Self-Hosted Continue.dev with Ollama vs. Cloud-Connected: What Five-Person Startups Actually Give Up
This is not a best-of list. Those answer a question you didn’t ask. What you need is a decision framework for whether to run your own AI coding assistant infrastructure when your team is small, your runway is finite, and your tolerance for yak-shaving is lower than your tolerance for shipping slowly.
Step 1: Deconstruct Your Actual Situation
You are five people. Maybe pre-revenue, maybe seed-funded with twelve to eighteen months of runway. You have no dedicated DevOps hire. Your compliance regime is either minimal or in the “SOC 2 prep” phase where you’re documenting things you haven’t enforced yet. Your code contains whatever you’ve built so far—maybe nothing regulated, maybe something that will eventually need data residency guarantees.
Your budget ceiling is real. Your time ceiling is invisible until it breaks something.
Step 2: Segment Into Sub-Decisions
The choice between self-hosted Continue.dev+Ollama and a cloud-connected alternative fractures into four actual decisions, not one:
| Sub-decision | Self-hosted path | Cloud-connected path |
|---|---|---|
| Hardware sourcing vs. cloud API spend | Buy or lease GPU hardware; pay for electricity and colocation | Pay per-seat monthly fees plus potential usage overages |
| Model quality vs. data residency | Local models see no external network; quality depends on model size you can run | Frontier model access; code leaves your environment |
| Maintenance burden vs. setup speed | Ongoing Ollama administration, driver updates, model pulls | Account creation, API key, install extension |
| Team AI literacy vs. documentation quality | Team must debug inference failures without vendor support | Vendor handles model updates, availability, routing |
Step 3: Diagnose the Actual Bottleneck
Here is the question that matters: does anyone on your team want to become the Ollama admin, or is shipping speed the only metric that matters?
If you have a founder or early engineer who finds infrastructure satisfying—who will cheerfully spend a weekend getting CUDA drivers right and won’t resent the ongoing model management—self-hosting is viable. If that person does not exist, self-hosting becomes an unstaffed role that bleeds velocity during every model update or inference failure.
The bottleneck is rarely technical. It is organizational: who owns the thing when it breaks at 11 PM before a demo?
Step 4: Prescribe a Specific Path
If no one wants the Ollama admin role: Use a cloud-connected option. Accept the trade-off that your code leaves your environment and you pay ongoing per-seat fees. The specific pick depends on your existing stack and privacy tolerance.
If someone owns the infrastructure role and your code contains nothing regulated: Consider self-hosted Continue.dev with Ollama. Accept the trade-off that you trade money for time, and that model quality will lag behind frontier models you could access via API.
If your code contains export-controlled algorithms, patient data under HIPAA without a BAA, or strict data residency requirements: Self-hosting or a privacy-first cloud option becomes necessary, not optional. The trade-off shifts to hardware cost and model capability.
What Continue.dev Actually Is Now
Continue.dev’s status changed materially in 2025. Per their official site, Continue was acquired by Cursor. The final open-source release is documented in their GitHub releases. That release removed anonymous telemetry and authentication requirements, per the changelog—changes that matter if you’re evaluating self-hosting for privacy reasons.
This creates future-maintenance uncertainty for self-hosters. The codebase remains freely available, but Cursor’s commercial incentives run toward their own product, not toward continuing development of an open-source alternative. You are not adopting into an active ecosystem; you are adopting into a snapshot. Plan accordingly.
What Ollama Provides and Demands
Ollama enables local model inference. For code models specifically, it requires GPU RAM sufficient for the model size you want to run—larger models demand more memory. The vendor’s official documentation discusses hardware requirements qualitatively; specific GPU RAM figures vary by model and are not stated in the material reviewed.
The operational reality: you are running a model server. Someone updates it, monitors it, and troubleshoots when completions slow or fail. Ollama abstracts much of this, but “self-hosted” still means “your responsibility when the abstraction leaks.”
Cloud-Connected Alternatives: Structural Comparison
For teams rejecting the Ollama admin role, the landscape has specific price anchors:
| Tool | Pricing structure | Relevant note |
|---|---|---|
| GitHub Copilot Business | The vendor’s official pricing page lists $19 USD per user per month | Includes 1,900 AI credits per user; pooled at billing entity level |
| Cursor Teams | The vendor’s official pricing page lists $32 per seat per month for annual plans; $40 per seat per month for monthly plans | Charged at public list API prices plus Cursor Token Rate for third-party model requests |
| Sourcegraph Cody Pro | The vendor’s official pricing page lists $9 per user per month | Enterprise Starter tier lists $19 per user per month |
| Sourcegraph Cody Enterprise Starter | The vendor’s official pricing page lists $19 per user per month | — |
| Tabnine | Pricing varies by plan — check the vendor’s current pricing page | Acquired by Tricentis; emphasizes “AI code assistant that you control” |
| JetBrains AI Assistant | Pricing varies by plan — check the vendor’s current pricing page | Supports bring-your-own API keys; 20 JetBrains AI Credits per 30 days included in Toolbox subscription |
Cursor’s own commercial tiers create tension for Continue.dev self-hosters: Cursor now owns Continue, and Cursor’s revenue comes from cloud subscriptions. The Teams plan’s compute-limit pricing means unpredictable costs if your usage exceeds pooled allowances.
JetBrains AI Assistant and Tabnine position themselves as privacy-conscious alternatives. JetBrains explicitly supports bringing your own API keys or base URL. Tabnine, post-acquisition by Tricentis, emphasizes organizational control over token processing.
Who Self-Hosted Continue.dev + Ollama Is WRONG For
- Teams without hardware budget. GPU hardware for code models is not optional. If you cannot allocate capital to hardware or sustained GPU cloud instances, this path is closed.
- Teams needing frontier model quality. Local models you can reasonably run on startup-accessible hardware will not match the capability of frontier models accessible via API. If your work requires cutting-edge reasoning, accept the cloud trade-off.
- Teams where no one wants infrastructure ownership. Unstaffed infrastructure becomes failed infrastructure. Do not assume someone will “just figure it out.”
- Teams expecting active maintenance. The final open-source release is a snapshot. You are not getting new features or security patches on a predictable cadence.
Who Cloud-Connected Is WRONG For
- Teams with strict data residency requirements. If your contracts or regulations require code to remain in specific jurisdictions, verify where cloud providers process data. This is not security advice; consult qualified counsel for your regulatory context.
- Teams whose code contains export-controlled or patient data under HIPAA without a BAA in place. Cloud processing of such data without appropriate contractual protections creates compliance exposure. The vendor’s official documentation may describe security controls; applicability to your specific situation requires independent verification. This is not security advice.
- Teams where per-seat costs compound unsustainably. At five people, $19-$40 per seat per month is manageable. At scale, or with usage overages, the math shifts.
The Specific Recommendation
For the five-person startup with no dedicated DevOps, no regulated data, and shipping speed as the primary metric: adopt a cloud-connected option, likely GitHub Copilot Business or Cursor Teams depending on your IDE and workflow. Accept the trade-off of recurring per-seat cost and code leaving your environment. The time you do not spend administering Ollama is time spent on product.
The exception: if you have a team member who actively wants to own local inference infrastructure, and your code contains nothing that creates compliance exposure if leaked, self-hosted Continue.dev with Ollama is defensible. Accept the trade-offs of frozen codebase, hardware cost, and model quality below frontier level.
Commercial Disclosure
WeTooling has no affiliate relationships with any vendor discussed here and receives no commission. WeTooling is in phase one; we do not run our own tests or benchmarks. All figures cited above come from vendor documentation and official pricing pages as noted. Where no figure was available in the material reviewed, we have written qualitatively.
Visit DigitalOcean’s official site
Bottom line: The decision is not technical. It is organizational. Name who owns the infrastructure before you choose the infrastructure. If no one raises their hand, you have already chosen cloud-connected—now pick which one based on your existing stack and the specific per-seat costs the vendors’ official pricing pages list.
