Reliability

Auto failover across providers

When a provider goes down or rate-limits, requests automatically route to backups. Your users never see an error.

Health-checked providers Exponential backoff Per-request routing Structured logs

Visual

Failover path at a glance

How a request moves through primary, retry, and fallback.

01

User request

Incoming API call with payload

02

Health-aware routing

Picks the fastest healthy provider

03

Primary provider attempt

Success when healthy, else retry

04

Retry with backoff

On 429/5xx, try backup provider

05

Fallback succeeds

Healthy provider returns response

06

Trace + metrics logged

Every hop recorded for debugging

Live flow
workflow: "smart_summary"
providers: ["openai:gpt-4o", "anthropic:sonnet", "groq:mixtral"]
on_error: "next_available"
max_attempts: 3
backoff_ms: [400, 800, 1600]
trace_id: req_92f0...
              
1

Detect degraded providers

Health windows decide eligibility before the first call is made.

2

Retry with context

Same payload and routing metadata flow through retries with backoff.

3

See every attempt

Request logs capture provider, latency, tokens, and status for each hop.

Failover speed

~450ms

Median time to recover after a provider 429/500.

Observability

Full trace

Each hop recorded in request logs with tokens & latency.

Control

Per-request

Opt-in or customize retries for specific workflows.

Scroll the playbook

01 · Prepare

Select providers with health scores and prioritize by latency or cost.

02 · Route

Route requests with retry budgets and per-workflow configs.

03 · Recover

Failover to healthy models with exponential backoff.

04 · Observe

Inspect attempts, tokens, and timing in request logs.

When to use

  • Critical user flows that must not 500.
  • Routing across OpenAI, Anthropic, and Groq with cost/latency preferences.
  • Experiments where you want automatic fallbacks without client changes.

What you get

  • Health-aware routing before calls are made.
  • Consistent payloads with structured outputs and streaming.
  • Full transparency via analytics and request logs.

Programmatic access

Call your workflow—failover is configured in the console

POST https://api.modelriver.com/v1/ai
Authorization: Bearer mr_live_your_key

{
  "workflow": "customer-support",
  "messages": [
    { "role": "user", "content": "..." }
  ]
}

// Response shows failover attempts
{
  "data": { ... },
  "meta": {
    "attempts": [
      { "provider": "openai", "status": "error" },
      { "provider": "anthropic", "status": "success" }
    ]
  }
}

Configure primary/fallback providers in your workflow. The API handles retries automatically and reports every attempt in the response.

Ship uptime your users feel

Pair failover with rate limiting, structured outputs, and analytics to keep experiences fast and predictable.