Not to mention the meta of account limits, billing, ZDR contracts, etc.
If anything, you're better off supporting multiple LLMs as backup because most model providers have been so inconsistent with working all the time
You’re clearly not building a product based on an LLM.
I’m still using various old Anthropic and OpenAI models for products I’ve built and released because I can’t risk the behavior changing in unpredictable ways and the users being pissed.
It’s much easier to switch out some deterministic software than an LLM which you’ve spent a ton of time on testing and benchmarking and understanding its nuances. Changing it is like replacing an employee who’s critical to the business.
As for which model does the building... I'm not at all attached. Enough logic, and CI gates/tests live outside the whims of the LLM to be able to hotswap them any time.
In fact, most benchmarks show this! Most benchmarks have similar performance for the same classes of models.
On top of this, there are tools like open router, or even the openai SDK which trivially allows you to swap endpoints for the LLM!
If you're using the agents SDK from openai or something, then yeah it's not interchangeable but that's you doing it wrong
Because this claim is counter to my experience as well.
Of course my numbers are a sample of one and I am not spending a lot of money or time on it. Just lazily trying things on my "happen to have this" hardware. But basically trying out the Claude Code I'm used to from work but locally with a bunch of open weight models.
I can run super tiny models on my 8GB NVIDIA card. They all suck (I have to use <=~5GB models if I want "usable" ~250k context that doesn't need to use system RAM and CPU (which makes things super slow).
I've also tried a GLM 4.7-flash, which even though it's super slow (in comparison) with ~250k context and it just doesn't cut it vs. the Claude Sonnet or Opus I get to use at work. All the while these are all touted as "totally usable, Claude/ChatGPT killer!" replacements.
It's just not "there" with tool use or building software for that matter. Like, just a simple Claude "web search" fails with it. So I asked it to build itself its own "web search" functionality and it just couldn't. It made so many mistakes its just not funny any more. And it couldn't recover from them either. I retried a few times (as I didn't have python installed and it wanted to implement it using that - this happens to be new system - never mind other attempts). I spent as much time doing this (and failing) as I spent building an actual full feature at work last week w/ Sonnet.
If it can't build itself a simple web search to .md file tool/skill, how am I supposed to trust this with actual coding? I'm used to being able to point Claude at our large code base and essentially work with it like a junior doing my bidding. Maybe 5.2 is a killer game changer vs. what I was able to try out (if slowly) but you really have to show me to convince me at this point. And not with synthetic benchmarks. In those, all of the models I tried are supposedly super awesome.
Just spend $5 on OpenCode Go and give GLM 5.2 a shot if you have the time. It's not quite as good as Opus, but it's more than good enough for many tasks.
$5 the first month, then price is doubled.
Honestly, these days probably less friction switching out Redis or Elasticsearch (backend) than changing LLM provider (human facing).
Fable is seriously good enough now to, in a 20k line project, take "replace Mongoengine with raw PyMongo" and not screw anything up.
Those will be a pain.
Two LLMs with the same numbers on important benchmarks could have vastly different behavior in actual deployment. Not sure if as hard to switch as Excel <> Libre but still not "cheap and easy".
But the point is that at any moment, there is friction in switching
Rolling out AI access in a large business is still hard, especially if you're trying to do it safely e.g stopping people throwing all your company data including user PII into a chat for productivity reasons.
It's more a staff training and guardrails issue than a choosing which LLM to use issue, but I imagine picking an open model like GLM would make it harder because the 'enterprise stuff' will be missing.
Individuals perhaps, but not organizations.
But, eventually, I’m quite sure that AWS will also provide open models with those contracts without any inertia. Copilot is already offering Kimi.
My company has a deal with Devin and they provide new models all the time, and open models are becoming the most used ones by our internal metrics, especially because the company is very worried about cost.
They’re much cheaper to run, eg, Llama 3.3 Instruct 70B is 5-10x cheaper than Sonnet 5.
https://aws.amazon.com/bedrock/pricing/
Say you have 20% of usecases that require the more expensive model — but in 80% you could just use Llama instead of Sonnet (eg, for basic queries of a document). That saves 80% of that 80%, or 65% of your total bill!
That is the kind of “swap” that’s likely to occur in automated tooling as pricing pressure kicks in — “can you save 65% on our AI bill by switching Bedrock over in 80% of uses?”
There are some ok models on there (Qwen 3 Coder Next is usable and fast, for instance) but the lack of updates in a fast-moving field makes it something I don't want to recommend to my org.
There's barely any moat. All the data is with connectors, memory is near useless
Once your team gets settled with Claude teams, cowork, and the various plugins, it’s going to be a pain in the butt to switch.
But switching models is just a command.
AI is possibly the first product in history that will eagerly help you replace it with one of its competitors.
Or even better just silently sabotage the migration so you can’t do it. Something we can definitively expect from Claude given past behavior
For now
I use OpenRoutet which lets you switch between providers (Anthropic, ChatGPT, Z-AI) whenever you want. Sometimes I'll have two different models from different providers evaluate each other's answers.