Comparison at a Glance
Four routes to running ComfyUI in production. All managed options handle infrastructure - the differences are in cost model, customization, and who controls the GPU.
| ComfyDeploy | ViewComfy | Runflow | DIY (Docker) | |
|---|---|---|---|---|
| Setup time | Minutes | Minutes | Minutes | Hours to days |
| GPU pricing model | Not public | T4 $0.65/hr · A100 $4.10/hr | $0.05–$0.55/img (contact) | From $0.44/hr (RunPod spot) |
| Cold start | Memory snapshot (1-click) | 8–10s (snapshot) / 30–77s (cold) | Managed | Depends on setup |
| Custom nodes | Full - all plans | Team plan and above | Via workflow API | Full control |
| Auth / access control | Built-in | Built-in | Built-in (REST API keys) | DIY (Nginx / reverse proxy) |
| Scaling | Managed | Managed | Managed | Manual |
| ComfyUI version | Platform-managed | Platform-managed | Platform-managed | You choose |
| Best for | Dev teams deploying UIs | Collaborative studio / client work | High-volume API products | Cost-sensitive, high-volume batch |
What Each Option Actually Is
Before pricing, the architecture question: are you deploying a UI that humans interact with, or an API that code calls? That single decision determines which option makes sense.
ComfyDeploy, ViewComfy, and Runflow all host ComfyUI on managed infrastructure - you do not touch servers, configure Nginx, or manage GPU drivers. The difference is in their intended user. DIY means you rent a GPU (RunPod, Salad, Vast.ai), run ComfyUI yourself in Docker, and own all the operational concerns that come with that.
ComfyDeploy
ComfyDeploy is a cloud platform purpose-built for deploying ComfyUI workflows as APIs and shareable UIs. You connect your ComfyUI workflow, and ComfyDeploy handles the infrastructure: GPU allocation, model loading, endpoint exposure, and auth.
The standout feature is memory snapshots. ComfyDeploy can snapshot the GPU memory state of a loaded ComfyUI session, so subsequent cold starts skip the model loading step entirely - resuming in under a second rather than 30–90 seconds. For production APIs where users wait on responses, this eliminates the most painful part of serverless GPU hosting.
ComfyDeploy strengths
- Full custom node support on all plans - you are not locked to a curated node library
- Memory snapshot cold starts - one-click activation, dramatically reduces first-request latency
- UI sharing - deploy a workflow as a shareable link with a built-in UI, no frontend code required
- API endpoints - every deployed workflow also gets a REST API endpoint for code-driven access
- Version management - deploy multiple versions of a workflow, roll back without downtime
ComfyDeploy limitations
- Pricing not public - you need to contact sales or sign up to see compute costs; budget planning requires a direct conversation
- GPU selection is platform-managed - you do not choose specific hardware; the platform allocates based on your workflow requirements
- Newer platform vs RunPod and Vast.ai - smaller community and less public documentation on edge cases
ViewComfy
ViewComfy positions itself as the collaborative layer for ComfyUI teams. The core product is a visual workflow editor built on top of ComfyUI, with cloud hosting that makes it easy to share workflows with clients or non-technical collaborators who do not need to understand ComfyUI's node graph.
GPU pricing is published: T4 at $0.65/hr and A100 at $4.10/hr. This is higher than raw GPU rental prices (Vast.ai T4 can be found for $0.10–$0.20/hr), but you are paying for the managed platform layer: the UI, collaboration features, model library, and uptime management.
ViewComfy cold start behavior
ViewComfy uses memory snapshots on paid plans. With a snapshot, cold start is 8–10 seconds. Without a snapshot (first deploy or snapshot not yet created), cold start runs 30–77 seconds depending on model size and the GPU it lands on. For a client-facing UI where someone clicks "Generate" and waits, 30–77 seconds is noticeable; 8–10 seconds is acceptable.
ViewComfy strengths
- Published pricing - T4 $0.65/hr and A100 $4.10/hr are visible without contacting sales
- Collaboration focus - designed for sharing workflows with clients and team members who don't use ComfyUI directly
- Memory snapshots on paid plans - 8–10s cold starts vs 30–77s without
- Model library - common models pre-loaded, reducing setup time for standard workflows
ViewComfy limitations
- Custom nodes on Team plan and above - not available on entry-level plans
- A100 at $4.10/hr is expensive vs DIY - a 10-hour workload costs $41 vs $6–$14 self-hosted on Vast.ai
- Primary focus is human-driven UI use, not high-throughput programmatic API workloads
Runflow
Runflow is built for teams that need ComfyUI's workflow power exposed as a clean REST API - not a UI that humans visit, but an endpoint that applications call. The pricing model reflects this: per-image billing ($0.05–$0.55 per image) that scales with actual usage rather than hourly GPU time.
The per-image model changes the economics significantly. At $0.10/image (mid-range of the pricing band), 10,000 images cost $1,000. On a self-hosted RTX 4090 on RunPod ($0.69/hr at ~400 imgs/hr for complex ComfyUI workflows), the same 10,000 images cost ~$17 in compute. The premium pays for zero infrastructure management, managed scaling, and guaranteed uptime - relevant when engineering time has a cost.
Runflow strengths
- API-first architecture - designed for code-to-code integration, not human-facing UIs
- Per-image pricing - pay for what you generate, no idle GPU costs during slow periods
- Managed scaling - handles traffic spikes without you provisioning additional capacity
- Built-in auth - API key management, rate limiting, and access control included
- Workflow versioning and rollback - deploy new workflow versions without breaking existing integrations
Runflow limitations
- Per-image pricing is significantly more expensive than self-hosted at high volume - best below 50K images/month
- Custom node support via workflow API - not a direct ComfyUI node graph environment; some custom node patterns need adaptation
- Contact required for volume pricing - no public rate card above entry tier
DIY: Docker on a GPU Cloud
The DIY option means: rent a GPU from RunPod, Salad, or Vast.ai; run a Docker container with ComfyUI installed; expose port 8188 to the internet; build your own auth layer, your own job queue, your own scaling logic. In exchange, you get the lowest cost per image and maximum control over everything.
From $0.44/hr on RunPod spot, a self-hosted ComfyUI setup running a complex workflow (ControlNet, upscale, multiple models) generates roughly 200–400 images per hour. At $0.44/hr and 300 images/hr, that is $0.0015/image - 33–366× cheaper than managed options depending on the platform and plan.
What "DIY" actually involves
- 1Container build: a Dockerfile installing ComfyUI, your custom nodes, and a startup script
- 2Model management: downloading model weights at startup from Hugging Face or your own storage (S3, R2)
- 3Auth layer: Nginx or a reverse proxy handling API key validation before requests reach ComfyUI
- 4Job queue: Redis or SQS to queue requests, handle concurrency, and retry failed jobs
- 5Monitoring: health checks, uptime alerts, log aggregation
- 6Scaling: for Salad, replica count configuration; for RunPod, manual pod management or custom orchestration
This is 8–40 hours of engineering setup plus ongoing maintenance. For teams generating under 100K images/month, this overhead often costs more in developer time than the savings justify. Above that volume, the economics flip decisively.
Total Cost Comparison: 10,000 Images Per Month
Assumes complex ComfyUI workflow (ControlNet + upscale, ~300 images/hr on RTX 4090). Managed platform estimates use mid-range pricing. DIY uses RunPod RTX 4090 at $0.69/hr.
| Option | Compute cost | Setup / ops overhead | Effective total | Notes |
|---|---|---|---|---|
| Runflow (mid-tier) | ~$1,000 | Near zero | ~$1,000 | $0.10/image estimate |
| ViewComfy (T4) | ~$217 | Near zero | ~$217 | T4 $0.65/hr × 33.3hrs |
| ComfyDeploy | Not public | Near zero | Contact for quote | - |
| DIY - RunPod RTX 4090 | ~$23 | ~$200/mo amortized | ~$223 | $0.69/hr × 33.3hrs + eng time |
| DIY - Salad RTX 4090 | ~$5 | ~$200/mo amortized | ~$205 | $0.16/hr × 33.3hrs + eng time |
Custom Nodes: The Deciding Factor
Custom ComfyUI nodes are the reason most teams do not switch away from self-hosted. The ecosystem of nodes for ControlNet, IP-Adapter, AnimateDiff, GGUF loading, WAS nodes, and hundreds of community contributions is what gives ComfyUI its power. Any platform that restricts custom nodes is fundamentally limiting what you can build.
ComfyDeploy supports full custom nodes on all plans - no restrictions, install what you need. ViewComfy restricts custom nodes to Team plans and above - a significant limitation for teams on entry-level plans. Runflow exposes ComfyUI via its workflow API - not a direct node graph environment, so your custom node architecture needs to fit their abstraction. DIY has full control - install any node, any version, without asking permission.
When custom nodes rule out managed options
- Workflows using AnimateDiff or video generation nodes - often rely on specific community node versions
- Production pipelines built around GGUF-based models loaded via llama.cpp nodes
- Highly customized ComfyUI-Manager-installed nodes with specific dependency versions
- Any workflow where you need to modify node source code for your use case
Decision Framework: Which Option to Choose
Choose a managed platform (ComfyDeploy, ViewComfy, or Runflow) when
- Your team does not have infrastructure expertise - managed platforms eliminate the GPU DevOps learning curve
- You need to be live within hours, not days - managed options are minutes from signup to working endpoint
- Volume is under 50,000 images/month - managed overhead is competitive once engineering time is factored in
- You need built-in collaboration or client-sharing - ViewComfy is the clear choice here
- You're building a product that calls ComfyUI via API without managing infrastructure - Runflow is purpose-built for this
Choose DIY when
- Volume exceeds 100,000 images/month - compute savings exceed operational overhead significantly
- You need full custom node control without platform restrictions or upcharges
- You require specific GPU models (A100, H100) at competitive rates - Vast.ai and Salad beat any managed platform on price for these
- You have strict data privacy requirements - images must stay within your own infrastructure
- Your pipeline involves multi-step workflows with custom tooling that does not fit within any managed platform's abstraction
Want to know which models run on your GPU? Try our GPU Matcher to instantly see all compatible models with optimal quantization and memory requirements.