Openrouter vs Together AI

A router and a host are not the same product

A routing layer is an intermediary. You integrate once, and it brokers your request out to whichever upstream serves the model you named, normalising the request and response shapes so that switching models is a string change rather than an integration. Its value is breadth and indirection: one credential, one bill, one client, and a catalogue far larger than any single operator would host.

A first-party host runs the serving stack. It has chosen which open-weight families to support, provisioned hardware for them, and owns the queueing, batching and scaling behaviour end to end. Its value is directness: fewer moving parts between your process and the accelerator, and a single organisation accountable when throughput degrades.

Read that as a trade rather than a ranking. Indirection buys optionality and costs you control; directness buys control and costs you optionality. Which one is worth more depends entirely on whether your model choice is still moving.

What the routing layer actually buys you

Its strongest case is the period when you do not yet know which model you are shipping. Evaluating candidates across several vendors normally means several contracts, several SDKs and several billing relationships before you have learned anything; a router collapses that to one integration, so the cost of trying a model drops to near zero and you can keep testing after launch instead of freezing your choice at the point of maximum ignorance.

The second case is continuity. When an upstream degrades or retires a model, a routing layer can shift traffic without a deploy, and that indirection is worth real money to a product whose core feature stops working when one vendor has a bad afternoon. Confirm what the failover actually does, though, silently rerouting to a different model is not the same guarantee as rerouting to the same model on different capacity, and only one of those preserves your output quality.

The cost side is that you have added a hop and a dependency. Your effective availability now includes the router itself, your traffic reaches the upstream on terms the router negotiated rather than terms you did, and anything requiring a direct relationship, committed capacity, custom deployments, contractual latency terms, is harder to reach through a broker.

When operating your own serving stack wins

Predictability is the honest reason to go direct. A workload that is latency-sensitive in a user-visible way cares less about the median than about the bad tail, and tail behaviour is a property of queueing and capacity policy, which is precisely what you can discuss with an operator and cannot fully see through an intermediary. If your product feels broken when a response is slow, that conversation matters more than catalogue size.

Depth is the second reason. Fine-tuning, dedicated or reserved capacity, private deployment, and negotiated terms are relationship features. They need an operator who runs the hardware, and they are the reason teams with a settled model and a serious volume curve tend to migrate toward first-party serving over time.

Compliance often forces the same answer. Once you must document where inference happens, who can see the payloads, and what is retained, a direct relationship gives you one party to put those commitments in writing. Through a broker you are asking about a chain, and every additional link is another thing your reviewers must accept.

The questions that actually settle it

• Is your model choice settled, or are you still evaluating?
• How does your traffic arrive, steady, or spiky and seasonal?
• What actually breaks when inference is slow or unavailable?
• What would leaving cost you, measured concretely?

Is your model choice settled? If you are still evaluating, the router wins on switching cost alone, and that advantage is largest exactly when uncertainty is highest. If you shipped months ago on one model and have no intention of moving, you are paying for optionality you have stopped using.

How does your traffic arrive? Steady, predictable load is the shape that benefits from reserved capacity and direct terms. Spiky or seasonal load is the shape that benefits from someone else absorbing the peaks, which is easier to buy through a layer designed to spread traffic across upstreams.

What breaks when inference is slow or unavailable? A background job that retries in a minute tolerates a longer chain than a synchronous feature a user is waiting on. Write down the actual user-visible consequence before you weigh any latency claim, because that consequence is what determines how much predictability is worth to you.

And what would leaving cost? Both paths are exits with a price. Measure it concretely: how much of your code assumes one request and response shape, how much prompt and evaluation work is tuned to one model family, and how long a switch would realistically take. A router lowers that cost by design, which is itself a reason to start there and revisit later.

A short evaluation that produces a real answer

Do not benchmark with a synthetic prompt loop; it measures the vendor on a workload you do not have. Take your actual prompts, at your actual context lengths, with your actual concurrency, and run them against both paths for long enough to cross a busy period. Record the slow tail rather than the average, because the tail is what your users will report.

Then test the failure you fear most instead of the happy path. Force a rate limit and watch what comes back. Ask what happens when your chosen model is unavailable, and verify the answer against observed behaviour rather than a documentation promise. Check whether errors tell you which layer failed, through an intermediary, an unhelpful error is a genuine operational cost you will pay repeatedly at three in the morning.

Finish by writing down the decision and the two or three reasons behind it, along with the condition that would reverse it: a volume threshold, a latency requirement, a compliance review. This choice is reversible at a price, and the price stays lowest when it is revisited on a trigger you defined in advance rather than continuously.