provider you configure, so hosted-model usage is billed by that provider on top of the free tool.
Container Diet is MIT-licensed and free to install, which puts it on the opposite end of the market from per-seat container security platforms like Snyk or Sysdig. The only variable cost is the AI provider you wire in: hosted keys from OpenAI, Anthropic, or Groq bill per token, while Ollama keeps analysis local at no per-call cost.
In short
Container Diet — Container Diet scans Docker images and Dockerfiles for size bloat and hardening gaps, then rewrites the Dockerfile with AI. Best for DevOps engineers trimming image size and closing Dockerfile hardening gaps in a single pass, Developers cutting registry storage cost and container startup time, CI/CD pipelines that need automated Dockerfile scanning with JSON output. Free to use.
What is Container Diet?
Container Diet — Container Diet scans Docker images and Dockerfiles for size bloat and hardening gaps, then rewrites the Dockerfile with AI. Best for DevOps engineers trimming image size and closing Dockerfile hardening gaps in a single pass, Developers cutting registry storage cost and container startup time, CI/CD pipelines that need automated Dockerfile scanning with JSON output. Free to use.
Container Diet is a free, MIT-licensed Go CLI that audits Docker images and Dockerfiles for wasted layers and security hygiene, then drafts a slimmer build. Run `container-diet analyze --dockerfile Dockerfile --auto-fix` and it prints a per-layer size breakdown plus AI-drafted recommendations, saving an optimized Dockerfile.diet you diff before applying. The vendor's own demo shows a Python image going from 412MB to 89MB by swapping in a slim base and cleaning apt caches in the same layer. Setup is three commands: `go install`, `container-diet init-config` to write a YAML config, then analyze. Output is tabular for humans or pipeable JSON via `--format json`, so the same audit doubles as a CI/CD gate. It reaches local images through the Docker-compatible socket, so Docker and Podman both work, and it can pull from remote registries or auto-pull missing images. The hardening pass flags root user violations, secrets baked into image layers, 777 permissions, SSH daemons, and stale base images — size and security findings arrive in one report instead of two scanners and a spreadsheet. At v0.5.0 the AI layer is provider-agnostic: OpenAI, Anthropic, OpenRouter, Ollama, Groq, DeepSeek, Mistral, xAI, or any OpenAI-compatible endpoint. Bring a key or point it at Ollama when images must not leave your network. A bundled MCP server exposes four tools to Claude Desktop, Cursor, Codex, and other MCP-compatible agents. This is a Dockerfile hygiene layer for DevOps engineers, platform teams, and developers who want slimmer, safer containers without a dashboard or per-seat bill. It sits next to your CVE scanner, not in place of one.
Key features
- Free MIT-licensed CLI distributed as an open-source Go binary
- Per-layer Docker image size breakdown in tabular CLI output
- JSON output for CI/CD pipelines via --format json
- AI optimization suggestions across 12+ providers including OpenAI, Anthropic, OpenRouter, Ollama, Groq, DeepSeek,
- Bring-your-own-key model access or local inference via Ollama
- Any OpenAI-compatible API endpoint supported
- Auto-fix generation writes a Dockerfile.diet with multi-stage builds, slim base images, layer caching, and hardening
- Detects root user violations in container images
- Detects exposed secrets baked into image layers
- Detects 777 file permissions and SSH daemons
- Flags stale base images
- MCP server exposing 4 tools to Claude Desktop, Cursor, and Codex
- Docker and Podman support via the Docker-compatible socket
- Analyze local daemon images, pull from remote registries, or auto-pull missing images
- One-command install via go install; YAML configuration via container-diet init-config
Behind the verdict
Pull this in when the question is "why is my image 1.2GB and why does it run as root?" You get a per-layer breakdown and a candidate Dockerfile.diet in one pass, and the diff is the review step — nothing is applied behind your back. The auto-fix output is the honest selling point. Its own example takes a Python image from 412MB to 89MB with a slim base and same-layer apt cleanup, which is exactly the kind of fix that takes a mid-level engineer twenty minutes of hunting. The generated file lands next to your original, so a bad suggestion costs you a glance. Where it bites: the security checks are Dockerfile hygiene, not vulnerability management. Root users, secrets in layers, 777 perms, SSH daemons, stale bases — all useful, none of it a CVE feed. A team expecting Trivy-grade package coverage will be disappointed, and that's a scoping problem, not a bug. Terminal-only is a deliberate trade. No dashboard, no hosted service, no per-seat invoice. If your platform team lives in a web UI, this will feel like a step backward; if they live in CI, the `--format json` flag is the whole integration story. The provider list is broader than most BYO-key tools at this price — twelve-plus endpoints including Groq, DeepSeek, Mistral and xAI, plus Ollama for air-gapped shops. In practice, the model quality mostly affects how good the prose suggestions are; the layer sizes and hardening flags are deterministic either way. Our favorite piece is the MCP server: four tools exposed to Claude Desktop, Cursor and Codex, so a developer can ask "why is this image huge" mid-edit. Compare it to hadolint, which lints rules but won't rewrite your build, or to Docker Scout, which covers supply chain but not the same-file rewrite. Container Diet's niche is the auto-fix plus editor
Models under the hood
as of 2026-09-09
Limitations
- Container Diet is a CLI-only tool with no web or mobile interface.
- It analyzes static images and Dockerfiles, not running containers, so it cannot detect runtime issues.
- Its strength is Dockerfile and layer hygiene rather than breadth of vulnerability coverage, so teams with formal security programs will want it alongside a CVE-focused scanner.
- AI suggestions depend on whichever provider or model you configure, so output quality varies with your endpoint.
- Auto-fix output is written as a separate Dockerfile.diet rather than applied in place, so you still have to review and merge it.
- As an open-source project, support is community-driven through GitHub and may lack fast response times.
as of 2026-10-14
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Is Container Diet still active?
Container Diet is live · free · last verified 24 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is Container Diet? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What do users say about Container Diet?
What people actually say about Container Diet — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
Researched27 mentions across 3 sources (YouTube, Product Hunt, Lemmy) · researched Oct 21, 2026.
Weighted by the 37 posts each of 3 sources contributed.
- +Free and MIT-licensed, so teams can audit it without procurement friction
- +Local-only analysis via Ollama keeps sensitive images from leaving the network
- +Combines size bloat and security hardening in a single CLI pass
- +Auto-fix generates a downloadable Dockerfile.diet with multi-stage builds and slim bases
- +Provider-agnostic AI layer works with 12+ backends, avoiding vendor lock-in
- −Users repeatedly ask how it differs from prompting a general LLM directly
- −No documented evidence of successful runs on large, real-world images
- −AI-suggested Dockerfile rewrites may break runtime dependencies without careful review
- −Community discussion is dominated by launch-day speculation, not production testimonials
- −The tool doesn't replace a CVE scanner, so teams still need a second security tool
- • You supply your own LLM API key, so usage costs depend on your provider (OpenAI, Anthropic, etc.)
- • Running local models via Ollama requires adequate hardware and setup time
- • No paid support tier, so troubleshooting relies on the open-source community
How we verified this
Every listed fact about Container Diet— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 24 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Container Diet 11 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed our verdict, who it suits, summary, who should skip it, features, description
- — changed who should skip it, our verdict, who it suits, features, summary, description
- — changed switching out, use cases, switching in, limitations, hidden costs, summary, description, who should skip it, setup time, our verdict, pricing fit, features
- — changed our verdict, who it suits, description, features, who should skip it, summary
- — changed summary, our verdict, pricing fit, description, switching in, setup time, features, switching out, hidden costs, integrations, limitations, who should skip it, use cases, who it suits
- — changed description, who it suits, summary, our verdict, who should skip it, features, integrations
Showing the 6 most recent of 11 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Container Diet cost in 2026?
Container Diet: Free to use. Pricing last verified 24 Oct 2026.
Where the pricing makes sense
The company stage and team size where Container Diet's pricing actually pencils out — and where peers do it cheaper.
Container Diet is MIT-licensed and free to install, which puts it on the opposite end of the market from per-seat container security platforms like Snyk or Sysdig. The only variable cost is the AI provider you wire in: hosted keys from OpenAI, Anthropic, or Groq bill per token, while Ollama keeps analysis local at no per-call cost.
Has Container Diet's pricing changed?
No plan price for Container Diet has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Container Diet?
Pick Container Diet if you are DevOps engineers trimming image size and closing Dockerfile hardening gaps in a single pass, Developers cutting registry storage cost and container startup time, CI/CD pipelines that need automated Dockerfile scanning with JSON output, Air-gapped or regulated teams running Ollama locally so image data never leaves the network.
Look elsewhere if you are Teams that need a GUI dashboard rather than a terminal CLI, Anyone looking for real-time monitoring of running containers, Compliance programs needing deep per-package CVE coverage instead of Dockerfile hygiene checks, Users who want a hosted SaaS with zero local install.
Skip it if Skip Container Diet if you need runtime container monitoring, a managed CVE database, or a GUI dashboard — it is a CLI that reads static images and Dockerfiles only.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Container Diet actually fits — and what changes day-one when you adopt it.
Adds container-diet analyze --format json to the CI pipeline and fails pull requests that increase image bloat or reintroduce a root user.
Outcome: Image size growth becomes a reviewed decision instead of something discovered during a slow deploy.
Uses the MCP server's four exposed tools to ask about the Dockerfile currently open in the editor instead of switching to a terminal.
Outcome: Hardening and slim-base advice arrives while the Dockerfile is being written, not after the image ships.
Configures Ollama in ~/.config/container-diet/config.yaml and runs analysis entirely against a local model.
Outcome: Layer and hardening findings stay inside the perimeter while still getting AI-generated suggestions.
Use cases
- Scan a Docker image and get a list of unnecessary packages to remove, shrinking it before deployment.
- Add the JSON output to a CI pipeline so a pull request that increases image bloat fails the build.
- Audit registry images for hardening misses like root user, exposed secrets, 777 permissions, and SSH daemons.
- Generate a Dockerfile.diet and diff it against your original to learn multi-stage and slim-base patterns.
- Compare base images (alpine vs ubuntu) to see which yields the leanest final image for your stack.
- Call the analyzer from Cursor or Claude Desktop through the MCP server while writing a Dockerfile.
- Route image analysis to a local Ollama model for images you would rather not send to a hosted API.
- Produce a pre-deployment report confirming no new bloat has crept into an image.
Setup time & first value
How long it actually takes to get something useful out of Container Diet — broken out by persona, not the marketing-page minute.
One command to install (go install github.com/k1lgor/container-diet/cmd/cli@latest), one to configure (container-diet init-config), then you edit ~/.config/container-diet/config.yaml with an API key or Ollama endpoint. Expect minutes to a first Dockerfile report if you already have Go and a model endpoint; add a CI step after that if you want the JSON output gating builds.
Switching to or from Container Diet
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From manual Dockerfile review: run analyze --dockerfile Dockerfile to get hardening findings and size context in one report.
- →From a size-only image tool: use --format json to keep size auditing and add root user, secrets, and 777 permission checks in the same run.
- →From a CVE-focused scanner: keep it for vulnerability coverage and add Container Diet for layer hygiene and auto-fix generation.
- →From eyeballing layer diffs: compare base images like alpine vs ubuntu directly to see which yields the leanest final image.
- ↗To Trivy or Snyk: move there for CVE coverage, but you lose the layer-size breakdown and auto-fix Dockerfile generation in the same report.
- ↗To a container security platform: move there if you need runtime monitoring and managed dashboards, giving up the terminal-first CLI and MCP integration.
- ↗To manual Dockerfile authoring: drop the tool entirely if you do not want AI-generated rewrites to review, but you take back the hardening checks too.
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “Container Diet”, and we withheld 6: 6 did not mention Container Diet. We are showing none, because we could not prove any of them are about Container Diet.
Official links
What are the best Container Diet alternatives?
The closest alternatives to Container Diet we list are Snyk, Dagger and Mintlify. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Snyk | Freemium | Pricing not published | — |
| Dagger | Freemium | Pricing not published | — |
| Mintlify | Freemium | Pricing not published | — |
Alternatives to Container Diet
View all
Tools that pair well with Container Diet
Common stack mates teams adopt alongside Container Diet, with the specific reason each pairing earns its keep.
Snyk
Snyk secures AI-generated code, governs coding agents, and pentests AI-native apps before they ship.
Dagger
Dagger turns CI/CD into programmable, container-native pipelines you write in Go, Python, or TypeScript and run the same locally, remotely,
Mintlify
Mintlify is the documentation platform for agents, shipping an MCP server with every docs site.
Container Diet vs its closest rival
Container Diet's closest head-to-head is with Spider Cloud — Spider Cloud and Container Diet are not direct competitors—they solve completely different problems. Spider Cloud is a web data extraction API for AI agents, while Container Diet is a Docker image optimization CLI. Choose Spider Cloud if you need real-time web content for RAG or LLMs; choose Container Diet if you want to shrink your container images. Both are strong in their niche. Verdict generated
Head-to-head comparisons
Container Diet vs Spider Cloud
Spider Cloud and Container Diet are not direct competitors—they solve completely different problems. Spider Cloud is a web data extraction API for AI agents, while Container Diet is a Docker image optimization CLI. Choose Spider Cloud if you need real-time web content for RAG or LLMs; choose Container Diet if you want to shrink your container images. Both are strong in their niche.
GeneratedContainer Diet vs Voyage Ai
Voyage AI and Container Diet serve fundamentally different needs—improving AI retrieval accuracy vs. slimming Docker images. For AI RAG pipelines requiring domain-specific embeddings and enterprise compliance, Voyage AI is the clear choice despite opaque pricing. For DevOps teams wanting a free, open-source tool to cut container bloat and fix security issues, Container Diet delivers unique value. Choose based on your primary pain point: retrieval quality or container efficiency.
GeneratedContainer Diet vs Temporal Ai
Temporal AI and Container Diet address entirely different problems. If you need to build fault-tolerant AI agents or multi-step workflows that survive crashes, Temporal's durable execution platform is essential. But if you're simply looking to slim down Docker images, Container Diet delivers fast, actionable, and entertaining advice for free. Choose based on your workflow orchestration needs versus container optimization requirements.
GeneratedFrequently asked questions
Best-of guides
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