ModelGate

This is awesome, cost predictability is a serious problem, gonna try this and see if it catches any redundant waste

@raz If I ask the same question to a bot thousands of times, can ModelGate’s cache serve it from its cached response and show me the savings?

How does the auto-downgrade routing work in production? If my code accidentally routes a 20-word classification task to GPT-4, can ModelGate step in and downgrade it to the mini model on the fly before the provider is billed?

Swapped the base URL in my test repo just to play and it already caught a repetitive loop I didn't even realize my bot was running.

Everyone worries about prompt injection on the way in, but the real nightmare is the model hallucinating and leaking an internal AWS key or customer PII on the way out. Having an outbound scanner that actually intercepts and redacts secrets before the response hits the user's browser is a safety net for production apps. Does the entropy scanner catch custom internal token formats well?

Does it stable for startup?

Waste breakdown chart showing repeated prompts and large contexts makes it much easier to see where the usage can be improved. It gives a quick view of what needs attention without going through every request. Nice work by the team on making this so easy to understand. Hope the launch goes well❤️

How does the consistency score and regression gate work with paraphrased prompts? Can I integrate the active tests into my CI to catch bot drift?

Setting a monthly spend cap (with 402 errors beyond) is a great way to avoid unexpected costs – I once accidentally consumed 100k tokens in an hour!

Looks interesting, especially as prices can easily skyrocket. Will try it out!

Managing 3 different SDKs and billing portals for OpenAI, Anthropic and Google is a massive headache for FinOps. Routing them all through a single gateway to get a unified spend view is exactly how multi-model stacks should be handled. Since each provider calculates and prices tokens slightly differently, does ModelGate normalize the token math across the dashboard or does it just log the raw counts exactly as the provider returns them?

Any plans to add support for other LLMs (like AWS Bedrock) or real-time alerting on spend anomalies? BTW, Congrats on the launch! Wishing you guys lots of users and a great journey ahead.

Repeated prompts can quietly waste tokens when the same thing keeps getting sent again and again. Its pretty useful that ModelGate can flag this as “Repeated Prompt” waste and suggest using a cache to cut down those extra calls :D

Token-by-token billing is cool – does the dashboard break down spend by model and token count so I know exactly where each penny went?

Congrats on the launch! Just to confirm, if I switch my OpenAI SDK’s base URL to ModelGate and add the x-api-key, will all my existing code calls be logged and priced automatically?