Best AI Software Factory Platforms 2026

Twenty engineering seats start at $380 a month with GitHub Copilot Business, $800 with Cursor Teams Standard, $880 with Devin Teams, or $1,000 with Warp Business before variable agent usage. Warp Factories is the best overall software factory platform for teams that want one configurable control plane across agents and models, but GitHub is the safer default when the repository is already the operating system.

The price range looks narrow until the meters begin moving. GitHub agent tasks also consume Actions minutes and AI Credits. Cursor Cloud Agents run at model API prices. Warp factory runs draw from plan credits and usage. Devin sells extra usage at API pricing. Factory quotes organization plans. Those are different economic units, so a cheap seat can still produce an expensive pull request.

The useful buying metric is cost per accepted pull request: platform, model, compute, and human review cost divided by pull requests that pass the normal release process. Agent launches, generated lines, and token volume are inputs. Accepted work is the output.

Pricing and availability were verified against each vendor's live pages on August 20, 2026. These platforms were compared through their current documentation, pricing, and product surfaces; they were not represented as hands-on tested.

The short answer

Warp Factories ranks first because it most directly matches the category. It treats the factory as configurable infrastructure: repositories, agents, models, permissions, checkpoints, triggers, compute, and measurement can live in one operating layer. It is also the least mature commercial choice here because Factories is in Early Access.

GitHub Copilot with Agent HQ ranks second and is the default for a GitHub-centered company. Issues, branches, pull requests, Actions, security checks, identity, and audit are already where the work lands. You give up some neutrality, but you remove an integration program.

Factory ranks third for organizations that need an autonomy ladder rather than a fleet dashboard alone. It spans supervised Droids, recurring Automations, persistent Droid Computers, and multi-agent Missions, then adds organization governance and analytics. The catch is commercial opacity: Business and Enterprise require a quote.

Cursor ranks fourth, but it is the one most likely to move up quickly. Origin now puts repositories, pull requests, code browsing, GitHub sync, and agents in one product, while Cloud Agents can subscribe to events, hold long-running goals, and fan work out to isolated subagents. Origin is still an early beta, and long-running work is not yet available in multi-repository environments.

Devin ranks fifth because it is strongest as a managed queue of scoped engineering delegations. Its parallel sessions, playbooks, schedules, API, and MCP are real factory ingredients. It is less suitable when the requirement is an open control plane across several agent harnesses.

Best AI software factory platforms at a glance

Those are subscription entry points, not total ownership costs. A team that buys Cursor Teams Standard at $40 a seat and then runs several frontier-model Cloud Agents every day can spend materially more than the $800 monthly floor for 20 seats. GitHub's $380 floor can acquire Actions and AI-credit costs. Warp's $1,000 Business floor contains $20 of agent usage per seat, but additional factory work still has a meter.

The first procurement question is therefore not “Which seat is cheapest?” It is “Which system turns our existing backlog into accepted work with the least new operating machinery?”

What counts as an AI software factory?

A coding assistant helps a person write code. A software factory moves a class of work through an operating loop.

Warp's useful definition is an automation around triage, specification, implementation, review, verification, shipping, and monitoring, with agents and humans moving work forward at each stage. That definition prevents the category from swallowing every editor autocomplete and chatbot. A factory can contain an interactive agent, but the interactive session is not the factory.

For this ranking, a platform had to clear four bars:

1. Execution away from one laptop. Work must run in a managed cloud or customer-controlled environment without keeping an engineer's machine awake.
2. Concurrent or repeated work. The platform must manage more than one task, session, or automation instead of merely answering one prompt at a time.
3. Reviewable repository output. The normal destination is a branch, pull request, test result, artifact, or other evidence that fits an existing release process.
4. An operating control. Cost, permissions, approvals, audit, policy, environment, or agent behavior must be visible and governable.

That cuts popular coding tools from this specific list when they stop at an editor. It also cuts general agent platforms that can technically call a Git provider but do not understand engineering environments, branches, tests, and pull-request review as first-class work.

The factory is not a promise of full autonomy. Warp says most organizations begin with roughly 20% to 30% of pull requests fully automated, starting with low-risk work. That is vendor guidance, not an independent benchmark, but the operating advice is sound: automate the repeatable slice and widen it only after the review burden stays controlled.

The broader AI agent platform market is useful when your workflow includes sales, support, finance, or research. The five products below are narrower. They are designed around software delivery, where a plausible answer is not enough and the last mile is a verified change.

1. Warp Factories: best overall

Warp Factories is the strongest fit for an engineering platform group that wants to define its own factory without building the orchestration plumbing from scratch. Its core object is configuration, not a single agent personality: repositories, agents, models, permissions, and checkpoints can be expressed together, while an API, CLI, SDK, and MCP expose the same operating layer.

The openness is the decisive difference. Warp says a factory can use Warp, Claude Code, Codex, Cursor, or another MCP-capable agent, pair different pipeline stages with frontier or open-weight models, and run on Warp's cloud or customer infrastructure. Enterprise adds self-hosted workers, the customer's own code forge, and BYOLLM inference that does not consume Warp credits.

That makes Warp more infrastructure-like than the other choices. A platform engineer can treat the factory definition as a versioned operating asset, change the model used for review without redesigning intake, or replace an agent harness without moving every workflow to a new dashboard. It is the best hedge against a market in which model quality, inference price, and preferred coding harness can change within a quarter.

Warp also closes an important review gap. It says factory agents can produce screenshots or video of computer-use verification before a pull request ships. The platform tracks factory insights such as cost per pull request and automation rate. That is closer to an engineering-leadership question than a leaderboard of prompts or tokens.

The wall is maturity. Factories is in Early Access, and the most flexible deployment features sit on Enterprise. Business is capped at 25 seats. An organization adopting Warp today is buying a strong architecture and accepting that parts of the commercial and operational surface are still being established.

Warp's own site reports 30%+ automation coverage for pull requests merged with zero edits, 200,000 agent runs a day across factories, and a 20% reduction in cost per pull request. Those are vendor-reported metrics, not independently audited benchmarks. Use them as questions for a reference call, not as values in your internal business case.

Warp pricing

Warp's live pricing has five tiers:

• Free: $0 per month. Factory work is pay as you go at a 20% markup over API rates.
• Build: $20 per month on monthly billing or $18 per month on annual billing. It includes 1,500 credits, described as $20 of agent usage at API rates, and additional factory usage at API rates.
• Max: $200 per month monthly or $180 per month annually. It includes 18,000 credits, 12 times Build's included usage.
• Business: $50 per user per month monthly or $45 per user per month annually, for up to 25 seats. Each seat includes 1,500 credits, described as $20 of usage, plus team metrics, data controls, custom inference options, and SAML SSO.
• Enterprise: custom. It adds unlimited seats, shared usage pools, self-hosted workers, the customer's own code forge, BYOLLM, and an implementation engineer.

Qualifying organizations can receive up to $10,000 in factory usage during Early Access. That is a selective launch credit, not a standing free trial, and it should not be used to hide the post-credit unit economics.

How to pilot Warp Factories without creating a platform project

1. 1

   Choose one low-risk work class

   Pick a repeatable queue such as dependency updates or flaky-test triage. Do not start with a broad “fix the backlog” objective, because inconsistent task shape makes cost and acceptance impossible to compare.

2. 2

   Write the control boundary first

   Define the repositories, allowed triggers, agent and model, environment, permissions, human checkpoint, and evidence required before merge. Treat the factory definition like production infrastructure and require review on changes to it.

3. 3

   Keep the release system unchanged

   Preserve branch protection, continuous integration, code ownership, security scans, and the normal human approval. The pilot should test the factory, not quietly lower the definition of accepted work.

4. 4

   Tag every run and its outcome

   Record the task class, agent and model combination, variable spend, reviewer minutes, rework, failed checks, and whether the pull request merged without material edits. A successful-looking run that never merges is a cost, not output.

5. 5

   Tune one layer at a time

   Change the prompt or skill, model, environment, or approval rule separately. If several layers change between batches, the factory can improve while the organization learns nothing repeatable.

Best for: Platform engineering teams that want a configurable, multi-harness factory layer.
Standout: Factories as code across repositories, agents, models, permissions, and checkpoints.
Pricing: Free PAYG; Build $20/month; Max $200/month; Business $50/user/month; Enterprise custom.
Free trial: No standing trial; qualifying Early Access organizations can receive up to $10,000 in usage.

Verdict: Pick Warp when portability and factory design are strategic. Skip it when the company needs a mature, generally available procurement default this quarter and already lives entirely inside GitHub.

2. GitHub Copilot and Agent HQ: best for GitHub-native teams

GitHub Copilot is the practical winner when GitHub already owns source, issues, pull requests, identity, branch rules, Actions, and security. Agent HQ extends those primitives into a mission-control model where Copilot, third-party agents, and custom agents can be assigned, steered, and tracked without creating a second code-operations system.

This is a distribution advantage, but it is also an operating advantage. Work can begin in GitHub Issues, Azure Boards, Jira, Raycast, Linear, an IDE, the CLI, Slack, or Microsoft Teams. The output returns to familiar pull-request controls. GitHub says Copilot-created work is checked with its secret, code, and supply-chain security tools before the pull request is finalized.

The enterprise control plane is now generally available. It adds agent-aware audit logs, session start, finish, and failure events, recent cloud-agent session activity, custom-agent standards, and policy administration. The candid exception is the enterprise-wide MCP allowlist, which remains in public preview.

The budget is easy to start and easy to misunderstand. Copilot agent tasks consume both GitHub Actions minutes and AI Credits. Business and Enterprise seats contribute credits to a shared pool, and additional credits cost $0.01 each. Code completions and next-edit suggestions are unlimited on paid plans and do not consume the pool, so editor activity and factory activity do not share the same cost behavior.

GitHub is not automatically best because it is already installed. It is best when avoiding a new integration and identity layer is worth anchoring the factory to GitHub's repository and compute primitives. If the company runs GitLab or Bitbucket as the system of record, GitHub becomes a migration or duplication decision before it becomes an agent decision.

GitHub Copilot pricing

GitHub's current plan pages list six tiers across individuals and organizations:

• Free: $0 with limited chat and agent usage.
• Pro: $10 per user per month, with cloud-agent and code-review access and $15 in total monthly AI Credits.
• Pro+: $39 per user per month, with premium models, audit logs, and $70 in total monthly AI Credits.
• Max: $100 per user per month, positioned for sustained high-volume agent work with $200 in total monthly AI Credits.
• Business: $19 per user per month with 1,900 AI Credits per user contributed to the organization pool.
• Enterprise: $39 per user per month with 3,900 AI Credits per user; it requires GitHub Enterprise Cloud.

The individual plans are not substitutes for an organization rollout. Business and Enterprise supply the pooled billing and administrative layer. GitHub's organization billing page does not state a free trial for those plans.

The $380 floor is the lowest published team baseline in this ranking. Its strongest business case is not “GitHub has the cheapest agent.” It is “the company avoids buying, connecting, securing, and teaching a separate control plane.” That saving disappears if GitHub is not already the code home.

Best for: Organizations whose software-delivery system of record is GitHub.
Standout: Agents, repositories, issues, pull requests, security, identity, and policy in one existing workflow.
Pricing: Free $0; Pro $10; Pro+ $39; Max $100; Business $19/user; Enterprise $39/user, all monthly.
Free trial: Organization-plan trial not stated on the live billing page.

Verdict: Choose GitHub when repository gravity is stronger than the need for platform neutrality. Skip it when adopting the factory would first require moving the code home.

3. Factory: best for a governed enterprise rollout

Factory is the best choice for a large organization that wants to increase autonomy in stages and attach governance to each stage. Its product model starts with Droids and skills for well-defined work, moves into Automations for recurring workflows, uses Droid Computers for persistent remote execution, and reaches multi-agent Missions for work decomposed into parallel tracks.

That progression is more useful than a blanket “autonomous engineering” claim. A company can keep sensitive or ambiguous work supervised while promoting repetitive, measurable tasks into recurring automation. Factory itself says autonomy is gradual and specific to an organization's readiness.

Droids can plan, write, test, and ship from terminal, IDE, browser, or Slack, with support across VS Code, JetBrains, Vim, Jira, and CLI workflows. Teams can adjust boundaries for edits, execution, and approvals, and choose among Claude, GPT, Gemini, or other models for a task. The promise is one agent core and organizational context across more of the development lifecycle.

Factory's enterprise measurement layer is another reason it ranks above Cursor and Devin for governed rollout. Factory Analytics tracks token use, tools, adoption, output, per-user activity, and readiness, with OpenTelemetry export. It is available to Enterprise customers and includes API access. Those dashboards still do not decide quality by themselves: files, commits, and pull requests are activity until they are accepted with tolerable review and defect cost.

The main wall is price discovery. Individual tiers are public, but Business and Enterprise are custom. A serious comparison therefore needs a quote with a representative task mix, expected model use, deployment boundary, support requirement, and a written definition of shared usage. Without that, the buyer can compare features but not economics.

Factory pricing

Factory's individual pricing has three published tiers:

• Pro: $20 per month, including the Factory App, Droid CLI, Droid SDK, and cloud and local background agents.
• Plus: $100 per month, with roughly five times Pro usage and managed Droid Computers for remote runs.
• Max: $200 per month, with roughly ten times Pro usage and early feature access.

Individual use is controlled by separate rolling 5-hour, 7-day, and 30-day limits. Extra Usage is prepaid, starts at $10, and does not expire. Missions require Extra Usage to be enabled and pause if a rolling limit is reached. That is acceptable for an individual pilot and a poor foundation for an organization-wide service expectation.

Factory's organization pricing has two quote-only tiers:

• Business: custom pricing for up to 150 seats, with shared limits, onboarding, SSO, SAML/SCIM provisioning, zero-data retention, audit trails, and policy controls.
• Enterprise: custom pricing for unlimited seats, with dedicated compute, on-premise deployment, sub-organizations, customer-managed encryption keys, data residency, and priority service terms.

Organization use is shared across the workspace and governed by the contract instead of individual rolling limits. The live pricing pages do not state a free plan or free trial.

Best for: Enterprises that need a staged autonomy program, policy controls, deployment choices, and leadership measurement.
Standout: A coherent ladder from supervised Droids to recurring Automations and multi-agent Missions.
Pricing: Pro $20/month; Plus $100; Max $200; Business custom; Enterprise custom.
Free trial: Not stated on the live pricing pages.

Verdict: Pick Factory when the procurement is an enterprise operating program, not a developer-tool reimbursement. Skip it when a transparent self-serve team price is a hard requirement.

4. Cursor: best for IDE-first teams adding cloud fleets

Cursor is the most consequential capability change in this ranking because the editor is becoming a code home and agent operating system. Origin entered early beta on August 17 with hosted repositories, pull requests, browsing, GitHub synchronization, and agents in the same surface; two days later, Cursor added event subscriptions, long-lived goals, isolated-VM subagents, and steering improvements to its cloud harness.

The business consequence is bigger than a new tab. Cursor can move from one tool in the developer budget to part of the source-control and automation budget. That can remove context handoffs between editor, cloud agent, and pull request. It also increases switching cost and the blast radius of an immature feature.

Cursor has handled the first transition carefully. A GitHub repository synchronized into Origin keeps GitHub as its source of truth. Updates happen in real time, and pull-request comments synchronize both ways. A team can browse and review in Cursor without declaring an immediate repository migration.

The Cloud Agent layer is substantial. Agents run in isolated virtual machines with repositories, dependencies, secrets, startup commands, and network access. They can be launched from web, desktop, iOS, Slack, GitHub, Bitbucket, Linear, or API. They produce screenshots, videos, and logs, and a human can take over the remote desktop for verification.

Cursor supports GitHub, GitLab, Bitbucket, and Azure DevOps connections. Multi-repository environments are supported, but long-running operation is not yet available for them. That named limit matters: a goal that spans frontend, backend, and infrastructure may fit a one-shot coordinated change but not the always-on loop the marketing direction implies.

Origin is also early beta on paid plans, with enterprise organizations able to opt out. That is the correct stage for mirroring selected GitHub repositories and measuring review behavior. It is not the stage for making Origin the only copy of a regulated company's most important repositories.

Cursor pricing

Cursor's pricing pages now span individual, regional, team, and enterprise tiers:

• Hobby: free, no credit card, with limited Agent requests and Composer access.
• Start: ₹649 per month, tax included, and available only in India. It includes Cursor Models and Cloud Agents but excludes the Other Models pool, on-demand use, Bugbot, Auto, Automations, and the SDK.
• Pro: $20 per month with $20 of included Other Models use.
• Pro Plus: $60 per month with $70 of included Other Models use.
• Ultra: $200 per month with $400 of included Other Models use.
• Teams Standard: $40 per user per month.
• Teams Premium: $120 per user per month with five times Standard's Agent limits.
• Enterprise: custom, adding the commercial and security controls needed for pooled use, invoicing, SCIM, and advanced governance.

Cloud Agents are charged at the selected model's API price. On Teams and Enterprise, third-party model requests also carry a Cursor Token Rate of $0.25 per million tokens. Cursor estimates that daily Agent users often consume $60 to $100 per month in total usage and multiple-agent or automation power users often consume $200 or more. Those are vendor estimates, but they are a useful warning against budgeting only the seat.

The correct Monday move is not a code-host migration. Synchronize a few GitHub repositories into Origin, keep GitHub as source of truth, and compare whether agents resolve pull-request feedback with less human relay. The code-host decision comes after the workflow result.

Best for: Teams already productive in Cursor that want cloud execution, event-driven agents, and an agent-native repository experiment.
Standout: The tight loop among editor, isolated cloud agents, proof artifacts, pull requests, and Origin code hosting.
Pricing: Hobby free; Start ₹649 in India; Pro $20; Pro Plus $60; Ultra $200; Teams $40 or $120 per user; Enterprise custom.
Free trial: Hobby is a free plan; Origin requires a paid plan during early beta.

Verdict: Pick Cursor when the editor is already the team's daily center and the next step is cloud delegation. Skip a full Origin migration until the beta, enterprise controls, and recovery procedures meet the codebase's risk level.

5. Devin: best for a queue of scoped delegations

Devin is the cleanest choice when the factory starts as a queue of bounded engineering tasks rather than a configurable multi-harness platform. Agent mode can implement changes, run tests, debug, and open pull requests, while managed Devins split larger work into isolated parallel sessions coordinated by another session.

The scope guidance is unusually useful. Devin recommends starting with clear success criteria and says, as a rule of thumb, that a task taking a human three hours or less is most likely to succeed. Larger work should be separated into focused sessions and run in parallel. That is vendor guidance, not a benchmark, but it gives a manager a concrete intake rule.

The advanced layer contains genuine factory mechanics. A coordinator can scope work, monitor sessions, resolve conflicts, and compile results. The MCP can create sessions with prompts, playbooks, tags, and ACU limits, search and inspect sessions, message or terminate them, and wait for parallel sessions to finish. Schedules support recurring and one-time work.

This makes Devin a good fit for test backfills, repeated migrations, small bugs, dependency work, and well-specified tickets. Each task can have an explicit definition of done and produce its own pull request. The manager sees a work queue rather than designing an orchestration architecture first.

The tradeoff is control-plane breadth. Devin's public product is organized around Devin sessions and their environments. If the requirement is one configuration layer that can swap among several independent coding harnesses, Warp is the closer fit. If the requirement is GitHub-native identity and policy across many agent providers, GitHub is the closer fit.

Devin pricing

Devin's live pricing has five tiers:

• Free: $0 with a light agent quota, limited model availability, unlimited inline edits, and unlimited Tab completions.
• Pro: $20 per month with frontier models, SWE 1.7 and leading open-source models, Devin Cloud, and extra usage at API pricing.
• Max: $200 per month with significantly higher quotas.
• Teams: $80 per month for the team plan plus $40 per month per full developer seat. It includes unlimited team members, collaboration, centralized billing, analytics, and priority support.
• Enterprise: custom, with SAML/OIDC SSO, centralized controls, dedicated account management, and a dedicated deployment option.

Paid usage allowances refresh daily and weekly, and additional usage is sold at API pricing. Devin Free is a permanent free plan, not a trial of the organization controls.

Devin can be a better operational choice than a more open system when the company lacks a dedicated platform team. A narrower product with a clear task queue is easier to own than a flexible factory nobody is assigned to run. The deciding question is whether the organization wants to delegate tickets or engineer a reusable development system.

Best for: Teams with a backlog of well-scoped tasks, migrations, test work, and repeated delegations.
Standout: Managed parallel sessions with coordination, playbooks, schedules, API, and MCP controls.
Pricing: Free $0; Pro $20/month; Max $200; Teams $80/month plus $40/full seat; Enterprise custom.
Free trial: Free plan available.

Verdict: Choose Devin when the first factory is a disciplined ticket queue. Skip it when the platform mandate is to standardize heterogeneous agents, models, and compute behind one portable definition.

Who should pick what?

The decision flips on the system you are trying to preserve.

Choose GitHub Copilot when GitHub is already the code, identity, pull-request, CI, and security system. Its $19 Business seat is not the whole cost, but the absence of a new operating layer can make it the lowest-risk default.

Choose Warp Factories when agent and model portability are strategic, platform engineering can own a factory definition, or self-hosted workers and custom inference are on the roadmap. The decision flips away from Warp when Early Access is disqualifying.

Choose Factory when the project is an enterprise autonomy program with staged controls, deployment requirements, and leadership analytics. The decision flips away when procurement needs transparent self-serve organization pricing.

Choose Cursor when developers already work in Cursor and the company wants to add cloud agents, event subscriptions, and an Origin experiment without moving GitHub immediately. The decision flips away when early-beta code hosting or the current multi-repository limit collides with risk requirements.

Choose Devin when the input is a queue of clear, bounded tasks and the output is one reviewable pull request per session. The decision flips away when the company wants an open layer over several agent harnesses.

There is a second rule beneath those product choices: do not buy more autonomy than the review system can absorb. Ten parallel agents are not capacity if every pull request waits for one overloaded maintainer. The factory budget must include the human bottleneck it creates.

How these platforms were picked

This is a verified comparison, not a fabricated test log. Every stated price, tier, limit, deployment option, workflow surface, and current beta status came from a vendor's live page during this run. Vendor-reported outcome metrics are labeled as such.

Seven criteria determined the order:

• Operating-loop coverage: can the product move work from intake to reviewable output, not just generate code?
• Concurrency: can it manage repeated, scheduled, or parallel work away from a laptop?
• Review evidence: does the reviewer receive tests, logs, screenshots, video, checks, or a clean pull-request trail?
• Governance: are identity, policy, permissions, approvals, audit, and data boundaries available at the appropriate tier?
• Meter visibility: can a buyer find the seat floor and identify the additional usage unit?
• Portability: how tightly are repository, model, agent, and compute choices coupled to the vendor?
• Named limits: what stops the platform from fitting the obvious buyer today?

Warp won because its product maps most directly to a configurable factory control plane. GitHub followed because existing workflow gravity beats theoretical flexibility for many companies. Factory ranked above Cursor on enterprise maturity, while Cursor ranked above Devin on the breadth of its editor, cloud, event, and code-host loop. Devin remains a strong choice for the narrower but common job of processing scoped work reliably.

No active partner from the supplied monetization pool belongs in this category. None was inserted as a weak sixth choice. A ranking that changes to satisfy an affiliate slot is less useful to readers and less likely to earn trust later.

For a broader enterprise view of agents, repository controls, and seat economics, see the current enterprise coding-agent comparison.

The ones to avoid

Avoid treating an individual coding subscription as the company factory. GitHub Pro, Cursor Pro, Factory Pro, and Devin Pro can support a pilot, but a reimbursement policy is not shared identity, policy, audit, spend control, or offboarding. Move to the organization tier before the agent touches production workflows at scale.

Avoid a local coding assistant marketed internally as autonomous infrastructure. If a laptop must stay awake, tasks cannot be queued or observed centrally, and output does not return through the normal review system, the organization has bought a faster editor, not a factory.

Avoid a homemade collection of cron jobs, API keys, and agent scripts without an owner. DIY can be correct when factory infrastructure is a genuine company capability. It is wrong when no one owns sandbox updates, secret rotation, duplicate events, retries, concurrency, review evidence, incident response, cost attribution, and decommissioning.

Avoid moving regulated or irreplaceable repositories exclusively to Cursor Origin during early beta. Mirror selected GitHub repositories first and keep GitHub as source of truth. Origin can prove whether the integrated workflow is valuable before it becomes a continuity risk.

Avoid running Factory Missions on an individual allowance as if it were a company service. Individual rolling limits can pause Missions. Business and Enterprise replace that model with shared contract terms; obtain those terms before promising capacity.

Avoid an annual factory contract before the accepted-PR economics exist. A large usage allowance can look discounted and still finance rework. The pilot must measure human review, rejected output, failed checks, and variable use alongside subscription price.

Finally, avoid a single-vendor mandate before the task classes are known. One controlled platform can be desirable, but standardize the intake, evidence, policy, and cost model first. The best agent for a routine dependency update may not be the best agent for a multi-repository migration.

The Monday move: buy one accepted workflow

Do not start Monday by comparing demo prompts. Start by naming one work class the factory should own by Friday of the following week.

Use 20 to 30 tagged tasks from the same low-risk queue. Dependency updates, flaky-test triage, bounded test backfills, and small bugs are better than a random backlog because the tasks share a shape. Exclude emergency work and architecture changes, where urgency or ambiguity will distort the comparison.

Monday morning: define accepted work

Write the release conditions before an agent begins:

• the repository and permitted file surface;
• the required tests and security checks;
• the branch-protection and code-owner rules that remain unchanged;
• the evidence required, such as logs, screenshots, or a short verification note;
• who can approve, reject, or stop a run;
• the maximum variable spend allowed for the pilot.

An accepted pull request is one that meets those existing conditions and merges without material human rewriting. A draft that looks impressive but is abandoned is not partial output. It is spend and evidence about the failure mode.

Monday afternoon: calculate the subscription floor

For 20 seats, enter the relevant starting point: GitHub Business at $380 a month, Cursor Teams Standard at $800, Devin Teams at $880, or Warp Business at $1,000. Factory requires a quote. Then create separate lines for model use, cloud compute or Actions, implementation time, and reviewer time.

Do not force unlike allowances into one fake “included tokens” number. Keep each vendor's meter in its native unit, then convert the actual pilot invoice and human time into dollars at the end.

Tuesday through the following Thursday: hold the system constant

Give each platform the same task class, environment readiness, repository instructions, and definition of done. Keep continuous integration, branch protection, security checks, and human approval unchanged.

Record these outcomes for every task:

• whether a pull request was opened;
• whether it passed required checks;
• whether it merged;
• reviewer minutes;
• material rework;
• failed or abandoned runs;
• model, compute, Actions, credit, or agent spend;
• elapsed time from intake to accepted pull request;
• policy exceptions or human interventions.

This is the minimum useful factory ledger. Generated lines and total agent sessions can be retained for debugging, but they should not decide procurement.

Friday: calculate cost per accepted pull request

Add subscription allocation, variable platform use, model and compute charges, implementation time, and reviewer time. Divide that total by accepted pull requests. Compare the result with the baseline cost and lead time for the same work class.

Then read the failure distribution. A platform with a slightly higher cost per accepted pull request may still win if its evidence is better, failures are easier to diagnose, and the operating boundary is more portable. A cheaper platform may lose if senior maintainers spend the difference rewriting output.

Expand only when the factory improves an outcome the business funds without increasing review debt, incidents, or uncontrolled spend. If it does not, change one layer, such as task scope, environment, model, or instructions, and run another batch. Do not widen repository access to rescue an unclear result.

The business consequence is simple: the new budget line is not “AI coding seats.” It is accepted software throughput with a governed variable meter. Buy that outcome, and the platform choice becomes much easier.

Frequently asked questions

What is an AI software factory?

An AI software factory is a controlled development loop in which agents and humans move work from intake through planning, implementation, verification, and reviewable repository output. It differs from a coding assistant because the unit is a repeatable workflow, not one interactive response.

Can I create my own AI software?

Yes, but creating an AI application and operating a software factory are different jobs. A factory is the infrastructure around repeated changes, so begin with one bounded work class, existing branch protections, a human checkpoint, and measurable acceptance criteria.

How much does it cost to build an AI software factory?

Among the published team plans here, 20-seat subscription floors run from $380 to $1,000 per month, while Factory organization pricing is custom. Total cost also includes models, compute or Actions, agent overages, implementation, and human review, so cost per accepted pull request is the more useful comparison.