API-Based AI Coding: When to Use Continue.dev, Cody, and Codeium Outside the Browser

Use Continue.dev when you need full control over models, keys, and context, including local inference. Use Cody when your bottleneck is understanding a large, multi-repo codebase and your org already runs Sourcegraph. Use Codeium (now Windsurf) when you want fast, low-friction autocomplete without managing API keys or per-token bills.

Browser chat is fine for one-off questions. The moment you are pasting code back and forth, you have already lost. The real productivity gains from these tools come from running them inside the editor and, in Continue's case, wiring them directly to model APIs you control. This article is about that second mode: what it costs, when it wins, and when it is the wrong call.

What "API-based" actually means here

These three tools sit at different points on a spectrum of control:

• Continue.dev is an open source extension for VS Code and JetBrains where you supply the model. You point it at Anthropic, OpenAI, a Bedrock or Azure endpoint, or a local Ollama instance, and it handles chat, inline edits, and autocomplete against whatever you configured. The Continue documentation covers dozens of providers.
• Cody from Sourcegraph is API-based in a different sense: it calls hosted models on your behalf, but its differentiator is the context API, meaning Sourcegraph's code graph and search feeding relevant snippets from repos you do not have checked out. Sourcegraph has refocused Cody on enterprise deployments, so evaluate it as an org-level purchase, not a personal tool. Details live in the Cody docs.
• Codeium rebranded as Windsurf and now ships both a plugin for existing IDEs and its own editor. It runs proprietary models tuned for completion speed. You do not bring a key; you accept their inference in exchange for near-zero setup and very low autocomplete latency.

So "outside the browser" splits into two sub-questions: which tool goes in your editor, and whether you route inference through your own API keys or a vendor's hosted plan.

When does bringing your own API key actually pay off?

Continue with your own Anthropic or OpenAI key is the only option here that exposes raw per-token economics, so let's be concrete. Anthropic's pricing puts Claude Sonnet class models at roughly 3 dollars per million input tokens and 15 dollars per million output tokens. A typical chat turn with a few files of context runs 5,000 to 15,000 input tokens. If you do 40 such turns a day, you are spending somewhere between 1 and 4 dollars daily on chat, before prompt caching. That is often cheaper than a flat 20 dollar seat if your usage is bursty, and dramatically more expensive if you run long agentic edit loops that re-send context on every step.

Bring-your-own-key wins clearly in these situations:

• Compliance requires a specific endpoint. If your security team has approved AWS Bedrock or Azure OpenAI but not vendor SaaS, Continue pointed at your approved endpoint is the difference between using AI at work and not using it at all.
• You need a model the hosted tools do not offer. New model drops are usable in Continue the day the API is live. Hosted assistants lag by weeks or never add them.
• You want local inference for air-gapped or sensitive code. Continue plus Ollama running a 7B coder model gives you autocomplete that never leaves the machine.

Here is a Continue config that reflects a setup I actually recommend: a frontier model for chat and edits, a small local model for autocomplete, because autocomplete over a paid API is a cost and latency mistake (more on that below):

1name: byok-assistant
2version: 1.0.0
3models:
4 - name: claude-sonnet
5 provider: anthropic
6 model: claude-sonnet-4-20250514
7 apiKey: ${{ secrets.ANTHROPIC_API_KEY }}
8 roles: [chat, edit, apply]
9 - name: local-autocomplete
10 provider: ollama
11 model: qwen2.5-coder:7b
12 roles: [autocomplete]

The failure mode to watch: BYOK means nobody is rate-limiting you but your credit card. An agentic refactor that loops on a failing test can burn several dollars in minutes because each iteration re-sends the working set. Set a hard monthly budget cap in your provider's console before you start, not after the first surprising invoice.

The autocomplete latency problem nobody prices in

Autocomplete is a different workload from chat, and this is where most BYOK setups go wrong. Useful inline completion needs to appear in well under half a second, ideally under 300 milliseconds, or you type through it and the suggestion becomes noise. Frontier models over public APIs routinely take 500 milliseconds to over a second just to first token, and every keystroke can trigger a request.

This is why Codeium built its business on purpose-trained, latency-optimized completion models served from their own infrastructure, and why it still feels snappier than most BYOK autocomplete configurations. It is also why the Continue config above routes autocomplete to a local Ollama model: a quantized 7B model on an M-series Mac or a modest GPU returns completions fast enough to be usable, and it costs nothing per keystroke.

My honest ranking for pure autocomplete quality-per-friction: Windsurf's plugin first, a well-tuned local model in Continue second, frontier API autocomplete a distant third. If autocomplete is 80 percent of what you want from AI tooling, do not build a BYOK setup at all. Install the Windsurf plugin and move on.

Where Cody fits: context, not keys

Cody's pitch is orthogonal to the other two. Continue answers "which model and where does it run." Cody answers "what does the model know about." If you work in a 40-repo microservice environment, the hard part of every AI interaction is context assembly: the relevant proto definitions live in one repo, the client in another, the deployment config in a third. Cody backed by a Sourcegraph instance can pull context across all of them without you cloning anything, because it rides on the same code graph that powers Sourcegraph search.

That capability has real prerequisites. You need a Sourcegraph deployment indexing your repos, someone administering it, and an enterprise contract. For a solo developer or a small team in a monorepo, Cody is over-engineered; Continue with good use of @codebase and file mentions covers you. The decision is organizational: if Sourcegraph is already in the building, turning on Cody is nearly free use on that investment. If it is not, do not adopt a code intelligence platform just to get an AI assistant.

One caveat from experience: cross-repo context retrieval is only as good as the indexing. Stale indexes or unindexed private dependencies produce confidently wrong answers that reference old function signatures. Treat any Cody answer that cites a file you have not opened as a claim to verify, not a fact.

When is the browser or a hosted plan still the right call?

API-based, in-editor setups are the wrong choice more often than enthusiasts admit:

• Exploratory design conversations. Long architectural back-and-forth with no specific files involved works fine in a browser chat, and hosted chat UIs often have better conversation management, artifacts, and sharing than any IDE panel.
• Unpredictable heavy usage. If you are running long agent sessions daily, a flat-rate subscription with generous limits beats metered API billing. BYOK converts a fixed cost into a variable one, which is only good if your variance is low.
• Teams without an infrastructure owner. A Continue config with secrets management, provider fallbacks, and a local model runtime is infrastructure. If nobody owns it, it rots: keys expire, model IDs get deprecated (Anthropic and OpenAI both retire model versions, and a hardcoded model string in a shared config fails for the whole team at once), and the tool quietly stops working.
• Onboarding juniors. Hand a new engineer Windsurf or a hosted Copilot-style plan. Handing them a YAML file and an API key adds a support burden that outweighs the flexibility.

A concrete way to decide

Run this decision in order and stop at the first match.

Reach for Continue.dev if any of these is true: your code cannot leave approved infrastructure, you need a specific model or endpoint the hosted tools lack, or you want local inference. Budget an afternoon for setup, set a provider spend cap immediately, and pair a frontier API model for chat with a local model for autocomplete.

Reach for Cody if your organization already runs Sourcegraph and your daily pain is reasoning across repos you do not have locally. Do not adopt it standalone for a small codebase.

Reach for Codeium/Windsurf if what you actually want is excellent autocomplete with zero configuration, or you are equipping a team where nobody wants to own tooling infrastructure.

Stay in the browser for design discussions, one-off questions, and anything where you would not have opened the editor anyway.

The mistake I see most is treating this as a single choice. It is not. My own setup is Continue for chat and edits against an Anthropic key, a local model for completions, and a browser tab for architecture arguments. Pick per workload, cap your spend, and re-evaluate quarterly, because the model APIs underneath all three tools change faster than any article about them.