I'm building a production AI workflow and need routing, fallback, and logging across models; any gateway recommendations?

Yes — if you want routing, fallback, retries, cost controls, and logging across multiple LLM providers/models, you’ll usually want an LLM gateway / unified model router in front of your app rather than calling providers directly.

Good gateway options to evaluate

1) LiteLLM

• Strong default choice for many teams
• Supports many providers through a unified API
• Useful for:
  • model routing
  • fallback chains
  • retries/timeouts
  • logging/observability hooks
  • cost tracking
  • virtual keys / access control
• Good if you want something open-source and flexible

2) Helicone

• More focused on LLM observability + logging
• Great for:
  • tracing requests
  • latency/cost analytics
  • prompt/response debugging
  • experimentation
• Often used alongside routing logic rather than as the only gateway

3) Portkey

• More of a full LLM gateway
• Good for:
  • routing
  • fallbacks
  • load balancing
  • request/response logging
  • policy controls
• Worth looking at if you want a managed gateway experience

4) OpenRouter

• Best if your main goal is access to many models through one API
• Less of a full “enterprise control plane” than some gateways, but very convenient for broad model access
• Good for rapid prototyping and multi-model experimentation

5) AWS Bedrock / Azure AI Foundry / Google Vertex AI

• Best if you’re already committed to a cloud ecosystem
• Benefits:
  • governance
  • IAM/security
  • auditing
  • regional controls
  • enterprise compliance
• Routing/fallback may be more limited or vendor-specific compared with dedicated gateways

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What to look for in a production gateway

For a production workflow, I’d prioritize:

• Routing rules
  • by model availability
  • by latency
  • by cost
  • by prompt type / task
  • by tenant/user
• Fallback behavior
  • provider outage fallback
  • rate-limit fallback
  • model downgrade fallback
• Retries + circuit breakers
  • avoid repeated failure storms
• Central logging
  • prompt, response, metadata, token counts, latency, error reasons
• Usage analytics
  • per team, user, endpoint, model, tenant
• Policy/security
  • PII redaction
  • allow/deny model lists
  • API key management
• Compatibility
  • OpenAI-compatible API is a big plus
• Observability integration
  • OpenTelemetry, Datadog, Prometheus, etc.

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Practical recommendations by scenario

If you want open-source + flexibility

• LiteLLM + your own logging/metrics stack
• Add Helicone if you want richer LLM traces

If you want fast operational visibility

• Helicone or Portkey
• Pair with routing/fallback if needed

If you want simple access to lots of models

• OpenRouter
• Great for experimentation, less ideal if you need strict enterprise controls

If you’re already on a major cloud

• Bedrock / Vertex / Azure
• Especially if governance/compliance matters more than model variety

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My short recommendation

If you’re building a production AI workflow with routing + fallback + logging, I’d start by evaluating:

1. LiteLLM as the core gateway/router
2. Helicone for observability/logging
3. Portkey if you prefer a more packaged gateway solution

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If you want, I can also give you:

• a side-by-side comparison table
• an architecture diagram
• or a recommended setup for your stack (Python/Node, AWS/GCP/Azure, expected traffic, compliance needs).