Goethe University AI-ToolLab LLM API
Operated by studiumdigitale, Goethe University Frankfurt
A recurring no-cost API allowance covering 17 cataloged models.
https://litellm.s.studiumdigitale.uni-frankfurt.de/v1/Models mentioned
gpt-oss:20bgemma3:12bllama3.1:8bllama3:8bllama2:7bmistral:7bcodellama:7bollama_defaultall-minilm:33mbge-large:335mbge-m3:567mgranite-embedding:278mmxbai-embed-large:335mnomic-embed-text:v1.5paraphrase-multilingual:278msnowflake-arctic-embed2:568msnowflake-arctic-embed:335mEquivalent paid value
How this was valued: Each approved user starts with a €50 introductory budget for paid routes and is restricted rather than charged when exhausted; ongoing local/GWDG fair-use value remains unquantifiable.
Limits and terms
- Local Models
- free fair use
- Gwdg Models
- free fair use dynamic
- Paid Model Introductory Budget Eur
- 50
- Exhausted Behavior
- Access is restricted; user is not charged.
- Caveat
- Azure routes are excluded from the ongoing-free catalog.
What happens to your prompts?
The gateway says prompts are never stored and logs metadata only, but GWDG-routed handling and deletion controls are not fully documented.
- Plan Scope
- Goethe AI-ToolLab gateway, local routes, and qualifying GWDG fair-use routes.
- Prompt Retention
- The gateway says prompts are never stored.
- Response Retention
- An equally explicit response-retention statement is absent; GWDG routes inherit GWDG handling.
- Ordinary Logging
- Endpoint/model, timestamp, input/output token counts, success status, and access-account data are logged without prompt text.
- Model Training
- No gateway-specific rule is stated; GWDG qualifying routes separately exclude training.
- Product Improvement
- Content-based improvement is not documented.
- Human Or Operator Access
- Stored metadata is administratively accessible; transient troubleshooting is not fully described.
- Subprocessors And Routing
- Local routes use Goethe infrastructure and GWDG routes use Academic Cloud.
- Deletion Controls
- Prompt storage is excluded, but response, metadata, account, and upstream deletion controls are incomplete.
Eligibility
- Account Required
- Yes
- Eligible Users
- Goethe University employees
- Requirements
- valid HRZ accountcompleted EU AI Act training badgeapproved email request
Primary sources
Before you build with Goethe University AI-ToolLab LLM API
Read the classification narrowly
This record has current first-party evidence for some zero-cost hosted inference. The label describes the bounded offer supported by evidence on 2026-09-19; it does not guarantee permanence, production suitability, uptime, latency, model quality, or access from every region.
Resolve the live model route
This snapshot records 17 model IDs. Match the exact ID against the provider's live catalog before using it in code; zero-price routes and aliases can rotate while an older documentation page remains online. The recorded base endpoint is https://litellm.s.studiumdigitale.uni-frankfurt.de/v1/, but the provider's current API reference remains authoritative for paths, authentication, and request shape.
Confirm account and billing boundaries
An account is required, so public catalog evidence cannot by itself prove the limits, balance, or billing behavior attached to your workspace. The payment-card requirement is conditional or not clearly documented. Treat signup friction and billing exposure as unresolved until the account flow confirms them. The equivalent paid value shown here prices a documented allowance where possible; it is not cash, guaranteed savings, or protection from overage.
Re-read the prompt policy for your plan
This record classifies the reviewed handling as “Partially private”: The gateway says prompts are never stored and logs metadata only, but GWDG-routed handling and deletion controls are not fully documented. Provider policies can distinguish free, paid, enterprise, opted-in, and feature-specific traffic, so verify the governing terms for the exact account and route that will receive your data.
Follow the evidence, then re-check it
The record links 2 first-party sources covering the offer, catalog, limits, pricing, terms, privacy, or adoption evidence available to the audit. Prefer the newest governing document or live catalog when sources disagree, and submit a correction when a provider changes a material term.
Match the quota to the workload shape
Translate the published allowance into the traffic pattern you actually expect instead of comparing headline totals alone. A daily token pool can look generous while a low requests-per-minute or concurrency ceiling blocks interactive bursts; a high request limit can still fail a long-context job when input, output, or per-request tokens are capped. Separate prompt tokens from generated tokens, include retries and tool calls, and test the largest realistic payload. If the provider documents more than one limit window, the tightest window at your peak load is the practical ceiling. Research, experimental, and community access can also carry eligibility or fair-use constraints that cannot be modeled as a simple number.
Plan fallback without changing the rules
A fallback should preserve more than API syntax. Confirm that the substitute route supports the required modality, context length, streaming behavior, structured output, tools, and safety controls, then compare its prompt-retention and training terms. An OpenAI-compatible request shape does not make providers operationally or contractually equivalent. Decide which errors may trigger a retry, cap retry storms, and prevent an exhausted free route from silently switching to a billable model. If deterministic output matters, record the model revision and sampling settings; rotating aliases and free-model pools can change behavior even when the endpoint remains available.
Monitor the offer as a dependency
Capture the model ID, response model field, rate-limit headers, usage fields, latency, HTTP status, and any provider request identifier for each test. Watch for authorization failures, quota exhaustion, catalog removal, policy revisions, and dashboard balance changes as separate failure modes. Re-check the provider’s live catalog and governing pages on a schedule proportionate to the workload’s importance, and keep the dated sources that supported your decision. Free capacity is especially suitable for prototypes, evaluations, fallbacks, and bounded workloads when the application can tolerate change; a production dependency still needs observability, an exit path, and an owner responsible for re-verification.
Test one complete request before scaling
Start with the smallest permitted request using the exact credential, model ID, endpoint, and account type you intend to deploy. Record the HTTP status, response headers, usage fields, latency, and any dashboard balance change. Then exercise the failure path: an invalid model, an exhausted quota, or a rate-limit response should fail clearly without silently switching to a paid route. If the provider supports streaming or tool calls, validate those features separately because a free model can expose a narrower capability set than its paid counterpart. Keep a budget ceiling outside the application whenever billing is possible, and avoid sending sensitive data until the observed route matches the reviewed data agreement.