An OpenAI-compatible Chat Completions application can usually begin an AtmoRouter migration by changing three settings: the base URL, API key and model ID. The important work is verifying the behaviors your application depends on before directing normal traffic to the new configuration.
Keep the existing configuration available while you test. Changing gateways does not transfer balances, credentials or provider-specific settings.
Record your current integration
The OpenRouter quickstart uses https://openrouter.ai/api/v1 as its OpenAI-compatible base. AtmoRouter's Chat Completions base is https://atmorouter.dev/v1.
Before switching, identify the request features your application actually sends:
- Model ID and any automatic model-selection rules.
- Streaming and stream cancellation.
- Function tools, structured output or image inputs.
- Custom provider routing, fallback settings or extra headers.
- Retry policies, timeouts and maximum output limits.
OpenRouter-specific controls are documented in its API reference. Do not assume another gateway will interpret them identically just because both accept Chat Completions requests.
Create a new key and map the model
Generate an AtmoRouter key in API Keys, then find the intended model in the catalogue. Copy its full ID. Similar model names across services are not a reliable mapping, and a family prefix from one gateway may not exist on another.
Verify the input/output prices and context window for your chosen entry. If a feature is essential, such as tool calling, test that feature explicitly. A successful text response alone does not prove that an agent can complete its workflow.
Make your client configuration explicit
Here is a Python Chat Completions example. Install the openai package, then set ATMOROUTER_API_KEY and ATMOROUTER_MODEL in the process environment before running it.
pythonimport os
from openai import OpenAI
client = OpenAI(
base_url="https://atmorouter.dev/v1",
api_key=os.environ["ATMOROUTER_API_KEY"],
timeout=60.0,
)
response = client.chat.completions.create(
model=os.environ["ATMOROUTER_MODEL"],
messages=[{"role": "user", "content": "Reply with one short sentence."}],
max_tokens=64,
)
print(response.choices[0].message.content)This example calls Chat Completions. It does not establish support for a different endpoint such as /responses, nor for every optional parameter accepted by another provider. Check AtmoRouter's API reference against your actual request shape.
Validate a small workload
Start with short requests from a development environment. Confirm that the intended model appears in Usage logs, and compare the recorded tokens and cost with your expectation.
Then exercise one real example of each feature you use. For streaming, check incremental output and what happens when your client cancels. For function tools, check both the model's proposed call and the application's tool-result response. Inspect typed errors rather than retrying every non-success response without a limit.
Keep the model and prompt fixed when comparing timing. The latency and tokens-per-second guide explains why first-text delay and end-to-end throughput answer different questions.
Move traffic gradually
Store both configurations as named deployment settings. Send a limited portion of noncritical traffic to the new setting, review errors and spending, then expand only if the results meet your requirements. Keep a rollback configuration available during the transition.
Your OpenRouter key is not an AtmoRouter key. After the migration is complete, remove unused credentials from the application and rotate any key that was exposed during debugging.
For an evaluation before migration, read OpenRouter vs AtmoRouter. For client-specific steps, see Hermes, Cline and OpenCode.