Last month, our AI infrastructure costs tripled overnight. One team spun up an experimental feature using GPT-4, forgot to set limits, and by morning we'd burned through our entire monthly budget. The worst part? Our existing API gateway had no idea it was happening.
Traditional API gateways were built for REST endpoints, not AI workloads. They track requests per second, but a single API call might consume 10 tokens or 10,000 tokens. Request-based metrics become meaningless when cost varies by 1000x between calls.
If you're running AI in production, you've hit this wall. Traditional infrastructure can't give you visibility into actual costs or compliance risks. You need governance built specifically for LLMs.
The AI Governance Problem Nobody Talks About
Cost spirals happen silently. Your monitoring shows healthy request rates while token consumption; and your bill explodes. You find out after you've spent thousands on a poorly optimized prompt.
Compliance violations are invisible. Someone sends PII to an external LLM provider. Your API gateway logged the request but has no concept of what data passed through. You discover this during the security audit.
Multi-tenant isolation breaks down. Different teams need different model access, budgets, and rate limits. Traditional gateways force you to provision separate infrastructure or build custom middleware.
Provider failures cascade. Your primary LLM provider throttles or goes down. Your gateway has retry logic but doesn't understand that GPT-4 and Claude can serve similar requests. Failover requires rewriting application code.
What AI Governance Actually Requires
Token-aware cost tracking. Track costs at the token level; input vs output tokens, different pricing across models, and cache hits that change the cost equation.
Hierarchical budget controls. Real organizations need organization-wide caps, team-level allocations, and per-application limits. These budgets must be checked in real-time before requests execute.
Content-aware security. Detect PII in prompts, identify prompt injection attempts, and enforce content policies before data reaches external providers.
Intelligent routing and fallbacks. Automatic failover that understands model equivalence, routing between GPT-4 and Claude without application changes.
Audit trails that matter. Immutable records of who accessed which models, what data was sent, what policies were enforced, and what happened when violations occurred.
How Purpose-Built LLM Gateways Solve This
I recently discovered Bifrost while researching solutions to our cost explosion problem. It's an open-source LLM gateway that actually understands AI workloads, and it's been a game-changer for our infrastructure.
maximhq
/
bifrost
Fastest LLM gateway (50x faster than LiteLLM) with adaptive load balancer, cluster mode, guardrails, 1000+ models support & <100 µs overhead at 5k RPS.
Bifrost
The fastest way to build AI applications that never go down
Bifrost is a high-performance AI gateway that unifies access to 15+ providers (OpenAI, Anthropic, AWS Bedrock, Google Vertex, and more) through a single OpenAI-compatible API. Deploy in seconds with zero configuration and get automatic failover, load balancing, semantic caching, and enterprise-grade features.
Quick Start
Go from zero to production-ready AI gateway in under a minute.
Step 1: Start Bifrost Gateway
# Install and run locally
npx -y @maximhq/bifrost
# Or use Docker
docker run -p 8080:8080 maximhq/bifrost
Step 2: Configure via Web UI
# Open the built-in web interface
open http://localhost:8080
Step 3: Make your first API call
curl -X POST http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "openai/gpt-4o-mini",
"messages": [{"role": "user", "content": "Hello, Bifrost!"}]
}'
That's it! Your AI gateway is running with a web interface for visual configuration, real-time monitoring…
Here's what makes it different:
Virtual keys as governance primitives. Instead of sharing provider API keys across applications, Bifrost uses virtual keys that act as access control boundaries. Each virtual key can have its own budget, rate limits, model access restrictions, and provider configurations. When a key hits its budget, requests stop; before you waste money.
Hierarchical budget enforcement. You can set budgets at the customer level (organization-wide), team level (department budgets), and virtual key level (per-application). Bifrost checks all applicable budgets before executing a request. If any budget is exceeded, the request is blocked. After successful completion, costs are deducted from all levels simultaneously.
Real-time rate limiting on tokens and requests. You can set both request-per-minute limits and token-per-hour limits. If one provider config hits its rate limit, Bifrost automatically excludes it from routing while other providers remain available. This prevents cascading failures while maintaining cost controls.
Automatic failover with health monitoring. Bifrost monitors provider health through success rates, response times, and error patterns. When a primary provider fails, it transparently routes to fallback providers without application changes. The system handles retries with exponential backoff, so transient failures don't become outages.
Enterprise audit logging. Every request generates immutable audit logs with cryptographic verification. The system logs authentication events, authorization decisions, configuration changes, security events, and data access patterns. These logs support SOC 2, GDPR, HIPAA, and ISO 27001 compliance requirements.
Content security with guardrails. Bifrost integrates with AWS Bedrock Guardrails, Azure Content Safety, and Patronus AI to provide real-time content filtering. You can detect and redact 50+ types of PII, block prompt injection attempts, filter harmful content, and enforce custom policies; all before data reaches external providers.
What surprised me most was the performance. Bifrost is written in Go, not Python, which means it adds only 11 microseconds of overhead at 5,000 requests per second. For context, that's 50x faster than Python-based alternatives like LiteLLM. When you're running high-throughput AI applications, that performance difference matters.
How This Compares to Alternatives
If you're evaluating LLM gateways, you'll likely encounter options like LiteLLM and Portkey. LiteLLM has been popular for multi-provider routing, but teams increasingly report performance issues at scale due to its Python implementation. The setup also requires YAML configuration and more infrastructure management expertise.
Portkey offers comprehensive enterprise features but comes with corresponding complexity and pricing. For teams that need fast, reliable AI infrastructure with governance but don't require extensive enterprise features, it might be more than necessary.
Getting Started
The nice part about Bifrost being open-source is you can try it immediately without sales calls or procurement processes. Deployment takes less than 30 seconds:
# Using NPX
npx -y @maximhq/bifrost
# Or Docker
docker run -p 8080:8080 maximhq/bifrost
The web UI provides visual configuration for virtual keys, budgets, and providers. You don't need to write config files unless you want infrastructure-as-code for production deployments.
For teams already running containerized infrastructure, Bifrost integrates cleanly into existing Kubernetes clusters or Docker Compose setups. The gateway exposes standard OpenAI-compatible endpoints, so you can swap it in without changing application code. Just point your existing OpenAI client at Bifrost's URL, add a virtual key header, and you immediately get governance controls.
After our cost explosion incident, we implemented Bifrost with hierarchical budgets across all our teams. Three months in, we've had zero budget overruns, automatic failover has handled multiple provider outages without incidents, and our compliance team finally has the audit trails they need. The performance overhead is negligible; our P99 latency actually improved slightly because the intelligent routing avoids slow providers.
If you're building AI infrastructure and struggling with governance, check out Bifrost on GitHub. The repository includes documentation, deployment examples, and an active community. It's worth spending 30 seconds to see if it solves your problems too.
Have you dealt with AI governance challenges? What solutions have worked for your team? I'd love to hear your experiences in the comments.
Top comments (0)