TokelyTHE SHORT ANSWER
Choose an OpenAI-compatible provider by checking the exact endpoints and features your application uses, then evaluating billing, reliability and data handling. Compatibility usually describes part of the request and response format. It does not guarantee every OpenAI feature or identical model behavior.
Which API surface does your application need?
List your actual calls: Chat Completions, Responses, embeddings, streaming, tools, structured output and multimodal input. Include less visible dependencies such as token-count endpoints, file uploads, conversation storage and cancellation.
For each dependency, record whether support is documented, conditional on the model or unavailable. A successful “hello” request tests only a narrow part of the integration.
What should a compatibility check cover?
Use representative request shapes. Include a long conversation, an empty or optional field, a tool-result exchange, a schema-constrained answer and a stream that ends early. Check finish reasons and usage fields as well as generated text.
If the application depends on Responses, verify that endpoint directly. Do not assume that Chat Completions support implies Responses support. Likewise, an embeddings endpoint says nothing about a provider’s chat models.
How do you compare pricing correctly?
Price the same workload by uncached input, cached input, cache writes and output. Add any request, tool or platform charges. Confirm how reasoning tokens and unsuccessful requests are treated.
Ask whether switching routes can change the charge. A model’s headline price may describe a normal route while another eligible supplier costs more. This difference matters during incidents, when fallback volume increases.
What reliability evidence is useful?
Look for published incident information, documented limits and operational support channels. Then measure your own representative workload when you are ready to evaluate. Separate time to first token, total latency, valid-output rate and cost per accepted result.
A public benchmark can help shortlist models, but it cannot establish the reliability of a specific provider path under your production traffic.
What should you verify about data?
Identify which parties receive prompts, whether content is logged, how long records remain and what happens during fallback. Request any contractual commitments your organization needs before sending restricted data. A familiar SDK does not establish a privacy policy.
What does Tokely document?
Tokely supports model-specific Chat Completions and Responses contracts, embeddings, and an Anthropic-compatible Messages endpoint for enabled Claude models. Its migration guide documents limitations including application-managed Responses history and restrictions on file inputs. Tokely migration guide.
Start by matching your application requirements to the model catalog and API docs. Once the contract fits, the OpenAI SDK integration guide shows the client changes.