Choose Augment Code when
Choose Augment Code when teams working across large monorepos and complex production codebases.
Augment Code vs Codex
Which AI coding workflow fits you?
Reviewed Jul 12, 2026
47-field comparison matrix
| Dimension |  Augment CodeEnterprise coding-agent and context platformView full Augment Code analysis |  CodexCross-surface coding agent and multi-agent command centerView full Codex analysis | Official evidence |
|---|---|---|---|
| Product formDetermines whether adoption replaces the primary editor or layers an agent across existing tools. | Enterprise coding-agent and context platform | Cross-surface coding agent and multi-agent command center | Augment Code product overview OpenAI Codex product overview |
| Primary surfacesShows how much of the team's current development environment can remain in place. | Visual Studio Code extension · JetBrains IDE extension · Vim and Neovim plugin · Auggie terminal CLI · GitHub Code Review · Context Engine MCP and SDK · Cosmos cloud automation | ChatGPT desktop app in Codex mode · Codex IDE extension · Codex CLI · Codex web · Codex cloud · GitHub | Augment Code product overview OpenAI Codex product overview |
| Execution environmentsAffects machine usage, task isolation, and the ability to continue long-running work away from a laptop. | Local IDE workspace · Local terminal workspace · Augment-managed cloud services · Cosmos sandboxed compute · Enterprise VPC, single-tenant, or on-premises deployment | Local workspace · Git worktree · OpenAI-managed cloud container | Augment Code product overview OpenAI Codex product overview |
| Model strategyInfluences model choice, vendor concentration, and how usage costs vary by task. | What Augment Code is — Model strategy: Augment Code is a team-oriented AI software-engineering platform built around a semantic Context Engine. It supplies context and agents through IDEs, terminal, GitHub review, APIs, MCP, and cloud automation.Confirmed | What Codex is — Model strategy: Codex is a coding agent connected by a ChatGPT account across the desktop app, IDE, terminal, web, and cloud. The desktop experience is designed as a command center for supervising multiple agents and long-running work.Confirmed | Augment Code product overview OpenAI Codex product overview |
| Free starting pointSets the cost of running a real proof of concept before committing a team. | $0/plan — No free plan listed. No ongoing free plan is listed; trials and beta access may be offered separately | $0/plan — Free entry available. Codex is included across ChatGPT plans, including Free and Go, with limits that vary by plan | Augment Code current pricing Using Codex with your ChatGPT plan |
| Best-fit buying archetypeTurns the feature inventory into an explicit initial choice while keeping editorial judgment separate from product facts. | What Augment Code is — Best-fit buying archetype: Augment Code is a team-oriented AI software-engineering platform built around a semantic Context Engine. It supplies context and agents through IDEs, terminal, GitHub review, APIs, MCP, and cloud automation.Confirmed | What Codex is — Best-fit buying archetype: Codex is a coding agent connected by a ChatGPT account across the desktop app, IDE, terminal, web, and cloud. The desktop experience is designed as a command center for supervising multiple agents and long-running work.Confirmed | Augment Code product overview OpenAI Codex product overview |
| Native full editorDetermines migration effort and whether developers can consolidate manual coding and agent work into one editor. | Editor and coding workflow — Native full editor: IDE users work through Agent, Chat, and context-aware suggestions. Agent creates a plan, edits files, executes commands, uses MCP integrations, exposes every action, and saves checkpoints for rollback. Auggie provides a similar workflow in a terminal UI.Confirmed | Coding workflow — Native full editor: Codex can inspect repositories, edit files, execute commands and tests, review diffs, and complete substantial engineering tasks. Local work is available through the app, IDE extension, and CLI, while cloud tasks can continue independently.Confirmed | Using Augment Agent OpenAI Codex product overview |
| Dedicated inline completionInline completion affects hundreds of small daily edits that do not warrant delegating a full agent task. | Editor and coding workflow — Dedicated inline completion: IDE users work through Agent, Chat, and context-aware suggestions. Agent creates a plan, edits files, executes commands, uses MCP integrations, exposes every action, and saves checkpoints for rollback. Auggie provides a similar workflow in a terminal UI.Confirmed | Coding workflow — Dedicated inline completion: Codex can inspect repositories, edit files, execute commands and tests, review diffs, and complete substantial engineering tasks. Local work is available through the app, IDE extension, and CLI, while cloud tasks can continue independently.Confirmed | Using Augment Agent OpenAI Codex product overview |
| End-to-end agent changesShows whether the product can move beyond suggestions to implementation, testing, and revision. | Editor and coding workflow — End-to-end agent changes: IDE users work through Agent, Chat, and context-aware suggestions. Agent creates a plan, edits files, executes commands, uses MCP integrations, exposes every action, and saves checkpoints for rollback. Auggie provides a similar workflow in a terminal UI.Confirmed | Coding workflow — End-to-end agent changes: Codex can inspect repositories, edit files, execute commands and tests, review diffs, and complete substantial engineering tasks. Local work is available through the app, IDE extension, and CLI, while cloud tasks can continue independently.Confirmed | Using Augment Agent OpenAI Codex product overview |
| Codebase context approachContext retrieval quality becomes more important as repository size and architectural complexity grow. | Editor and coding workflow — Codebase context approach: IDE users work through Agent, Chat, and context-aware suggestions. Agent creates a plan, edits files, executes commands, uses MCP integrations, exposes every action, and saves checkpoints for rollback. Auggie provides a similar workflow in a terminal UI.Confirmed | Coding workflow — Codebase context approach: Codex can inspect repositories, edit files, execute commands and tests, review diffs, and complete substantial engineering tasks. Local work is available through the app, IDE extension, and CLI, while cloud tasks can continue independently.Confirmed | Using Augment Agent OpenAI Codex product overview |
| Visual and frontend workflowVisual input and browser verification reduce the gap between generated frontend code and the intended design. | Editor and coding workflow — Visual and frontend workflow: IDE users work through Agent, Chat, and context-aware suggestions. Agent creates a plan, edits files, executes commands, uses MCP integrations, exposes every action, and saves checkpoints for rollback. Auggie provides a similar workflow in a terminal UI.Confirmed | Coding workflow — Visual and frontend workflow: Codex can inspect repositories, edit files, execute commands and tests, review diffs, and complete substantial engineering tasks. Local work is available through the app, IDE extension, and CLI, while cloud tasks can continue independently.Confirmed | Using Augment Agent OpenAI Codex product overview |
| Terminal, build, and test executionThe implementation loop is incomplete if the agent cannot run the repository's own verification commands. | Editor and coding workflow — Terminal, build, and test execution: IDE users work through Agent, Chat, and context-aware suggestions. Agent creates a plan, edits files, executes commands, uses MCP integrations, exposes every action, and saves checkpoints for rollback. Auggie provides a similar workflow in a terminal UI.Confirmed | Coding workflow — Terminal, build, and test execution: Codex can inspect repositories, edit files, execute commands and tests, review diffs, and complete substantial engineering tasks. Local work is available through the app, IDE extension, and CLI, while cloud tasks can continue independently.Confirmed | Using Augment Agent OpenAI Codex product overview |
| Debugging workflowShows whether the product can diagnose runtime behavior rather than only rewrite code from static context. | Editor and coding workflow — Debugging workflow: IDE users work through Agent, Chat, and context-aware suggestions. Agent creates a plan, edits files, executes commands, uses MCP integrations, exposes every action, and saves checkpoints for rollback. Auggie provides a similar workflow in a terminal UI.Confirmed | Coding workflow — Debugging workflow: Codex can inspect repositories, edit files, execute commands and tests, review diffs, and complete substantial engineering tasks. Local work is available through the app, IDE extension, and CLI, while cloud tasks can continue independently.Confirmed | Using Augment Agent OpenAI Codex product overview |
| Project instructions and rulesPersistent instructions reduce repeated prompting and encode architecture, testing, and review requirements. | Context and extensibility — Project instructions and rules: The Context Engine is Augment's central differentiator. Context Services expose semantic search through MCP, TypeScript and Python SDKs, HTTP, CLI, and open-source connectors for repositories, documentation, schemas, runbooks, and local files.Confirmed | Context and extensibility — Project instructions and rules: Codex uses AGENTS.md, configuration, rules, skills, plugins, MCP, hooks, and programmable interfaces to align tasks with project and team requirements.Confirmed | Augment Context Services overview Introducing the Codex app |
| Reusable skillsSkills turn specialized repeatable work into a maintained capability instead of a copied prompt. | Context and extensibility — Reusable skills: The Context Engine is Augment's central differentiator. Context Services expose semantic search through MCP, TypeScript and Python SDKs, HTTP, CLI, and open-source connectors for repositories, documentation, schemas, runbooks, and local files.Confirmed | Context and extensibility — Reusable skills: Codex uses AGENTS.md, configuration, rules, skills, plugins, MCP, hooks, and programmable interfaces to align tasks with project and team requirements.Confirmed | Augment Context Services overview Introducing the Codex app |
| MCP supportMCP enables reusable connections to external tools, services, and private operational context. | Context and extensibility — MCP support: The Context Engine is Augment's central differentiator. Context Services expose semantic search through MCP, TypeScript and Python SDKs, HTTP, CLI, and open-source connectors for repositories, documentation, schemas, runbooks, and local files.Confirmed | Context and extensibility — MCP support: Codex uses AGENTS.md, configuration, rules, skills, plugins, MCP, hooks, and programmable interfaces to align tasks with project and team requirements.Confirmed | Augment Context Services overview Introducing the Codex app |
| Plugins and marketplaceA managed extension ecosystem affects discovery, reuse, permissions, and supply-chain governance. | Context and extensibility — Plugins and marketplace: The Context Engine is Augment's central differentiator. Context Services expose semantic search through MCP, TypeScript and Python SDKs, HTTP, CLI, and open-source connectors for repositories, documentation, schemas, runbooks, and local files.Confirmed | Context and extensibility — Plugins and marketplace: Codex uses AGENTS.md, configuration, rules, skills, plugins, MCP, hooks, and programmable interfaces to align tasks with project and team requirements.Confirmed | Augment Context Services overview Introducing the Codex app |
| HooksHooks allow deterministic checks and integrations around otherwise probabilistic agent behavior. | Context and extensibility — Hooks: The Context Engine is Augment's central differentiator. Context Services expose semantic search through MCP, TypeScript and Python SDKs, HTTP, CLI, and open-source connectors for repositories, documentation, schemas, runbooks, and local files.Confirmed | Context and extensibility — Hooks: Codex uses AGENTS.md, configuration, rules, skills, plugins, MCP, hooks, and programmable interfaces to align tasks with project and team requirements.Confirmed | Augment Context Services overview Introducing the Codex app |
| SDK and non-interactive executionProgrammable entry points determine whether the agent can be embedded in CI, scripts, and internal tooling. | Context and extensibility — SDK and non-interactive execution: The Context Engine is Augment's central differentiator. Context Services expose semantic search through MCP, TypeScript and Python SDKs, HTTP, CLI, and open-source connectors for repositories, documentation, schemas, runbooks, and local files.Confirmed | Context and extensibility — SDK and non-interactive execution: Codex uses AGENTS.md, configuration, rules, skills, plugins, MCP, hooks, and programmable interfaces to align tasks with project and team requirements.Confirmed | Augment Context Services overview Introducing the Codex app |
| Team capability distributionCentral distribution prevents every developer from maintaining incompatible private agent setups. | Context and extensibility — Team capability distribution: The Context Engine is Augment's central differentiator. Context Services expose semantic search through MCP, TypeScript and Python SDKs, HTTP, CLI, and open-source connectors for repositories, documentation, schemas, runbooks, and local files.Confirmed | Context and extensibility — Team capability distribution: Codex uses AGENTS.md, configuration, rules, skills, plugins, MCP, hooks, and programmable interfaces to align tasks with project and team requirements.Confirmed | Augment Context Services overview Introducing the Codex app |
| Parallel agentsParallelism is the main throughput advantage of moving from pair programming to agent orchestration. | Agents and orchestration — Parallel agents: Augment supports configurable CLI subagents and Cosmos automation. Subagents receive independent context windows and specialized prompts, while Cosmos provides concurrent remote sessions and enterprise-scale workflow execution.Confirmed | Agents and orchestration — Parallel agents: Codex supports multiple agents running in parallel across projects. Built-in worktrees isolate local tasks, cloud environments isolate delegated work, and scheduled Automations handle recurring jobs.Confirmed | Auggie subagents OpenAI Codex product overview |
| Git worktree isolationWorktrees let multiple agents change one repository without colliding in the same checkout. | Agents and orchestration — Git worktree isolation: Augment supports configurable CLI subagents and Cosmos automation. Subagents receive independent context windows and specialized prompts, while Cosmos provides concurrent remote sessions and enterprise-scale workflow execution.Confirmed | Agents and orchestration — Git worktree isolation: Codex supports multiple agents running in parallel across projects. Built-in worktrees isolate local tasks, cloud environments isolate delegated work, and scheduled Automations handle recurring jobs.Confirmed | Auggie subagents OpenAI Codex product overview |
| Cloud agentsCloud execution keeps long tasks running without tying them to developer hardware. | Agents and orchestration — Cloud agents: Augment supports configurable CLI subagents and Cosmos automation. Subagents receive independent context windows and specialized prompts, while Cosmos provides concurrent remote sessions and enterprise-scale workflow execution.Confirmed | Agents and orchestration — Cloud agents: Codex supports multiple agents running in parallel across projects. Built-in worktrees isolate local tasks, cloud environments isolate delegated work, and scheduled Automations handle recurring jobs.Confirmed | Auggie subagents OpenAI Codex product overview |
| SubagentsSubagents allow a primary task to delegate exploration and implementation without blocking the parent workflow. | Agents and orchestration — Subagents: Augment supports configurable CLI subagents and Cosmos automation. Subagents receive independent context windows and specialized prompts, while Cosmos provides concurrent remote sessions and enterprise-scale workflow execution.Confirmed | Agents and orchestration — Subagents: Codex supports multiple agents running in parallel across projects. Built-in worktrees isolate local tasks, cloud environments isolate delegated work, and scheduled Automations handle recurring jobs.Confirmed | Auggie subagents OpenAI Codex product overview |
| Local and cloud continuityContinuity determines whether developers can delegate, resume, and locally verify work without recreating context. | Agents and orchestration — Local and cloud continuity: Augment supports configurable CLI subagents and Cosmos automation. Subagents receive independent context windows and specialized prompts, while Cosmos provides concurrent remote sessions and enterprise-scale workflow execution.Confirmed | Agents and orchestration — Local and cloud continuity: Codex supports multiple agents running in parallel across projects. Built-in worktrees isolate local tasks, cloud environments isolate delegated work, and scheduled Automations handle recurring jobs.Confirmed | Auggie subagents OpenAI Codex product overview |
| Scheduled automationsScheduled background work is essential for recurring triage, monitoring, and maintenance rather than one-off coding. | Agents and orchestration — Scheduled automations: Augment supports configurable CLI subagents and Cosmos automation. Subagents receive independent context windows and specialized prompts, while Cosmos provides concurrent remote sessions and enterprise-scale workflow execution.Confirmed | Agents and orchestration — Scheduled automations: Codex supports multiple agents running in parallel across projects. Built-in worktrees isolate local tasks, cloud environments isolate delegated work, and scheduled Automations handle recurring jobs.Confirmed | Auggie subagents OpenAI Codex product overview |
| Mobile and remote supervisionRemote supervision matters when agents run longer than a normal editor session or need approval away from a desk. | Agents and orchestration — Mobile and remote supervision: Augment supports configurable CLI subagents and Cosmos automation. Subagents receive independent context windows and specialized prompts, while Cosmos provides concurrent remote sessions and enterprise-scale workflow execution.Confirmed | Agents and orchestration — Mobile and remote supervision: Codex supports multiple agents running in parallel across projects. Built-in worktrees isolate local tasks, cloud environments isolate delegated work, and scheduled Automations handle recurring jobs.Confirmed | Auggie subagents OpenAI Codex product overview |
| Diff review and feedbackA strong review surface keeps humans in control without forcing them to inspect an agent's entire execution log. | Delivery and code review — Diff review and feedback: Agent can start branches and pull requests from GitHub issues or connected tools, while Augment Code Review evaluates GitHub pull requests on every paid plan. Enterprise adds analytics, allowlists, multi-org support, and richer MCP context.Confirmed | Delivery and code review — Diff review and feedback: Codex connects implementation with diff review, testing, GitHub, pull requests, and dedicated code review. Local and cloud output remains reviewable before it is merged.Confirmed | Using Augment Agent OpenAI Codex product overview |
| Git isolation and rollbackRollback and isolation reduce the cost of trying alternative implementations or recovering from a poor edit. | Delivery and code review — Git isolation and rollback: Agent can start branches and pull requests from GitHub issues or connected tools, while Augment Code Review evaluates GitHub pull requests on every paid plan. Enterprise adds analytics, allowlists, multi-org support, and richer MCP context.Confirmed | Delivery and code review — Git isolation and rollback: Codex connects implementation with diff review, testing, GitHub, pull requests, and dedicated code review. Local and cloud output remains reviewable before it is merged.Confirmed | Using Augment Agent OpenAI Codex product overview |
| Pull-request workflowPull-request integration determines how easily delegated work enters a normal engineering review process. | Delivery and code review — Pull-request workflow: Agent can start branches and pull requests from GitHub issues or connected tools, while Augment Code Review evaluates GitHub pull requests on every paid plan. Enterprise adds analytics, allowlists, multi-org support, and richer MCP context.Confirmed | Delivery and code review — Pull-request workflow: Codex connects implementation with diff review, testing, GitHub, pull requests, and dedicated code review. Local and cloud output remains reviewable before it is merged.Confirmed | Using Augment Agent OpenAI Codex product overview |
| Dedicated code reviewDedicated review can find defects independently of the agent that authored the change, but packaging and cost differ. | Delivery and code review — Dedicated code review: Agent can start branches and pull requests from GitHub issues or connected tools, while Augment Code Review evaluates GitHub pull requests on every paid plan. Enterprise adds analytics, allowlists, multi-org support, and richer MCP context.Confirmed | Delivery and code review — Dedicated code review: Codex connects implementation with diff review, testing, GitHub, pull requests, and dedicated code review. Local and cloud output remains reviewable before it is merged.Confirmed | Using Augment Agent OpenAI Codex product overview |
| CI and PR follow-throughFollow-through reduces the manual loop of watching checks, reading failures, patching, and waiting again. | Delivery and code review — CI and PR follow-through: Agent can start branches and pull requests from GitHub issues or connected tools, while Augment Code Review evaluates GitHub pull requests on every paid plan. Enterprise adds analytics, allowlists, multi-org support, and richer MCP context.Confirmed | Delivery and code review — CI and PR follow-through: Codex connects implementation with diff review, testing, GitHub, pull requests, and dedicated code review. Local and cloud output remains reviewable before it is merged.Confirmed | Using Augment Agent OpenAI Codex product overview |
| Verification artifactsArtifacts make it possible to judge whether an agent actually tested and inspected its work. | Delivery and code review — Verification artifacts: Agent can start branches and pull requests from GitHub issues or connected tools, while Augment Code Review evaluates GitHub pull requests on every paid plan. Enterprise adds analytics, allowlists, multi-org support, and richer MCP context.Confirmed | Delivery and code review — Verification artifacts: Codex connects implementation with diff review, testing, GitHub, pull requests, and dedicated code review. Local and cloud output remains reviewable before it is merged.Confirmed | Using Augment Agent OpenAI Codex product overview |
| Free planA free plan supports a real repository trial before procurement or team rollout. | $0/plan — No free plan listed. No ongoing free plan is listed; trials and beta access may be offered separately | $0/plan — Free entry available. Codex is included across ChatGPT plans, including Free and Go, with limits that vary by plan | Augment Code current pricing Using Codex with your ChatGPT plan |
| Personal paid entryShows the recurring commitment required for sustained personal use without conflating a client subscription with a platform bundle. | $null/plan-dependent — See official pricing. No standalone individual price is listed in the reviewed pricing material. Business includes $100 in pooled monthly usage across LLM inference, Context Engine, and compute for up to 50 seats. LLM inference uses provider public API list price plus a 40% service fee; compute has no service fee. Additional usage is pay as you go. Enterprise is custom. | $null/plan-dependent — See official pricing. No standalone individual price is listed in the reviewed pricing material. Codex usage draws from the plan's agentic usage and credit pool. The current rate card primarily maps model input, cached-input, and output tokens to credits. | Augment Code current pricing Using Codex with your ChatGPT plan |
| Primary usage unitThe billing unit determines whether cost forecasting follows requests, model list prices, tokens, or credits. | Pricing and usage — Primary usage unit: Augment's current public pricing lists Business at a flat $100 per month for up to 50 seats, including $100 in pooled monthly usage. Enterprise is custom. This supersedes older public Indie, Standard, and Max pricing.Confirmed | Pricing and usage — Primary usage unit: Codex is included through ChatGPT plans rather than sold only as one standalone client subscription. Limits vary by plan, and additional use is managed through a credits system whose cost depends on model and token mix.Confirmed | Augment Code current pricing Using Codex with your ChatGPT plan |
| Overage behaviorOverage rules determine whether work stops at a limit or continues with a variable bill. | Pricing and usage — Overage behavior: Augment's current public pricing lists Business at a flat $100 per month for up to 50 seats, including $100 in pooled monthly usage. Enterprise is custom. This supersedes older public Indie, Standard, and Max pricing.Confirmed | Pricing and usage — Overage behavior: Codex is included through ChatGPT plans rather than sold only as one standalone client subscription. Limits vary by plan, and additional use is managed through a credits system whose cost depends on model and token mix.Confirmed | Augment Code current pricing Using Codex with your ChatGPT plan |
| Team entry planTeam pricing determines the baseline before variable model use, review, and enterprise controls. | $100/month — Business flat monthly price for up to 50 seats. Business flat monthly price for up to 50 seats. Business includes $100 in pooled monthly usage across LLM inference, Context Engine, and compute for up to 50 seats. LLM inference uses provider public API list price plus a 40% service fee; compute has no service fee. Additional usage is pay as you go. Enterprise is custom. | $null/plan-dependent — See official pricing. No standalone team price is listed in the reviewed pricing material. Codex usage draws from the plan's agentic usage and credit pool. The current rate card primarily maps model input, cached-input, and output tokens to credits. | Augment Code current pricing Using Codex with your ChatGPT plan |
| Code-review billingReview volume can become a material cost separate from interactive coding. | Pricing and usage — Code-review billing: Augment's current public pricing lists Business at a flat $100 per month for up to 50 seats, including $100 in pooled monthly usage. Enterprise is custom. This supersedes older public Indie, Standard, and Max pricing.Confirmed | Pricing and usage — Code-review billing: Codex is included through ChatGPT plans rather than sold only as one standalone client subscription. Limits vary by plan, and additional use is managed through a credits system whose cost depends on model and token mix.Confirmed | Augment Code current pricing Using Codex with your ChatGPT plan |
| Enterprise billing and usage administrationLarge teams need spend visibility, procurement support, allocation controls, and auditability in addition to feature access. | Pricing and usage — Enterprise billing and usage administration: Augment's current public pricing lists Business at a flat $100 per month for up to 50 seats, including $100 in pooled monthly usage. Enterprise is custom. This supersedes older public Indie, Standard, and Max pricing.Confirmed | Pricing and usage — Enterprise billing and usage administration: Codex is included through ChatGPT plans rather than sold only as one standalone client subscription. Limits vary by plan, and additional use is managed through a credits system whose cost depends on model and token mix.Confirmed | Augment Code current pricing Using Codex with your ChatGPT plan |
| Training-data policySource code and prompts may contain proprietary logic, credentials, or regulated data. | Security and governance — Training-data policy: Augment says paid-plan customer data is never used for AI training and documents SOC 2 Type II. Enterprise offerings add customer-managed encryption, ISO/IEC 42001 controls, SSO and SCIM, data residency, granular access, audit trails, SIEM, and private deployment options.Confirmed | Security and governance — Training-data policy: Codex combines OS-enforced local sandboxing, configurable approvals, default network restrictions, isolated cloud containers, and ChatGPT workspace controls. Data-training defaults depend on whether the user is on a consumer or business plan.Confirmed | Augment Code security and privacy Codex agent approvals and security |
| Local sandboxLocal sandboxing limits the damage of a mistaken or manipulated command on a developer machine. | Security and governance — Local sandbox: Augment says paid-plan customer data is never used for AI training and documents SOC 2 Type II. Enterprise offerings add customer-managed encryption, ISO/IEC 42001 controls, SSO and SCIM, data residency, granular access, audit trails, SIEM, and private deployment options.Confirmed | Security and governance — Local sandbox: Codex combines OS-enforced local sandboxing, configurable approvals, default network restrictions, isolated cloud containers, and ChatGPT workspace controls. Data-training defaults depend on whether the user is on a consumer or business plan.Confirmed | Augment Code security and privacy Codex agent approvals and security |
| Cloud execution isolationCloud isolation determines whether agent tasks can access host systems or unrelated organizational data. | Security and governance — Cloud execution isolation: Augment says paid-plan customer data is never used for AI training and documents SOC 2 Type II. Enterprise offerings add customer-managed encryption, ISO/IEC 42001 controls, SSO and SCIM, data residency, granular access, audit trails, SIEM, and private deployment options.Confirmed | Security and governance — Cloud execution isolation: Codex combines OS-enforced local sandboxing, configurable approvals, default network restrictions, isolated cloud containers, and ChatGPT workspace controls. Data-training defaults depend on whether the user is on a consumer or business plan.Confirmed | Augment Code security and privacy Codex agent approvals and security |
| Cloud-agent network defaultNetwork access enables dependency installation and research but increases prompt-injection and data-exfiltration risk. | Security and governance — Cloud-agent network default: Augment says paid-plan customer data is never used for AI training and documents SOC 2 Type II. Enterprise offerings add customer-managed encryption, ISO/IEC 42001 controls, SSO and SCIM, data residency, granular access, audit trails, SIEM, and private deployment options.Confirmed | Security and governance — Cloud-agent network default: Codex combines OS-enforced local sandboxing, configurable approvals, default network restrictions, isolated cloud containers, and ChatGPT workspace controls. Data-training defaults depend on whether the user is on a consumer or business plan.Confirmed | Augment Code security and privacy Codex agent approvals and security |
| Command approvalsApproval policy controls how often an agent can act autonomously versus requiring a human checkpoint. | Security and governance — Command approvals: Augment says paid-plan customer data is never used for AI training and documents SOC 2 Type II. Enterprise offerings add customer-managed encryption, ISO/IEC 42001 controls, SSO and SCIM, data residency, granular access, audit trails, SIEM, and private deployment options.Confirmed | Security and governance — Command approvals: Codex combines OS-enforced local sandboxing, configurable approvals, default network restrictions, isolated cloud containers, and ChatGPT workspace controls. Data-training defaults depend on whether the user is on a consumer or business plan.Confirmed | Augment Code security and privacy Codex agent approvals and security |
| SSO and SCIMIdentity federation and automated provisioning are required for reliable access removal and enterprise onboarding. | Security and governance — SSO and SCIM: Augment says paid-plan customer data is never used for AI training and documents SOC 2 Type II. Enterprise offerings add customer-managed encryption, ISO/IEC 42001 controls, SSO and SCIM, data residency, granular access, audit trails, SIEM, and private deployment options.Confirmed | Security and governance — SSO and SCIM: Codex combines OS-enforced local sandboxing, configurable approvals, default network restrictions, isolated cloud containers, and ChatGPT workspace controls. Data-training defaults depend on whether the user is on a consumer or business plan.Confirmed | Augment Code security and privacy Codex agent approvals and security |
| Audit and policy controlsPolicy controls let security teams constrain repositories, models, tools, networks, and access while preserving an audit trail. | Security and governance — Audit and policy controls: Augment says paid-plan customer data is never used for AI training and documents SOC 2 Type II. Enterprise offerings add customer-managed encryption, ISO/IEC 42001 controls, SSO and SCIM, data residency, granular access, audit trails, SIEM, and private deployment options.Confirmed | Security and governance — Audit and policy controls: Codex combines OS-enforced local sandboxing, configurable approvals, default network restrictions, isolated cloud containers, and ChatGPT workspace controls. Data-training defaults depend on whether the user is on a consumer or business plan.Confirmed | Augment Code security and privacy Codex agent approvals and security |
“Not publicly confirmed” means the reviewed first-party sources did not establish the capability. It does not mean “unsupported.” No star ratings, review counts, or paid rankings are used.
Editorial bottom line
Choose the workflow that fits the team
Both products can be relevant to AI-assisted software work. Compare the product form, available execution surfaces, documented workflow, pricing model, and governance requirements against the same repository task.
Choose Codex when
Choose Codex when developers who want the same coding agent in ChatGPT, an IDE, and the terminal.
Evaluate both when
Run the same representative repository task and compare output quality, review effort, execution surface, and governance requirements.
Cost scenarios
Compare pricing by workload, not sticker price
Free evaluation
Start with the documented free entry points before comparing paid usage.
Augment Code: No ongoing free plan is listed; trials and beta access may be offered separately
Codex: Codex is included across ChatGPT plans, including Free and Go, with limits that vary by plan
Run the same scoped task in both products and compare review effort, output quality, and limits before standardizing.Daily individual use
Compare the published individual entry and the documented usage model.
Augment Code: See official pricing. Business includes $100 in pooled monthly usage across LLM inference, Context Engine, and compute for up to 50 seats. LLM inference uses provider public API list price plus a 40% service fee; compute has no service fee. Additional usage is pay as you go. Enterprise is custom.
Codex: See official pricing. Codex usage draws from the plan's agentic usage and credit pool. The current rate card primarily maps model input, cached-input, and output tokens to credits.
Use actual workload data rather than treating plans with different usage units as directly equivalent.Governed engineering team
Evaluate governance, billing, identity, and deployment requirements alongside the seat or usage model.
Augment Code: $100/month — Business flat monthly price for up to 50 seats. Business includes $100 in pooled monthly usage across LLM inference, Context Engine, and compute for up to 50 seats. LLM inference uses provider public API list price plus a 40% service fee; compute has no service fee. Additional usage is pay as you go. Enterprise is custom.
Codex: See official pricing. Codex usage draws from the plan's agentic usage and credit pool. The current rate card primarily maps model input, cached-input, and output tokens to credits.
Validate the exact plan and execution surface with both vendors before making a governance decision.Workflow fit
Recommendations by team scenario
Choose Augment Code for its documented primary workflow
Augment Code is positioned as Enterprise coding-agent and context platform. Its source-reviewed guide is the right place to validate its detailed workflow, tradeoffs, and operating model.
Choose Codex for its documented primary workflow
Codex is positioned as Cross-surface coding agent and multi-agent command center. Its source-reviewed guide is the right place to validate its detailed workflow, tradeoffs, and operating model.
Run a governed pilot before standardizing
Product form alone is not enough for a governance decision. Review the official security, privacy, pricing, and execution documentation for the exact plan and deployment model.
FAQ
Questions about Augment Code and Codex
What is the main difference between Augment Code and Codex?
Augment Code is positioned as Enterprise coding-agent and context platform. Codex is positioned as Cross-surface coding agent and multi-agent command center. The aligned matrix shows how those product forms map to surfaces, workflow, pricing, and governance evidence.
Does Augment Code or Codex have a free entry point?
Augment Code: No ongoing free plan is listed; trials and beta access may be offered separately Codex: Codex is included across ChatGPT plans, including Free and Go, with limits that vary by plan
How should a team evaluate Augment Code versus Codex?
Run the same representative repository task in both products, then compare the documented execution surface, review effort, output quality, usage limits, and required governance controls.
Which product is safer for private company code?
There is no universal answer. Review the official security, privacy, network, approval, identity, and audit documentation for the exact plan and execution surface your team will use.
Methodology and sources
Primary evidence behind the matrix
Each field is grounded in current first-party product, documentation, pricing, security, privacy, changelog, or help material.
- Augment Code product overview · verified 2026-07-12
- Augment Code quickstart · verified 2026-07-12
- Augment Context Services overview · verified 2026-07-12
- OpenAI Codex product overview · verified 2026-07-12
- Introducing the Codex app · verified 2026-07-12
- Using Codex with your ChatGPT plan · verified 2026-07-12
- Augment Code current pricing · verified 2026-07-12
- Codex rate card · verified 2026-07-12
- Using Augment Agent · verified 2026-07-12
- Auggie interactive CLI · verified 2026-07-12
- Introducing upgrades to Codex · verified 2026-07-12
- Codex documentation · verified 2026-07-12
- Auggie subagents · verified 2026-07-12
- Augment Code security and privacy · verified 2026-07-12
- Augment Code privacy policy · verified 2026-07-12
- Codex agent approvals and security · verified 2026-07-12
- OpenAI business data privacy · verified 2026-07-12
- SSO for ChatGPT Business · verified 2026-07-12
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