What should I use for an internal AI gateway for my engineering team?

For an internal AI gateway for an engineering team, I’d usually optimize for:

1. API compatibility across many model providers
2. Centralized auth, quotas, logging, and policy
3. Low latency and reliability
4. Easy model/provider switching
5. Support for chat, embeddings, image, and tool/function calling
6. Privacy controls for prompts, outputs, and secrets

Good options

1. LiteLLM Proxy

Best default choice for many engineering teams.

Why it’s good:

• OpenAI-compatible API surface
• Supports many providers: OpenAI, Anthropic, Azure OpenAI, Bedrock, Gemini, Cohere, local models, etc.
• Centralized:
  • auth
  • rate limits
  • budgets
  • routing/fallbacks
  • logging
  • model aliases
• Works well as an internal “one endpoint” abstraction

When to pick it:

• You want to standardize on one internal API
• You have multiple model vendors
• You want to avoid locking apps to one provider

Tradeoff:

• You’ll still need to operate and secure it properly
• Some advanced enterprise governance features may need extra work around it

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2. Cloud provider gateway

Examples:

• AWS Bedrock
• Azure AI Foundry / Azure OpenAI
• Google Vertex AI / Gemini

Why it’s good:

• Strong enterprise controls
• Good IAM integration
• Easier compliance story if you already live in that cloud
• Fewer moving parts

When to pick it:

• Your team is mostly on one cloud
• You’re okay with using that cloud’s model ecosystem
• You want simpler ops over maximum flexibility

Tradeoff:

• Less portable
• Provider-specific APIs and features can leak into app code
• Multi-provider orchestration is more awkward

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3. Commercial AI gateway / control plane

Examples vary, but these tools often provide:

• unified API
• policy enforcement
• observability
• prompt management
• evals
• secrets handling
• caching

When to pick it:

• You need enterprise governance fast
• You have security/compliance requirements
• You want a supported product rather than self-hosting

Tradeoff:

• Cost
• Vendor lock-in
• Sometimes less flexible than rolling your own proxy

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My practical recommendation

If you want the most balanced internal gateway:

Use LiteLLM Proxy in front of your model providers.

It gives you:

• one internal API
• model routing
• failover
• budgeting/quotas
• provider abstraction

Then add:

• SSO / auth middleware
• request/response logging with redaction
• rate limits per team/service
• audit trail
• secret scanning / prompt redaction
• metrics + tracing

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A solid architecture

Apps / services
→ Internal AI Gateway
→ Model providers
→ logging/metrics/evals

Gateway responsibilities:

• authenticate callers
• authorize model access
• normalize requests
• enforce policies
• route to providers
• retry/fallback
• cache where appropriate
• capture cost/usage
• redact sensitive data

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What I’d avoid

• Pointing every app directly at OpenAI/Anthropic/etc.
• Building a custom gateway from scratch unless you have strong platform needs
• Using a gateway that can’t do provider failover or cost controls
• Letting teams embed vendor-specific APIs directly into core services

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If you tell me these 4 things, I can recommend a specific stack

1. Which cloud are you on? (AWS/Azure/GCP/multi-cloud)
2. Do you need self-hosting / VPC-only?
3. How many teams/apps will use it?
4. Do you need just inference proxying, or also prompt management/evals/guardrails?

If you want, I can also give you a “buy vs build” decision matrix or a reference architecture for a self-hosted internal AI gateway.