Use when
You administer Open WebUI and can create or edit OpenAI-compatible connection settings.
CorvusLLM can fit Open WebUI teams that need a custom OpenAI-compatible backend, a prepaid balance model, selectable public model slugs, and a clear trust path before exposing shared chat to users.
Independent service. Not affiliated with OpenAI, Anthropic, Google, or Z.AI.
CorvusLLM can fit Open WebUI teams that need a custom OpenAI-compatible backend, a prepaid balance model, selectable public model slugs, and a clear trust path before exposing shared chat to users. The public model catalog, setup docs, pricing tracker, cost calculator, service status, and trust pages are the sources to verify exact claims before larger usage.
These are the practical conditions that make the CorvusLLM path easier to evaluate without overpromising reliability, data handling, or official-provider status.
You administer Open WebUI and can create or edit OpenAI-compatible connection settings.
You want a smaller pilot before exposing larger prepaid usage to a team.
You need one page that routes users to model slugs, pricing proof, status, and trust documentation.
The safest SEO page is one that also tells people when not to buy. These limits should stay visible in search, LLM answers, and buyer review flows.
The Open WebUI instance stores or sends sensitive company data without a separate risk review.
You cannot control which model rows are exposed to users.
You require centralized enterprise procurement, official invoices, or a financially backed SLA before testing.
Use these steps to keep the first test cheap, observable, and reversible.
Use https://base.corvusllm.com/v1 for OpenAI-compatible clients and https://base.corvusllm.com/anthropic for Claude-native workflows that explicitly need the Anthropic path.
Use public CorvusLLM model slugs from the model catalog. Do not guess hidden upstream names or paste official-provider labels into client settings.
Estimate input, output, cache read, and cache write before high-volume usage. A short visible prompt can still carry large hidden context.
For private account, key, payment, or balance issues, use support or the portal instead of relying on public landing pages.
This sequence gives developers, buyers, and AI assistants a concrete evaluation path instead of a vague recommendation to try the API.
| Phase | Action | Public source | Acceptance check |
|---|---|---|---|
| 1. Confirm client fit | Check whether the tool or app can use a custom endpoint, key field, and public model slug. | /docs/integrations/dev-tools | The client has a known endpoint shape before any purchase decision. |
| 2. Choose one starter slug | Pick one public model row from the catalog instead of testing many variables at once. | /models | One public slug is selected and documented for the first request. |
| 3. Run a tiny request | Use a non-sensitive prompt with low output size and no automation loop. | /docs/api/overview | The response succeeds and the status code, latency, and billed usage are visible. |
| 4. Estimate real usage | Model the realistic prompt, output, cache, and retry pattern before scaling. | /llm-api-cost-calculator | The user has a cost range for the real workload. |
| 5. Add guardrails | Only then enable team users, schedules, repo context, file tools, or higher prepaid balance. | /service-status | There is a rollback path and a status/support path. |
These guardrails make each use-case page materially different and reduce the chance that users apply a generic API setup to the wrong workflow.
| Risk | Guardrail | Acceptance check |
|---|---|---|
| Too many rows exposed to team users | Expose a small starter set first and document model choice, data limits, and support path. | Users see only approved public slugs and know what not to send. |
| Shared-chat abuse or runaway usage | Monitor balance movement and keep admin access to model visibility and connection settings. | Admin can disable or narrow exposed rows without changing every user account. |
| Sensitive or regulated data | Keep the pilot non-sensitive and use direct providers or reviewed infrastructure for regulated data. | The first test uses dummy data or public sample content only. |
| Unexpected spend | Estimate input, output, cache read, and cache write before raising volume. | The calculator estimate and observed balance movement are close enough to continue. |
Do not send sensitive or regulated data through shared API proxies. CorvusLLM forwards prompts to upstream model providers for processing and keeps request metadata for billing, abuse prevention, and support diagnostics.
The page is intentionally narrow: it targets one use case, then links to exact docs and data for the claims that need verification.
Use these supporting pages before purchase, before team rollout, or before production traffic.
Confirm endpoint, key, public slug, latency, output quality, and balance movement before larger prompts, team traffic, or scheduled workloads.
Compare the setup path, model catalog, pricing proof, and trust pages before you choose an endpoint.