Mycel vs Solid: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Mycel and Solid — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Mycel

Freemium

Mycel learns a service firm's work from one past deliverable, then drafts every future one for owner approval before it ships.

One-Deliverable Onboarding: Upload a single past piece of client work and Mycel infers your firm's format, tone, and structure, so it can draft the next one without a lengthy template build.
Approval-Gated Output: Every draft waits for your sign-off before it ships, keeping the human as the last pair of eyes while removing the blank-page work.
Correction Memory: A correction you make once is carried into later drafts, so repeated edits stop recurring month after month.
White-Labelled Client Portal: Clients get their own sign-in on your brand, with credentials kept separate per business rather than shared under Mycel's name.
Recurring Desks: Prebuilt loops for accounts receivable chasing, monthly close packs, pipeline outreach, recruiting longlists, and contract redlines run on a schedule.
Rendered Deliverables: Output is inspected as the real artifact — an actual spreadsheet or document with the exact figures the client receives — not a filename in a queue.
Job-Based Metering: Volume is counted in jobs (one message answered, sync run, or document produced) with model costs included and no overage charge.
Apache-2.0 Self-Hosting: The same code can be run on your own servers with your own model key, free and unmetered, for teams that cannot use a hosted service.

Agency Deliverable Drafting: A consultancy or SEO agency uploads a past client report so Mycel drafts the monthly version for every account, leaving only review.
Bookkeeping Month-End Close: Finance-service firms run the close loop and receive a client-ready pack without an owner rebuilding it each cycle.
Accounts Receivable Chasing: Late invoices are followed up automatically so the principal stops asking clients for money twice.
Recruiting Longlists: Per-search candidate longlists are screened in writing and returned ready for a recruiter to shortlist.
Contract Redlining: Incoming contracts come back marked up and ready for signature rather than waiting for a free afternoon.
Owner Capacity Relief: A founder who is the bottleneck on every draft keeps final judgment but stops being the person who writes the first version.
Private-Cloud Deployment: Teams with security or procurement constraints self-host the Apache-2.0 runtime inside their own infrastructure.

Paid

Always-on AI agents with their own computers, phones, accounts and budgets that complete jobs end to end and message you when the work is done.

Dedicated Devices: Each agent can use Windows and macOS computers, Linux servers, iPhones and Android phones, so it can operate any software a person can.
No Prebuilt Connectors Needed: Agents connect through APIs, build missing integrations or operate websites, desktop software and phone apps directly.
Own Accounts and Budgets: Agents hold Google and Apple accounts and can sign up for and pay for services within your budget and approval rules.
Self-Healing Execution: When a tool or setup breaks, agents investigate, repair and re-verify the job, and explain blockers when they need help.
Self-Improving Memory: Corrections and fixes are retained across jobs so the agents change how they approach future work.
Self-Scaling Teams: Agents can create more Solid agents or bring in Codex and Claude Code, divide work and return one checked result.
Spending Transparency: As meta-agents they can break down what each job cost across AI usage, machines and purchases.
API and Enterprise Controls: Embed always-on agents in your product via the Solid API, with workspace-wide access, budget and approval policies on Solid Cloud, VPC or on-premises.

Custom Sales Demos: Turning customer meeting notes into a tested, hosted demo app with sample data.
LinkedIn Lead Generation: Researching accounts against an ICP, drafting outreach for approval, following up and updating the CRM.
AI Product Evals: Building and running simulated-user evaluations after every release and reporting what passed or failed.
Production Bug Resolution: Investigating alerts, reproducing issues, writing a tested fix and opening a PR for engineer approval.
Customer Support Resolution: Tracing stalled tickets across systems, applying policy-approved fixes and confirming with the customer.
Dashboard Automation: Connecting Gmail, HubSpot and other sources to build and deploy an automatically updating dashboard.