Two different weight classes, and that is the point
Why teams evaluate Archer alternatives
Archer is one of the originals in integrated risk management, and at the top of the market it earns its position: operational risk, IT risk, third-party risk, and regulatory compliance modeled together, configurable to almost any taxonomy, deployable on-premises where that is mandatory.
That power has a shape. Deployments are configuration projects measured in months, frequently with a partner. Pricing is modular and custom, commonly reported from $75,000 into the hundreds of thousands per year. And reviewers consistently describe the interface as dated with a steep learning curve — which matters when the people who need to file evidence are engineers, not risk analysts.
Most teams evaluating Archer alongside episki are not choosing between equals. They are asking whether they need an enterprise IRM platform at all, or whether they need the compliance program to run itself.
- No implementation project — self-serve signup, sensible defaults, first policy drafted in minutes
- A published price — $7,500/yr for the platform, unlimited users and frameworks
- Agents that do the drafting — instead of workflows that route it to a person
Where episki is different
episki does not try to be Archer. It makes the opposite bet: rather than configurability for a risk department, opinionated defaults plus agents that do the work.
Those agents draft policies, answer security questionnaires, map controls across frameworks, and advance vendor reviews over email between audits. The AI authors deterministic recipes — plain, inspectable procedures — that then run without a model in the loop, so an auditor reads how an artifact was gathered rather than trusting a generation.
And every control check produces a verdict. Each integration operation decodes its response, evaluates its assertions, and writes pass, fail, or inconclusive. Empty evidence attests nothing. An incomplete sync cannot mark a control clean. A failing check raises a finding with the offending records attached. An approved exception, bound to an approver and an expiry, can satisfy a check for named records without pretending the condition changed.
Boundaries are real: programs report against individual scopes with rules on cloud account, region, resource, and tag — enough to define a PCI cardholder data environment precisely, without modeling a taxonomy first.
When Archer might still be the better fit
If you are a large enterprise with a staffed risk function, need operational and regulatory risk modeled alongside IT risk, or have a hard on-premises requirement, Archer is the more capable platform and episki is not a substitute. The honest dividing line is whether you are buying a risk modeling system for a department, or an operator for a small team.
episki vs Archer: feature comparison
| Feature | episki | Archer |
|---|---|---|
| Approach | Autonomous GRC — agents run the program; humans approve the work that matters | Integrated risk management — a configurable enterprise platform spanning operational, IT, third-party, and regulatory risk |
| Built for | Security and compliance teams from one person to a few hundred employees, who need the program to advance without headcount | Large enterprises with a staffed risk function and a multi-year GRC roadmap |
| Pricing model | Published — platform $750/mo (or $7,500/yr) + optional modules; unlimited users and frameworks, with AI tokens the only metered resource | Custom enterprise licensing, modular by use case, commonly reported from $75,000 to $300,000+ per year depending on modules, users, and deployment |
| Time to value | Same-day — self-serve signup, connect a cloud account, and an agent drafts your first policy in minutes | A configuration and implementation project, frequently measured in months and often involving a partner |
| Deployment | Cloud, with optional regional data residency for US, EU, or Canada | On-premises or SaaS, which is a genuine advantage where on-prem is mandatory |
| Who does the work | Agents draft policies, narratives, questionnaire answers, and control mappings; a human approves | Your risk and compliance team, inside highly configurable workflows |
| Risk management | Risk module — qualitative and quantitative scoring, treatments, and acceptance wired to controls and evidence | The deepest integrated risk model in the category, connecting operational, IT, third-party, and regulatory risk in one framework |
| Controls & evidence | Continuous controls that produce a verdict — every check evaluates the evidence it collected and writes pass, fail, or inconclusive, and a failing check raises a finding. Empty or undecodable evidence attests nothing | Control and assessment management, with automated technical evidence collection depending on configuration and add-ons |
| AI capabilities | Agents draft, answer, and map — and the AI authors deterministic recipes that then run without a model in the loop, so output is reproducible | AI features layered onto an established enterprise platform |
| Integrations | AWS (multi-account, multi-region, and Organizations), GitHub, Google, Microsoft, Slack, Teams, Jira, Linear, Supabase, Vercel, and Netlify — each writing evaluated control coverage out of the box | Extensive integration capability, typically realized through configuration and professional services |
| User experience | Notion-like, keyboard-first editor, a global command palette, and a desktop app with tabs | A mature interface that reviewers consistently describe as dated, with a steep learning curve |
| API & agent access | REST API, a published entity-ontology catalog with a drift checksum, and a hosted MCP server whose writes route through the same API as the UI | REST API and enterprise integration tooling |
Why teams switch from Archer to episki
- Self-serve signup with no implementation project and no onboarding fee
- 34+ frameworks pre-built, adopted through a wizard rather than modeled by a consultant
- Same-day setup, with a 14-day free trial and no credit card
- Agents draft policies, narratives, and questionnaire answers from your own evidence
- Vendor reviews advance over email, with inbound attachments triaged and linked with provenance
- AI authors deterministic recipes; the recipes then run without AI in the loop, so auditors can trust the output
- Empty, undecodable, or partially collected evidence returns inconclusive and attests nothing
- A failing check raises a finding with the offending records attached
- An approved exception can satisfy a check for named records — but it needs an approver and it expires
