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Cline became the default open-source coding agent in 2025 after the editor-integrated workflow proved better than the autonomous-loop approaches that came before it. Most users run Cline against the Anthropic or OpenAI API, which works great and costs more than people expect at heavy usage.
The interesting hosting question is the model backend. Here is the realistic shortlist for teams that want a self-hosted Cline endpoint.
When Self-Hosting Makes Sense for Cline
Three cases:
- You have privacy requirements that rule out external API calls
- Your team has 5+ heavy Cline users and the monthly API bill exceeds 300 USD
- You want to experiment with coding-specific open models (DeepSeek Coder, Qwen Coder, StarCoder) that API providers do not offer
Outside these cases, the Anthropic API is faster and produces better code than any self-hosted setup under 200 EUR a month. Be honest about which side you are on.
Model Choice Drives the Spec
Cline benefits from larger coding-specialized models. The realistic options for self-hosting:
- DeepSeek Coder V2 16B: needs 16 GB VRAM for Q4, decent quality
- Qwen 2.5 Coder 32B: needs 24 GB VRAM for Q4, much better quality
- DeepSeek Coder V2 236B: needs 80 GB+ VRAM even at Q4, top tier but expensive
For meaningful productivity gains over the API, you want at least Qwen 2.5 Coder 32B. That sets the GPU floor.
VPS Comparison for Cline Backend
| Provider | Plan | GPU | vCPU | RAM | Monthly | Best fit |
|---|---|---|---|---|---|---|
| Hetzner Cloud | CCX33 | None | 8 | 32 GB | 138.49 EUR | 7B CPU inference, solo light use |
| Hetzner Server | GEX44 | RTX 4000 SFF Ada (20 GB) | 14 | 64 GB | 234 EUR + 114 setup | 32B at 4-bit, small team |
| Genesis Cloud | RTX 5090 | RTX 5090 (32GB) | 16 | 64 GB | ~120 EUR | 16B production, single user |
| Hetzner Server | GEX131 | RTX PRO 6000 Blackwell (96 GB) | 24 | 256 GB | 1,199 EUR + 599 setup | 70B coder model, team |
Hetzner CCX33: For solo light use with 7B models
If you accept that 7B coding models are limited and you want to experiment without GPU spend, the CCX33 with 32 GB RAM runs DeepSeek Coder 7B on CPU at 5 to 7 tokens per second. This is barely acceptable for Cline’s autocomplete flow and painful for the agentic workflows.
Pick this for personal experimentation only. The productivity hit versus API or GPU-backed models is real.
Get Hetzner: Hetzner Cloud.
Hetzner GEX44: The serious self-hosted Cline backend
234 EUR a month plus a one-off 114 EUR setup fee for an RTX 4000 SFF Ada with 20 GB VRAM runs Qwen 2.5 Coder 32B at 4-bit — but only just. The weights alone take about 19 GB, so you are working with almost no context headroom, and a long Cline session will feel it. A 13B coder model is the comfortable fit on this card.
This is the sweet spot for teams of 3 to 8 developers sharing a backend. The 48 GB VRAM also lets you swap models without restarting (one 32B model takes 22 GB, leaving room for another 13B model in parallel).
Pros for Cline specifically:
- VRAM size matches coding-specialized model sweet spot
- Network throughput handles concurrent developer requests
- Same data center as Hetzner Cloud means you can put the orchestration layer next door cheaply
The math: 234 EUR a month divided by 5 active developers is 47 EUR per developer, less than half a typical individual Cursor + Anthropic API cost.
Get Hetzner: Hetzner Cloud.
Genesis Cloud RTX 5090: For single developer with 16B models
Around 120 EUR a month gets you an RTX 5090 with 32 GB VRAM, which fits 16B coding models comfortably. Genesis Cloud pricing varies; check current rates. Useful for individuals who want privacy without the team-scale GPU commitment.
Hetzner GEX131: For teams with 70B coder models
When 32B is not enough quality and you want DeepSeek Coder V2 70B, the GEX131 has the VRAM: a single RTX PRO 6000 Blackwell with 96 GB. At 1,199 EUR a month plus a 599 EUR setup fee this is a serious commitment, but for an 8 to 15 developer team it can still beat Anthropic API costs at heavy usage. Note that the older dual-GPU GEX130 is no longer on Hetzner’s matrix and should be treated as discontinued.
What I Would Pick
For a single developer who wants to try self-hosting: skip it, use the Anthropic API and save the operational time. For a team of 3+ heavy Cline users: Hetzner GEX44, on a 13B coder model rather than a 32B unless you accept the tight context. The 234 EUR break-even sits around 4 developers if each would otherwise spend 60 to 80 USD a month on API costs.
Full VPS context is at the SelfHostVPS comparison. Coding agent hosting is a relatively new niche, expect more providers to enter this market through 2026.
Frequently asked questions
Does Cline need a VPS at all? It runs in my IDE.
Cline itself runs in your IDE (VS Code, Cursor, etc.) and does not need server hosting. The VPS question comes up when you want to host the model backend Cline talks to. For users who prefer self-hosted models over OpenAI or Anthropic API costs, running Ollama or vLLM on a VPS gives Cline a cheap local endpoint that scales with usage.
Which VPS spec is best for a Cline + Ollama backend?
For 7B coding models (DeepSeek Coder, Qwen Coder), a Hetzner CCX33 with 32 GB RAM and CPU inference gives 5 to 7 tokens per second, which is barely acceptable for Cline's coding workflow. For real production use with 13B or 32B coding models, you need GPU. The Hetzner GEX44 at 234 EUR a month plus a 114 EUR setup fee is the cheapest serious option, though its 20 GB of VRAM only just holds a 32B model at 4-bit.
Can Cline use a shared model backend for my whole team?
Yes, this is the strongest argument for self-hosting. A single GPU VPS running Qwen 2.5 Coder 32B at 4-bit can serve a handful of developers using Cline without becoming the bottleneck. The shared backend approach pays back the 234 EUR a month easily once you have 3+ heavy users.
Should I just use Anthropic Claude API instead of self-hosting for Cline?
For most individual developers, yes. Claude Sonnet via the API is faster than any self-hosted setup under 200 USD a month and produces better code. Self-hosting makes sense when you have privacy requirements (your code cannot leave your infrastructure), team-scale usage (5+ heavy users), or specific model preferences that API providers do not offer.