Stop Terminal Retries from Burning Your Token Budget
Your retry loop is part of the bill
The expensive failure is not always an outage. Sometimes your terminal agent waits, retries a long prompt, then switches endpoints and sends the same context again. Three attempts later, you have paid for duplicated work and still have no usable answer.
Treat endpoint health as a routing input, not permission to retry forever. I keep three budgets beside every production request: elapsed time, attempted calls, and estimated spend. A fallback must fit inside all three.
Fluxo gives that routing layer one key and one base URL, https://api.fluxorelay.com, across its model catalog. It works with Claude Code, Codex, Gemini CLI, and any OpenAI SDK. Keep application-side health state separate from client configuration; a shared API surface does not mean every model has identical availability or behavior.
Measure real traffic before buying synthetic probes
Start with passive observations from requests you already make. Record the requested model, task class, status, time to first token, total duration, reported usage, and whether the result passed your application checks. Never log credentials or full prompts by default.
A successful HTTP response is not enough. A coding endpoint that returns empty output or invalid tool arguments is unhealthy for that workflow, even if its status is 200. Conversely, one slow response should not trigger a fleet-wide switch.
- Track transport failures separately from output validation and policy refusals.
- Measure streaming first-token latency separately from completion time and stalls.
- Aggregate observations by model and workload, not merely hostname.
- Expire old observations so yesterday's outage does not control today's routing.
Use synthetic probes only when passive traffic cannot answer the question. During idle periods, send a tiny request with a small output limit, then back off after repeated failures. Add jitter so every worker does not probe simultaneously. A cheap text probe establishes basic reachability, not tool correctness or long-context reliability.
Open a circuit instead of multiplying retries
Implement a circuit breaker in your application or gateway. As a starting configuration, open a model's circuit after five transient failures within thirty seconds, provided you have enough observations to avoid reacting to one unlucky request. Tune these numbers against actual traffic.
While open, skip that route for thirty seconds. Then allow one half-open trial across the shared routing scope. Close after successful trials; reopen and extend the cooldown after another transient failure. Coordinate this state across workers when practical, or each process may independently flood the recovering endpoint.
Classify failures before acting. Authentication errors and malformed requests need configuration fixes, not another paid model call. For rate limits, honor retry guidance when available. A fallback helps only if its quota or failure domain is sufficiently independent.
Give each coding task a fallback contract
Do not route by model name alone. Define the minimum context capacity, tool behavior, output format, and quality your task needs. Test those requirements before adding a route to production.
For example, evaluate Claude Sonnet 5.5 as the primary route for repository edits and DeepSeek V4 Flash as a fallback for bounded tasks. That pairing is an evaluation candidate, not a claim of interchangeable behavior or guaranteed savings. Compare current rates, measured usage, and accepted outputs.
- Allow at most two total attempts within one request deadline.
- Reserve enough time for the fallback before launching another attempt.
- Trim redundant context without removing instructions or required source material.
- Validate structured output before allowing downstream tools to execute actions.
After partial streaming output, do not silently concatenate a replacement answer. Mark the attempt incomplete and restart explicitly. For tools with side effects, use idempotency controls or application deduplication before replaying anything.
Reconcile fallback savings against actual token usage
Fluxo bills per real token, including cache reads and writes, with no monthly fee. Track those categories separately: changing models can sacrifice useful cache reuse, and an apparently cheaper fallback can become expensive after retries or rejected answers.
Review cost per accepted task, not just cost per call. Keep referral credits separate from operating efficiency; Fluxo users earn a referral share when people they invite pay. If you want to measure this with your own workloads, consider creating a Fluxo account. For help, leave a note on the contact page after signing in.