Ami vs Cursor 2.0: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Ami and Cursor 2.0 — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Ami
AiSDR
AI GTM agent that picks the audience, writes and launches outbound campaigns, reads the results and fixes what stops working.
Key features
- Autonomous Campaign Loop: Ami picks the target audience, builds and launches the campaign, reads what comes back and changes what is not working, so each campaign sharpens the next without a human restarting the cycle.
- Baked-In GTM Experience: Arrives with 27 industry playbooks and the lessons of 17,150 prior AiSDR campaigns and 19,501 meetings, so a first campaign launches with patterns other teams paid to learn.
- Signal-Triggered Outreach: Watches hiring, funding and job-change signals and acts at the moment they happen rather than months later.
- Performance Triage: When response rates slip, Ami digs into audience, message and sequence to pinpoint what is breaking and proposes fixes before the budget is spent — flagging, for example, a positive response rate under 1% after 21+ days.
- Omnichannel Sequences: Configurable sequences combining email via Gmail or Outlook, LinkedIn connection requests, DMs and InMail, and AI call steps through the Aircall dialer with scripts and automated follow-ups.
- Deep Per-Lead Personalization: Researches the top three most relevant data points per lead and personalizes from ICP data, activity, LinkedIn data and HubSpot properties.
- Native CRM Sync: Two-way HubSpot sync on every plan and two-way Salesforce sync on higher tiers, with AI research and monitoring running over that CRM data.
- Review Mode: Campaigns and Ami's proposed corrections stay drafts until approved, so the agent's autonomy is opt-in rather than assumed.
Best for
- Founder-Led Outbound: A solo founder builds pipeline without hiring an SDR, starting self-serve with no sales call required.
- Rescuing Stalled Campaigns: A revenue team catches a dying sequence early when Ami flags a collapsing positive-response rate and rewrites the audience or message.
- Replacing Outbound Agencies: A company that has paid outside firms without results brings the motion in-house under one agent.
- Warm-Signal Prospecting: A sales team reaches buyers right after a funding round, a relevant hire or a job change instead of cold-listing an industry.
- CRM-Grounded Targeting: A HubSpot or Salesforce team has outreach built from and logged back into existing CRM data rather than a disconnected tool.
- Multichannel Follow-Up: A team runs email, LinkedIn and dialer touches in a single sequence with replies handled in 5-10 minutes or in co-pilot mode.
Cursor 2.0
Cursor
An AI-first code editor with an agent-focused interface and Composer coding model for fast, multi-agent programming workflows.
Key features
- Agent-Focused Interface: A redesigned UI built to orchestrate one or more autonomous agents directly inside the editor, enabling tasks like multi-step code generation, automated refactors, and background analysis.
- Composer Coding Model: A purpose-built coding model (Composer) for Cursor that produces context-aware code completions, transformations, and multi-file edits optimized for agent workflows.
- Background Agents & Reliability Improvements: Persistent background agents that monitor workspaces, run long-running tasks, and surface results without blocking the developer, with enhancements for agent stability and performance.
- Workspace Indexing & PR Search: Built-in indexing of repositories and pull requests that enables fast semantic search, PR-aware generation, and code navigation tailored to large codebases.
- Internal Browser & Preview: An embedded browser environment for rendering and testing outputs, previews, and external resources without leaving the editor.
- Marketplace / MCP Integration: Support for a marketplace and configurable upstream providers to install extensions, rules, and integrations that extend agent behaviors and project-specific tooling.
- Customizable Rules (.cursorrules): Support for configuration files and rule sets to constrain generation style and enforce team standards across agent outputs and automated edits.
- Cross-Platform Downloads & Versioning: Official downloadable clients for Windows, macOS, and Linux with regular version releases and changelogs for updating the editor and agent capabilities.
- Redesigned editor interface optimized for AI-driven coding workflows
- Composer: visual/structural tool to build and orchestrate multiple agents
- First purpose-built coding model tuned for working with agents and code generation
- Support for background agents and long-running agent tasks
- .cursorrules support to define custom generation rules and behaviors
- Cross-platform desktop distribution: Windows, macOS, Linux installers
- Repository, release notes, and community resources hosted on GitHub (cursor/cursor)
- Integration points referenced: Remote SSH support, MCP/marketplace provider options, deeplink/PR indexing features
Best for
- AI Pair Programming: Use Composer and in-editor agents to generate complex functions, write unit tests, and iteratively refine code while maintaining context across multiple files.
- Automated Codebase Refactoring: Configure background agents to scan a repository, propose large-scale refactors, and apply multi-file edits while preserving PR history and links.
- Semantic PR Search & Review Automation: Use workspace indexing and PR-aware search to locate related code, auto-generate review suggestions, and prepare patch candidates for reviewers.
- Onboarding & Knowledge Capture: Install marketplace extensions to surface team conventions and project-specific rules so agents produce code consistent with company standards during developer onboarding.
- Interactive Debugging & Previewing: Leverage the internal browser to reproduce issues, test UI changes, and validate generated outputs without leaving the editor environment.
- Custom Tooling & Extensions: Extend Cursor via MCP or custom rules to integrate linters, CI links, or proprietary knowledge bases so agents can use internal resources when generating code.
- Interactive code generation and assistant-driven pair programming inside a desktop editor
- Composing and orchestrating multiple specialized agents to automate coding tasks
- Automated background code tasks such as PR indexing, search, and repository analysis
- Customizing generation behavior through rules files to enforce team or project conventions
- Using Remote SSH to work with remote development environments while leveraging agents