WorkLLM
Vs
Viktor
Viktor is an AI coworker you delegate tasks to in Slack or Teams. WorkLLM converts your company’s everyday work into AI agents that run it automatically, no technical expertise required.
This page compares how WorkLLM and Viktor work in practice so you can choose the right product for your company.
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WorkLLM vs Viktor
What each product is best at
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Full Comparison - WorkLLM vs Viktor
Asking AI
| Capability | WorkLLM | Viktor |
|---|---|---|
| Multi-model access and comparison | Access 200+ models, including GPT, Claude, Gemini, Llama, and Mistral, from one chat interface, and compare up to 4 side by side on the same prompt, switching per task whenever you want. | Offers about 10 named models across four presets (Ultra, Smart, Balanced, Cheap). The model is a workspace-level setting that applies to everyone and needs admin approval to change, it can't be switched per task, and there's no side-by-side comparison. |
| AI Answers, websearch & research capabilities | Get cited, up-to-date answers with web search, plus deep research using specialized models, right inside your workspace. | Delegated research requests pull from connected tools and the web to compile findings directly in Slack or Teams. |
| Chat with documents | Upload and chat with PDFs, Word files, spreadsheets, and presentations, grounded in your projects and organization memory. | Can read and work with documents shared in a conversation as part of a delegated task. |
| Remembers your preferences and context | Remembers your personal preferences, stays consistent within a conversation, and carries context across a shared project. | Persistent workspace memory means Viktor retains context and improves from team feedback over time. |
| AI answers grounded in your company knowledge | Every answer can pull from your company's own documents, decisions, and past work automatically, not a generic guess. | Persistent context comes from what's shared in conversations over time, not a structured, company-wide knowledge layer applied automatically. |
Getting the work done
| Capability | WorkLLM | Viktor |
|---|---|---|
| Create docs, sheets, slides, flowcharts, wireframes and diagrams | Docs, sheets, slides, flowcharts, and diagrams, delivered as an actual file or visual you can use immediately. | Delivers real deliverables, reports, PDFs, and dashboards, directly in the conversation as part of a delegated task. |
| Create & edit images, video & audio | Supports generating and editing images directly in chat; for video and audio, it understands but cannot create or edit them. | Not positioned around native image, video, or audio generation, delegated tasks focus on documents, data, code, and reports. |
| Write & debug code | Coding models help with generation and debugging inside the chat interface, not directly in your codebase. | Runs its own cloud compute environment to write and execute code, open real pull requests, and deploy a working internal app or dashboard your team can open from a link, a genuinely more complete coding workflow than chat-based help. |
| Write in your brand's voice and tone | Every generated document, email, or piece of content can pull your brand voice, tone, and business context from organization memory automatically. | Persistent context can pick up tone over time, but there's no dedicated, curated brand-voice layer applied automatically. |
| Do your work in your work apps | Ask in chat, and get data from your CRM, send a message in Slack, or update any record in any tool connected natively or through MCP. | Delegate a task in Slack or Teams, and Viktor acts directly inside your connected tools, one of the widest integration breadths available, 3,200+ tools. |
Automating the work
| Capability | WorkLLM | Viktor |
|---|---|---|
| Ready-made agent library for your workflows | A library of ready-made AI agents for sales, marketing, HR, product, operations, and more, each built to work across multiple apps in one run, so you're running one on day one, not building from a blank page. | No prebuilt library of named, role-specific agents, you delegate to one generalist coworker or build a persistent agent yourself using Agent Builder. |
| Automate your workflow in minutes | Describe your workflow in one prompt, and see the exact steps the AI agent will take before it runs, edit any step, and choose a model per step. | For most delegated tasks, there's no setup step at all, you install Viktor and start delegating directly, and it learns through correction over time. A separate Agent Builder exists for creating additional persistent agents from a prompt, but without a visible, editable plan or model choice before it runs. |
| Run your workflows in your work apps | An AI agent can act directly inside 100+ natively connected tools, or any other tools through MCP, no waiting for a native integration to be built. | Connects to 3,200+ tools, one of the widest breadths available, and publishes both an MCP server and client for further extension. |
| Full transparency: review every step, or approve as it runs | See the exact steps an AI agent will take before it runs, edit any step, choose a model per step, and decide whether it runs fully on its own or holds for your approval. | Pauses for approval on individual sensitive actions as they come up, but there's no visible, step-by-step plan of the full task shown before it starts. |
| Governance & usage: version history, run reports, and spend tracking | See exactly who ran each AI agent, how many times, what model it used, and what it cost, with full execution logs down to the individual run. | Credit-based billing shows overall spend, but there's no confirmed equivalent to per-agent version history and run-level reporting the way WorkLLM provides. |
| Shared AI agents across your workspace | Once an AI agent is built, anyone in the workspace can use it, and it always runs from the current version, so updating it once means every future run reflects that change automatically, for everyone. | Agents built with Agent Builder can be used by the workspace, but role-based access controls are still in development, so fine-grained control over who can use or edit a shared agent isn't fully available yet. |
Working with your team
| Capability | WorkLLM | Viktor |
|---|---|---|
| Co-prompting, commenting, & tagging colleagues on AI answers | Multiple people work inside the same AI thread, co-prompting, commenting, and tagging each other, so a conversation becomes shared work, not a private one-off. | Works natively inside Slack and Teams threads, so colleagues see the conversation, but there's no dedicated co-prompting or structured commenting layer on top of it. |
| Shared team projects | A project holds multiple folders, each with its own memory, and anyone with access can start a thread or add context, so the whole project builds shared knowledge, not just a list of past chats. | Persistent workspace-level context is shared, but there's no structured project system with multiple folders and their own memory. |
Security and control
| Capability | WorkLLM | Viktor |
|---|---|---|
| Tenant isolation and deployment options | Enterprise customers can run on dedicated, physically isolated infrastructure, a private VPC with your own servers and storage, included as part of the WorkLLM platform. | Runs in isolated execution environments, but there's no confirmed dedicated, physically isolated infrastructure option, even at the Enterprise tier. |
| Encryption and single sign-on | Data encrypted at rest and in transit, with SAML-based SSO for enterprise deployments. | Data encrypted at rest and in transit, with SAML 2.0 SSO available from its Seed-stage tier upward. |
| Access control, audit logs & guardrails, on every plan | Role-based permissions, audit logs, and input/output guardrails included by default. | Has a built-in approval flow for sensitive actions. Role-based access controls and a private mode are listed as still in development. |
| Your data is never trained on or retained | Customer data isn't used for model training, with zero data retention at the LLM layer. | States customer data isn't used for model training. A specific zero-retention commitment at the LLM layer isn't separately documented. |
| Admin dashboard and governance | A central admin dashboard with usage analytics per user and per agent. | Shows overall workspace credit usage. A detailed, per-user and per-agent dashboard isn't part of what's publicly documented. |
Disclaimer: Information about Viktor is based on publicly available documentation and product information as of September 2026. Features, pricing, and capabilities may change over time.
WorkLLM vs Viktor
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