[ comparison ]
Kikimora vs Torq:
conversation vs hyperautomation.
Torq positions itself as an autonomous AI SOC and security hyperautomation platform for large enterprises. Kikimora takes a different path: a chat interface that operates your security stack, where every write action waits for your approval. Here is an honest side-by-side.
Side by side
| Dimension | Kikimora | Torq |
|---|---|---|
| Approach / interface | Conversational. You operate the security stack by chatting; no playbook canvas to build. | Torq positions itself as an autonomous AI SOC and hyperautomation platform, driven by agents and workflow building. |
| Setup effort | Connect tools via read-only APIs, start asking questions. Free tier needs no credit card. | Enterprise onboarding around hyperautomation workflows and the HyperSOC agent system, per their public materials. |
| Automation model | AI proposes actions in chat; you decide what runs. No autonomous remediation loop. | Torq states that over 90 percent of cases are remediated autonomously by its Socrates omni-agent. |
| Human oversight | Human-in-the-loop by design: every write action is proposed and waits for explicit approval. | Offers oversight, but the headline value is autonomous case handling at scale. |
| Built-in scanning | Four built-in tools need no third-party account: Qualys WAS, Shodan, Wazuh, a local Network Scanner. | Orchestrates and automates across your existing tools rather than shipping its own scanners. |
| Integrations | 18 total: 14 user-activated (AWS, Azure, GitHub and more) plus 4 built-in. | Broad connector library and an alliance program with vendors like Wiz and Zscaler, per their announcements. |
| Data residency / EU hosting | EU company (Bulgaria), GDPR compliant. AI runs on Vertex AI EU-region endpoints only. | Region options per their documentation; confirm EU specifics with Torq directly. |
| AI model transparency | AI models from Google and Anthropic via Vertex AI EU endpoints. No training on customer data. | Uses AI agents across the platform; model details per their documentation. |
| Pricing entry point | Free tier: 30 assets, unlimited integrations, 5 million AI credits, no credit card. | Enterprise sales motion; raised a $140M Series D in early 2026 at a $1.2B valuation. |
| Target team size | SMB and mid-market security teams that want results without building automation. | Large enterprise SOCs, including Fortune 100 names cited in Torq announcements. |
Competitor details reflect Torq public positioning and announcements as of 2026. Verify current specifics with Torq directly.
When Torq is the better fit
Torq is built for large, mature SOCs that want autonomy at scale. Its HyperSOC platform and the Socrates agent are designed to take security cases from alert to remediation with minimal human steps, and Torq cites very high autonomous-resolution rates. If you run a high-volume operations center, have the staff to govern autonomous workflows, and your priority is throughput, Torq is a strong, well-funded choice that enterprises like the ones in its case studies have standardized on.
When Kikimora is the better fit
Kikimora fits teams that want to operate their security stack without building automation first. You connect tools via read-only APIs and start asking questions: what is exposed, what changed, what should I fix. Every write action is proposed and waits for explicit approval, so a person stays in the loop on every change. Four built-in tools (Qualys WAS, Shodan, Wazuh and a local Network Scanner) work with no third-party account, and the free tier covers 30 assets with no credit card. For SMB and mid-market teams, that is a faster, lower-commitment starting point.
Migration and coexistence
These two can coexist. A common pattern: Torq drives high-volume autonomous workflows in a central SOC while Kikimora gives engineers a conversational way to investigate and triage their own environment, with approval-gated actions. Because Kikimora connects through read-only APIs by default and needs no agents, you can trial it alongside an existing automation platform without disrupting it. Start with the free tier, point it at a slice of your assets, and compare the conversational workflow against your current playbooks before deciding where each tool belongs.
FAQ
Is Kikimora a replacement for Torq? +
Not exactly. Torq is an autonomous SOC hyperautomation platform aimed at large enterprise security operations centers. Kikimora is a conversational security platform where you ask questions and approve actions. If your goal is fully autonomous, high-throughput incident remediation across a mature SOC, Torq is built for that. If you want to operate your stack by chatting, with a human approving every write, Kikimora fits better.
Does Kikimora automate remediation like Torq does? +
Kikimora proposes write actions in the chat and waits for explicit approval before anything runs. It does not close cases autonomously. Torq highlights autonomous remediation of the majority of cases. The trade-off is control versus hands-off scale.
Do I need to build playbooks to use Kikimora? +
No. Kikimora is no-code and conversational. There is no workflow canvas or playbook to build. You connect tools and ask questions in plain language.
Where does Kikimora run AI and store data? +
Kikimora uses AI models from Google and Anthropic served exclusively from Vertex AI EU-region endpoints. Customer data is never used for training. It is an EU company based in Bulgaria, GDPR compliant, with read-only API access by default, AES-256 at rest and TLS 1.3 in transit.
Also compare
Try the conversational approach.
30 assets, unlimited integrations, 5 million AI credits. No credit card, no playbooks to build.