When LiteLLM Becomes a Bottleneck: Exploring Gateway Alternatives

LiteLLM has become a go-to starting point for teams building LLM-powered systems. At first, it feels like magic: a single library that connects multiple providers, handles routing, and abstracts away all the messy differences. For early experiments and small prototypes, it works so well that you barely notice what’s happening under the hood.

But as I started moving a LiteLLM-based system into production, the cracks began to show. Reliability, latency, memory usage, and long-running stability weren’t just minor annoyances anymore  they were walls I kept running into.

I didn’t realize it at first, but LiteLLM alone wasn’t enough for the scale I was aiming for. That’s when I started looking into gateway-based architectures and the different ways teams solve these operational challenges.

Why LiteLLM Is Often the First Choice

LiteLLM solves a real and immediate problem: unifying access to multiple LLM providers behind a single interface. For teams experimenting with OpenAI, Anthropic, Azure, or others, it removes a lot of boilerplate.

It’s especially appealing because:

• It’s provider-agnostic
• It supports logging and routing
• It integrates easily into existing Python-based stacks

For small teams or early prototypes, LiteLLM often works well enough that there’s no reason to look elsewhere.

The issues tend to appear later.

What Starts to Break as Usage Grows

As LiteLLM deployments grow in traffic and uptime expectations, several recurring problems begin to show up. These aren’t theoretical many are reflected in open GitHub issues.

At the time of writing, LiteLLM has 800+ open issues, which is not unusual for a popular open-source project, but it does signal sustained operational complexity.

A few representative examples:

• Issue #12067 – Performance and stability degradation under load
• Issue #6345 – Memory-related issues accumulating over time
• Issue #9910 – Logging and internal state affecting request handling

Individually, each issue can often be worked around. Collectively, they point to a deeper pattern.

Database in the Request Path

One recurring theme is that logging and persistence are tightly coupled to request handling. When a database sits directly in the request path, every call becomes vulnerable to:

• I/O contention
• Locking delays
• Cascading slowdowns during spikes

As traffic increases, this can turn observability ironically into a performance liability.

Performance Degradation Over Time

Another common complaint is that services perform well initially, then slowly degrade:

• Memory usage grows
• Latency becomes inconsistent
• Periodic restarts become necessary to maintain stability

For production systems expected to run continuously, this creates operational overhead and uncertainty.

Predictability Becomes Hard

At small scale, these issues are tolerable. At larger scale, they make capacity planning and SLOs difficult. Teams start compensating with:

• Over-provisioning
• Aggressive restarts
• Disabling features like detailed logging

At that point, the original simplicity starts to erode.

Why These Problems Are Hard to Fix Incrementally

It’s tempting to assume these issues can be patched one by one. In practice, many of them stem from core architectural decisions.

LiteLLM is not primarily designed as a high-throughput, long-running gateway. It’s designed as a flexible abstraction layer. As usage grows, responsibilities accumulate:

• Routing
• Logging
• Persistence
• Retry logic
• Provider normalization

Each additional responsibility increases pressure on the request path.

This is where the gateway model becomes relevant.

Gateway-Based Architectures as an Alternative

A gateway treats LLM access as infrastructure, not just a library. The core idea is separation of concerns:

• Request handling stays fast and minimal
• Logging and metrics are asynchronous
• State is pushed out of the hot path
• Long-lived stability is a first-class goal

This mirrors patterns already established in API gateways, service meshes, and reverse proxies.

Instead of embedding everything into the application runtime, the gateway becomes a dedicated control layer.

Bifrost as a Reference Implementation

Bifrost takes this gateway-first approach seriously. Rather than positioning itself as a drop-in wrapper, it’s designed to sit between applications and LLM providers as a standalone system.

For more detailed documentation and the GitHub repository, check these links:

• Bifrost Docs
• Bifrost GitHub

Several design choices are particularly relevant when contrasting it with LiteLLM.

No Database in the Request Path

One of the most important differences is that Bifrost does not place a database in the request path.

Logs, metrics, and traces are collected asynchronously. If logging backends slow down or fail, requests continue flowing.

The result:

• API latency remains stable under load
• Observability does not penalize throughput
• Failures are isolated instead of cascading

This single decision eliminates an entire class of performance issues.

Consistent Performance Over Time

Bifrost is built to run continuously without requiring periodic restarts. Memory usage is designed to remain stable rather than growing unbounded with traffic.

This matters operationally:

• No “it was fast yesterday” surprises
• Easier autoscaling
• Predictable SLOs

For teams running gateways 24/7, this predictability often matters more than feature breadth.

Stable Memory Usage

Memory leaks and gradual accumulation are some of the hardest production problems to debug. Bifrost’s architecture prioritizes:

• Bounded memory usage
• Clear lifecycle management
• Isolation between requests

That reduces the need for manual intervention and defensive restarts.

Alternatives Worth Considering

The LLM gateway space offers several viable approaches, each optimized for different environments and team needs. Here’s a quick breakdown my top choices:

Bifrost

Strong focus on performance, stability, and gateway fundamentals. Designed for teams that want a dedicated, production-grade LLM control plane.

• High-throughput, low-latency request handling
• Emphasis on reliability and operational stability
• Clear separation between gateway and application logic
• Better suited for backend-heavy or infra-driven teams

Cloudflare AI Gateway

Well integrated into Cloudflare’s ecosystem. A solid option if you’re already using Cloudflare for edge networking and observability.

• Built-in rate limiting, logging, and analytics
• Edge-first architecture with global distribution
• Easy setup for existing Cloudflare users
• Tighter coupling to Cloudflare services

Vercel AI Gateway

Optimized for Vercel-hosted applications. Convenient for frontend-heavy teams but more opinionated in deployment model.

• Seamless integration with Vercel projects
• Optimized for serverless and edge functions
• Minimal configuration required
• Less flexible outside the Vercel ecosystem

Kong AI Gateway

Built on top of Kong’s API gateway. Powerful, but often heavier and more complex to operate.

• Leverages mature API gateway capabilities
• Strong policy, security, and plugin ecosystem
• Suitable for enterprises already running Kong
• Higher operational overhead and learning curve

Each option represents a different balance between control, simplicity, scalability, and ecosystem lock-in there’s no universal “best,” only what fits your stack and team maturity.

Choosing the Right Tool Based on Scale

LiteLLM is often a good choice when:

• You’re experimenting or prototyping
• Traffic is low to moderate
• You value flexibility over predictability

Gateway-based solutions make more sense when:

• Traffic is sustained and growing
• Latency and uptime matter
• You want observability without performance penalties
• You need long-running stability

Neither approach is universally “better.” They serve different stages of maturity.

Final Thoughts

LiteLLM plays an important role in the ecosystem, and its popularity reflects that. But as systems scale, architectural assumptions start to matter more than convenience.

Gateway-based solutions exist because teams consistently run into operational limits with long-running, high-throughput LLM workloads. Whether it’s Bifrost, Cloudflare AI Gateway, Vercel AI Gateway, or Kong AI Gateway, these platforms provide a predictable control layer, stable performance, and observability without slowing down requests.

If LiteLLM is starting to feel like a bottleneck rather than an enabler, that’s usually a signal not that you chose the wrong tool, but that your system has outgrown it.

At that point, evaluating gateway-based alternatives isn’t premature. It’s practical, and it helps you scale with confidence.