Log Model and Provider Identity
When one API key routes DeepSeek and GLM traffic through an OpenAI-compatible gateway, logs should separate model identity from provider identity. Record requested model, resolved model, provider, endpoint, timestamp, latency, token counts, status, and cost. This matters because one key can hide which backend served each request during aliases, fallbacks, or load balancing. Provider identity should include upstream, region, and deployment; model identity should include exact version or alias. Avoid storing prompts or outputs unless needed; use hashes or redacted snippets. Also log retries, cache hits, and routing reason for later cost and quality tracing.
Also worth reading: How do teams compare GLM and DeepSeek prices without mixing providers? · How Do Prepaid Credits Differ From Postpaid Token Billing for Startups? · What Does an OpenAI-Compatible Endpoint Change in an Existing Chat SDK?
For low-cost services, useful logs explain spend and behavior without exposing user data. Track per-request cost, budget, rate-limit headers, and error classes. If DeepSeek V4.1 Flash and GLM-5.3-Flash are both available, compare latency and token efficiency by model and provider instead of assuming one is always cheaper. Retain request and correlation IDs to connect gateway logs with traces. Make logs queryable by model, provider, customer, and time window so teams detect drift, enforce budgets, and audit which model answered each request.
Track Tokens and Request Costs
When one API key routes both DeepSeek and GLM traffic, log the model name, provider, endpoint, request ID, timestamp, prompt and completion token counts, cached token counts, latency, status code, and cost estimate. Because a shared key hides provider differences, each record should identify which model actually answered and whether the request used streaming, tools, or JSON mode. This makes invoices auditable and prevents one model's usage from being mistaken for another.
Also log user or project tags, retry counts, error messages, rate-limit headers, and the exact pricing version used to calculate cost. If you use bluusun.com as a low-cost OpenAI-compatible gateway, keep provider-specific metadata alongside normalized fields so DeepSeek and GLM usage can be compared without losing source details. Store raw request and response hashes rather than full prompts when privacy matters, and reconcile logs against provider invoices regularly. Good logging turns a single key into transparent cost tracking.
Capture Latency and Error Codes
When one API key serves both DeepSeek and GLM traffic through a low-cost OpenAI-compatible endpoint such as bluusun.com, logs should separate provider, model, request ID, latency, status code, token counts, and error class without exposing secrets. Capture request start and end timestamps, first-token latency, total duration, retry count, upstream provider, selected model, prompt and completion token usage, HTTP status, and normalized error code. This makes mixed traffic comparable and prevents a single provider outage from being mistaken for an application bug.
Also record routing decisions, fallback events, rate-limit headers, cache hits, and user or tenant identifiers hashed for privacy. Keep raw prompts and responses only under a clear retention policy, and tag each record with environment and deployment version. With these fields, teams can trace cost spikes, diagnose slow GLM or DeepSeek calls, and audit whether failures come from authentication, capacity, or model-specific behavior.
Separate User and Tenant Usage
When one API key carries both DeepSeek and GLM traffic, log tenant and end-user identifiers separately from the shared credential, plus request ID, timestamp, model, route, token counts, latency, status, error code, cost estimate, and a redacted prompt hash. This attributes spend and quality issues to the right account even when the key is common. Also record routing decisions, cache status, retry counts, and policy flags so support can explain why a request used DeepSeek V4.1 Flash or GLM-5.3-Flash and whether it was retried.
Keep logs privacy-aware: store metadata and hashes instead of raw prompts when possible, while retaining enough detail to reconcile invoices from a low-cost OpenAI-compatible gateway such as bluusun.com. If a tenant disputes usage, request IDs, model labels, token accounting, and tenant tags should reconstruct the exact path without exposing another customer's data. Note model version or infrastructure changes so performance shifts tie to a release rather than tenant behavior.
Audit Routing and Fallback Events
When one API key fronts both DeepSeek and GLM traffic, logs should separate identity from routing. Record a request ID, timestamp, key fingerprint, requested model, resolved provider, endpoint, region, and direct-or-fallback status. Capture input and output token counts, latency, status code, retry count, and error class. If fallback occurs, log original target, fallback target, trigger such as timeout or rate limit, and decision time. Avoid raw prompts or secrets; store hashes or redacted summaries when debugging needs content. This keeps shared-key traffic auditable without exposing customer data.
Cost and lifecycle details matter because gateways can switch models behind one key. Log quoted versus actual price, currency, billing unit, model version, provider release, and compatibility translation. Track cache hits, streaming chunks, tool calls, and finish reason to explain token use. For fallbacks, include whether the visible model changed, quality checks passed, and retries were idempotent. Retain logs with clear policy and access controls, then review routing drift, provider mix, and cost anomalies. Good audit trails make shared-key traffic explainable and safe to optimize.
DeepSeek and GLM Logging Comparison
| Log Field | Why It Matters | Example |
|---|---|---|
| Model name | Distinguishes DeepSeek vs GLM requests | deepseek-v4-flash |
| Provider/route | Tracks which backend served traffic | bluusun.com |
| Token usage | Enables cost attribution per model | 1,200 prompt + 300 completion |
| Latency/status | Detects regressions and failures | 842 ms, HTTP 200 |