This content originally appeared on DEV Community and was authored by Michael Ryaboy
Picking a single LLM provider can feel like settling down on the first date. New models are released, user preferences change, prices move, rate limits are hit, and latency spikes appear at the worst moments. If your app depends on fast Time‑To‑First‑Token and predictable throughput, brand loyalty won’t save you when an outage occurs or an inference provider decides to lower model latency or decrease dedicated compute for a model.
Model Polyamory Should Be the Default
This post argues for model polyamory—by design. Instead of binding your app to one endpoint, put a router in front and treat providers as interchangeable parts. I use OpenRouter to fan out across multiple providers, handle retries and failures automatically, and let price and performance compete in real time. The result is fewer incidents, smoother latency, and the freedom to swap in newer, better models without rewrites.
Background: Why I Keep Building Routers
I have built many LLM routers in my life. Sometimes, I need to give users the ability to choose a model. Other times, I'm hitting rate limits or want to maximize free tiers from inference providers. The thing that invariably happens is I end up managing several models that seem to become outdated every few months.
LLM applications require reliability, and the dirty secret of LLM inference providers is that they all have poor reliability, including major players like OpenAI. Sudden jumps in latency, failed requests, and rate-limits are the norm, not the exception. If you are using a single provider without dedicated instances, you are pretty much guaranteed to have a bad time.
When designing LLM applications, keep two principles in mind:
- Your current model is temporary. The model you're using today won't be the one you use in a few months—newer models will emerge that are smarter, cheaper, and potentially faster.
- Expect failures at every level. Whether it's the model provider, AWS, GCP, or any other cloud infrastructure serving inference, outages are inevitable and happen regularly.
How OpenRouter Solves This
That's the problem OpenRouter solves. OpenRouter manages multiple providers for a single model or set of models, automatically routing to the cheapest available provider. When a provider fails, it seamlessly backs off and redirects requests to other providers without dropping them.
Since you typically have multiple providers for most OS models, you get substantially better durability and pricing:
Here OpenRouter will automatically route between DeepInfra, Parasail, and Inference.net.
Benefits
Adding new models is simple, and rate limits are virtually eliminated since OpenRouter negotiates limits individually with each provider. You can filter providers based on data training policies and latency requirements. When your model becomes outdated in a few months, switching takes just a few lines of code. Built on Cloudflare's infrastructure, routing is extremely fast—-typically under 50ms.
These days, as a consumer, I'm extremely annoyed when I can't use the specific model I want. I'm biased with extreme preferences, and become annoyed easily when a platform locks me into using a model that I don't think is the smartest for even the simplest of tasks.
Trade‑Offs
The main drawback of OpenRouter is weaker support compared to dedicated providers, and this is definitely something to consider. Still, the seamless model switching and provider flexibility make this trade-off worthwhile for most developers, and it's particularly compelling for individual developers who value agility over dedicated support.
Despite this, I don't really consider using anything but OpenRouter. In the beta phases, I can use completely free LLMs to test, then seamlessly transition to more powerful models.
You Need a Router
I can test multiple models to see which performs the best, and roll back changes extremely quickly. I can see statistics on what others are using for specific tasks, and get granular Time-To-First-Token and Tokens-Per-Second for every model. For classification tasks, this is an absolute necessity.
Another dirty secret of LLM inference providers is that they don't necessarily run at the same floating-point precision. Anthropic has been recently accused of using a quantized model during the day to serve increased demand. This kind of behavior makes loyalty to a single provider extremely difficult, even if they have some of the best coding models.
TL;DR
Just use a router like OpenRouter and eat the fee. Save yourself some time and headaches, and don't overengineer.
This content originally appeared on DEV Community and was authored by Michael Ryaboy
Michael Ryaboy | Sciencx (2025-08-26T23:02:09+00:00) Don’t Marry an LLM, Date Many. Retrieved from https://www.scien.cx/2025/08/26/dont-marry-an-llm-date-many/
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