Why one model is rarely enough
- They're good at different things. One writes the way you like. Another handles code better. A third is cheaper for bulk work.
- They go down at different times. A second provider is a backup plan.
- Prices and limits differ. Routing routine work to a cheaper model saves the expensive one for where it counts.
- Some work should stay local. A model on your own machine keeps sensitive text at home.
The real cost: context that doesn't travel
Each AI keeps its own history. Switch tools and you start from zero: which machines you have, what the project is, what you already tried, what broke last Tuesday. You paste it in, again. The work you did explaining things to one model is invisible to the next.
The subscription is the small cost. The big one is the minutes, every single day, spent rebuilding context by hand.
How people carry context today
A running context document
Keep a text file of the background every model needs and paste it at the top of a new chat. It works. It's also manual, and it goes stale the moment you forget to update it.
Each product's own instructions
Most AI products let you save standing instructions or project files. Useful, but they live inside that one product. Your notes in one don't reach the other.
Files in the repo, for coding agents
Coding agents increasingly read instruction files from the project itself. Claude Code reads CLAUDE.md at the start of every session and can also read an AGENTS.md written for other agents. Because the file lives in the repo, it follows the code to every tool and machine.
What a real shared memory needs
If you were designing it properly, a memory that works across models would:
- Remember your machines, your projects and what has already been done.
- Work no matter which model you're talking to today.
- Work on every machine you use.
- Mask secrets, so a password pasted once doesn't get repeated forever.
- Live on your own hardware, under your control.
Keys, accounts and costs
Bringing your own key means you pay each provider directly, on their pricing and under their terms. API usage is usually billed by how much you use rather than a flat monthly fee, so check each provider's current pricing before you route bulk work to it.
How Syrebrum does it
Syrebrum lets you add a key once for Claude, OpenAI, Gemini or Grok, reach Llama through OpenRouter or Groq, and run local models through Ollama. Switch whenever you like. Its shared memory remembers machines, projects and what has been done across machines and AI tools, with secrets masked, so the next model starts where the last one left off.
Questions
Can I use ChatGPT and Claude at the same time?
Yes. Many people subscribe to both. The friction is carrying context between them, which is what a shared memory solves.
Is it cheaper to use API keys than subscriptions?
It depends on how much you use. API usage is generally billed by volume, subscriptions by the month. Check each provider's current pricing.
Does Syrebrum send my memory to the AI providers?
When you use a cloud model, the text you send goes to that provider under your own account and terms. Local models through Ollama keep everything on your machine.