Open-source AI code assistant for VS Code and JetBrains with a hub for custom assistants, MCP tools, prompts, and rules — the go-to when a team wants a Copilot-shaped experience they fully control.
Open-source AI code assistant for VS Code and JetBrains with a hub for custom assistants, MCP tools, prompts, and rules — the go-to when a team wants a Copilot-shaped experience they fully control.
Continue.dev's status changed materially in 2026. The homepage fetched on September 2 says Continue has been acquired by Cursor and that its open-source codebase remains available as a foundation for others. The old pricing route returned a GitHub Pages 404. Builders should therefore evaluate Continue as open-source software with an uncertain service lifecycle, not assume the former hosted product or paid plans remain purchasable.
Before the acquisition, Continue provided chat, autocomplete, inline editing, agent workflows, reusable rules, documentation context, model choice, and Model Context Protocol tools in VS Code and JetBrains. Its Apache 2.0 codebase appealed to platform teams that wanted to select OpenAI, Anthropic, Gemini, or local models instead of accepting one vendor's fixed stack. Teams could assign a low-latency model to completion, a stronger model to chat, and local inference through Ollama or LM Studio when source code could not leave a controlled environment. Those architectural options remain useful, but users must verify which repositories, extensions, releases, and cloud dependencies Cursor will maintain.
There is no defensible current subscription price to publish. Repository evidence previously listed Starter at $3 per million tokens, Team at $20 per seat monthly, and Company on custom terms, but those are historical figures because the live pricing page is gone. A self-managed deployment still incurs model API charges, local GPU capacity, embeddings, security review, extension maintenance, and staff time.
The main advantage is control: teams can inspect the implementation, customize prompts and tools, and avoid one model provider. The main risk is lifecycle uncertainty after an acquisition. Compare maintained options including Aider, Cursor, GitHub Copilot Agents, and Windsurf; the AI coding agents comparison covers broader selection criteria.
Existing users should inventory dependencies on Continue-hosted services, pin a known-good extension, mirror required repositories, test provider authentication, and document an exit path. New pilots should use a disposable repository and confirm that completion, indexing, rules, and MCP connections work without retired services. Run 20 representative tasks and measure success, latency, model cost, false edits, and maintenance time. Continue remains relevant open-source code, but the acquisition makes manual verification essential before an organization standardizes it. Security review should include extension permissions, telemetry, data retention, model-provider terms, dependency provenance, and the handling of repository secrets. Platform owners should also assign responsibility for patching the fork and define how quickly developers can migrate if editor compatibility or hosted endpoints stop working. Those operational details determine whether the apparent flexibility is sustainable.
Procurement should not copy historical hosted-plan prices into a 2026 budget. Confirm extension availability, release ownership, security-patch responsibility, license obligations, and remaining hosted dependencies from maintained repositories and the acquisition notice. A fork may avoid subscription fees while adding engineering and incident-response costs.
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Continue is an open-source AI developer tool focused on configurable coding assistance and AI quality checks, not just autocomplete. The homepage fetched during this run described “quality control for your software factory” and “source-controlled AI checks on every pull request.” The pricing page exposed concrete current signals: Starter at $3 per million input/output tokens, Team at $20 per seat per month with $10 in credits per seat, and Company on custom pricing. Those numbers are useful for evaluation, though buyers should still verify current limits, annual terms, and model-credit details before purchase. Continue's strongest differentiator is control. /tools/copilot is often easier for GitHub-heavy teams that want a default assistant. /tools/cursor-agent is stronger when the team wants a full AI-native editor. /tools/aider is better for terminal-first pair programming. Continue is most compelling when engineering leaders want source-controlled rules, repeatable AI checks, and custom agents that match the team's standards. The fetched pricing page also listed integrations such as Slack, Sentry, and Snyk, which points toward a quality workflow rather than a generic chatbot. The tool works best when the team can define what “good” means. Weak prompts such as “review this PR” will create noisy comments. Better checks are concrete: confirm migrations include rollback notes, flag API changes without documentation, look for unsafe user input handling, verify logging in background jobs, or check that a bug fix includes a regression test. By storing standards in source control, teams can make those expectations visible, reviewed, and repeatable. The main risk is false confidence. AI checks can find real problems, but they can also miss obvious defects or comment on harmless code. Continue should sit beside unit tests, integration tests, static analysis, security scanning, and senior review, not replace them. For a pilot, choose 10 recent pull requests, define 5 standards, run Continue, and measure true positives, false positives, reviewer minutes saved, developer complaints, and defects caught before merge. Expand only if the signal-to-noise ratio is strong enough that reviewers trust the output. Operational rollout should begin with advisory checks, not blocking checks. Let reviewers see Continue's comments for two weeks, mark each as useful, noisy, or wrong, and only promote high-signal rules to required gates. Assign one owner for standards maintenance so old checks do not punish new architecture. If a rule triggers on more than 20-30% false positives, rewrite it before expanding usage; developer trust is the scarce resource. A good final review asks whether each check catches a real historical failure, has an accountable owner, and produces action a developer can take in less than five minutes.
Teams can define review standards in code so AI checks become repeatable instead of ad hoc reviewer comments.
Continue supports agents aimed at software quality workflows, including checks tied to integrations and team rules.
The open-source posture is useful for teams that want more configurability than closed coding assistants usually provide.
The Team tier adds centralized controls, private shared agents, and usage credits for managed adoption.
Company pricing is relevant for SAML/OIDC SSO, BYOK, invoicing, commitments, and SLA requirements.
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