LLM gateways are becoming a standard part of production AI systems. They sit between applications and model providers, handling routing, retries, observability, caching, and policy enforcement. Early on, the gateway is mostly a convenience layer. Later, it becomes infrastructure.
That transition is where many systems struggle.
This post looks at why LLM gateways often hit performance limits sooner than expected, how language choice plays a central role in that behavior, and why we built Bifrost in Go rather than Python. LiteLLM is a useful reference point here because it represents a common and very reasonable set of tradeoffs.
The early success of Python gateways
LiteLLM is popular for good reasons. It is written in Python, integrates well with the rest of the Python AI ecosystem, and exposes a large surface area of features. For teams experimenting with multiple providers or building early internal tooling, it works well and gets out of the way.
Python is also the fastest way to ship in this space. You can move from idea to working gateway quickly, add support for new providers without friction, and iterate on features with minimal ceremony. For low to moderate traffic, this approach is usually sufficient.
The problem is not correctness or feature coverage. The problem shows up when usage patterns change.
When traffic stops being bursty
Most early benchmarks and tests are burst-oriented. A few concurrent requests. Warm caches. Minimal contention. Under those conditions, many gateways look similar.
Production traffic rarely behaves that way.
Once a gateway sits on the hot path for multiple services, traffic becomes sustained. Concurrency stays high for long periods. Retries overlap with new requests. Providers enforce per-key and per-account limits. Small inefficiencies start to compound.
In Python-based gateways, this often manifests as:
- Increasing tail latency under load
- Higher CPU usage than expected
- Backpressure appearing in unexpected places
- Retry storms amplifying provider slowdowns
These are not bugs in LiteLLM. They are consequences of using a runtime that prioritizes flexibility and developer velocity over predictable concurrency and memory behavior.
Why language choice matters for gateways
LLM gateways are network-bound systems with high concurrency requirements. They spend most of their time waiting on upstream providers while managing large numbers of in-flight requests. In this regime, the cost of scheduling, memory allocation, and context switching matters.
Python excels at orchestration, but it was not designed for sustained high-throughput network services. The GIL, garbage collection behavior, and async scheduling overhead all become visible once concurrency grows.
This is why performance issues in gateways are often misattributed. Teams assume the model is slow, or the provider is degraded, when a meaningful portion of the latency is coming from the gateway itself.
Why they built Bifrost in Go
maximhq
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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…
Bifrost is written in Go because Go is well-suited to this class of problem.
Go’s concurrency model makes it straightforward to manage tens of thousands of in-flight requests without complex async machinery. Goroutines are cheap. Memory behavior is predictable. The runtime is optimized for long-lived network services.
This does not mean Go is universally better than Python. It means Go is a better fit for a gateway that needs to:
- Maintain stable latency under sustained load
- Apply routing and retry logic consistently
- Handle partial failures without cascading slowdowns
- Scale horizontally without surprising behavior
The goal with Bifrost was not to out-feature Python gateways. It was to stay boring under pressure.
Feature velocity versus performance headroom
One of the real tradeoffs in this space is feature velocity.
LiteLLM can ship features faster because Python makes experimentation easy. That is valuable. Many teams need that flexibility early on.
Bifrost takes a different approach. Features are added carefully, with attention to how they affect the request path. The bar for adding complexity is higher because complexity shows up as latency or instability later.
This difference becomes important when gateways move from internal tooling to shared infrastructure.
Performance cliffs are expensive to fix later
The hardest thing to change in a system is its runtime foundation.
Once a gateway becomes critical infrastructure, rewriting it to address performance problems is risky and expensive. Teams often try to patch around the issue by adding more replicas, increasing timeouts, or reducing observability.
Those mitigations help temporarily, but they do not change the underlying behavior.
Choosing a runtime with more performance headroom early on does not guarantee success, but it reduces the number of hard constraints you hit later.
What this means in practice
LiteLLM remains a solid choice for many use cases. It is flexible, accessible, and integrates well with the Python ecosystem. For experimentation and moderate workloads, it does its job.
Bifrost exists for a different phase of the lifecycle. When the gateway itself becomes part of the performance envelope, language choice stops being an implementation detail and starts being an architectural decision.
We built Bifrost to make this layer boring, reliable, and predictable under load. That tradeoff matters once LLM usage stops being a side feature and starts becoming infrastructure.
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