LiteLLM Alternative: Why Go Developers Are Switching

LiteLLM has become one of the most popular AI API gateways, with over 57,000 GitHub stars and a thriving community. It’s Python-based, open-source, and self-hostable — a solid choice for many teams. But for Go developers and performance-sensitive applications, LiteLLM’s Python foundation introduces friction. This article explores why Go-based alternatives like Bring AI are gaining traction, with a detailed comparison and migration guide.

LiteLLM Overview: Strengths and Limitations

What LiteLLM Does Well

LiteLLM, created by BerriAI, has earned its 57K GitHub stars for good reasons:

• 100+ model integrations: Supports OpenAI, Anthropic, Google, Cohere, Azure, AWS Bedrock, and many more.
• OpenAI SDK compatibility: Use the OpenAI SDK with any provider.
• Self-hosted: Run it on your own infrastructure for full control.
• Proxy server mode: LiteLLM Proxy provides a unified API endpoint.
• Cost tracking: Built-in spend management and budget alerts.
• Fallback support: Automatic failover between providers.
• Community: Active community, extensive documentation, regular updates.

Where LiteLLM Falls Short for Go Teams

Despite its strengths, LiteLLM has several limitations that become apparent in production, especially for Go-centric teams:

1. Python Runtime Overhead

LiteLLM runs on Python, which means:

• Higher memory usage: A LiteLLM proxy instance typically uses 200-500MB of RAM, compared to 20-50MB for a Go-based equivalent.
• GIL limitations: Python’s Global Interpreter Lock constrains true parallelism, affecting throughput under heavy concurrent load.
• Slower startup: Python processes take longer to start, complicating serverless deployments.
• Dependency management: Python’s packaging ecosystem (pip, virtualenv, conda) adds complexity compared to Go’s single-binary deployment.

2. No Native Go SDK

If your application is written in Go, you must either:

• Use the OpenAI Go SDK pointed at LiteLLM’s proxy (works, but adds an extra network hop)
• Write HTTP client code manually (tedious and error-prone)
• Use LiteLLM’s Python SDK via subprocess or gRPC (architecturally awkward)

There’s no first-class Go SDK for LiteLLM.

3. Performance Under Load

Python’s async I/O (asyncio) is competent but not as efficient as Go’s goroutines for handling thousands of concurrent connections. Under high load, LiteLLM’s throughput can plateau, requiring horizontal scaling earlier than a Go-based solution.

4. Deployment Complexity

Deploying LiteLLM requires:

• Python runtime + dependencies
• Redis (for distributed rate limiting and budget tracking)
• PostgreSQL (for logging and analytics)
• A WSGI/ASGI server (Uvicorn, Gunicorn)

A Go-based gateway deploys as a single binary with no external dependencies.

Why a Go-Based AI API Gateway Matters

Go (Golang) has become the language of choice for infrastructure and networking tools, and for good reason:

Performance

Go compiles to native machine code, offering performance comparable to C/C++ with the developer experience of a modern language. For an API gateway that proxies thousands of requests per second, this translates to:

• Lower latency: Go’s efficient HTTP handling and goroutine-based concurrency minimize per-request overhead.
• Higher throughput: Go servers can handle 10-100x more concurrent connections per CPU core than Python.
• Predictable performance: No GIL, no JIT warmup, no garbage collection spikes (Go’s GC is sub-millisecond for most workloads).

Deployment Simplicity

Go compiles to a single static binary. No runtime, no dependencies, no virtual environments:

# Go-based gateway (e.g., Bring AI) — Dockerfile
FROM scratch
COPY bring-ai /bring-ai
CMD ["/bring-ai"]
# Image size: ~15MB

# LiteLLM — Dockerfile
FROM python:3.12-slim
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["uvicorn", "litellm_proxy:app", "--host", "0.0.0.0"]
# Image size: ~400MB+

Operational Efficiency

For DevOps teams, Go services are easier to operate:

• Single binary: No dependency conflicts, no pip hell.
• Cross-compilation: Build for Linux/amd64, linux/arm64, darwin from any platform.
• Embedded resources: Configuration, templates, and even databases can be embedded in the binary.
• Low resource footprint: Run more instances per server, reducing infrastructure costs.

Concurrency Model

Go’s goroutines are lightweight (2KB stack initial size) compared to OS threads (1-8MB). This means a Go-based gateway can handle hundreds of thousands of concurrent connections without breaking a sweat:

// Go handles each request in a goroutine — efficient and simple
func handleRequest(w http.ResponseWriter, r *http.Request) {
    go proxyToBackend(r, w)  // 2KB overhead per goroutine
}

Python’s asyncio achieves concurrency through event loops, which work well but require explicit async/await syntax throughout the codebase and can’t match Go’s model for CPU-bound tasks.

Bring AI vs LiteLLM Comparison

When to Choose LiteLLM

• You need a self-hosted, open-source solution.
• Your team is Python-centric and comfortable with the ecosystem.
• You want full control over the gateway code and configuration.
• You need custom middleware and are willing to write Python.

When to Choose Bring AI

• You want a managed service with zero infrastructure to maintain.
• Your team uses Go or values low-latency, low-resource infrastructure.
• You need both OpenAI and Anthropic SDK compatibility from one gateway.
• You want flexible payment options (QRIS, crypto, card).
• You need access to 200+ models including Asian providers (GLM, Kimi, DeepSeek).

Migration Guide: LiteLLM to Bring AI

Migrating from LiteLLM to Bring AI is straightforward because both support the OpenAI API format. Here’s a step-by-step guide:

Step 1: Get an Bring AI API Key

Sign up at bring.buatjalan.xyz and generate an API key from the dashboard.

Step 2: Update Your Base URL

If you’re using the OpenAI SDK pointed at LiteLLM, just change the base_url:

Before (LiteLLM):

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:4000/v1",  # LiteLLM proxy
    api_key="sk-litellm-key",
)

After (Bring AI):

from openai import OpenAI

client = OpenAI(
    base_url="https://bring.buatjalan.xyz/v1",  # Bring AI
    api_key="bring-your-key",
)

Step 3: Update Model Names

LiteLLM uses provider-prefixed model names (e.g., anthropic/claude-3-5-sonnet). Bring AI uses cleaner model names. Map your models:

# Update model names in your code
response = client.chat.completions.create(
    model="claude-3-5-sonnet",  # Was: "anthropic/claude-3-5-sonnet"
    messages=[{"role": "user", "content": "Hello!"}],
)

Step 4: Migrate Configuration

LiteLLM uses a YAML config file for routing, fallbacks, and budgets. Bring AI provides these through the dashboard and API:

# Set up fallback via Bring AI API
import requests

requests.post(
    "https://bring.buatjalan.xyz/v1/fallbacks",
    headers={"Authorization": "Bearer bring-ai...ey"},
    json={
        "primary": "gpt-4o",
        "fallbacks": ["claude-3-5-sonnet", "gemini-2.0-flash"],
    },
)

Step 5: Remove Infrastructure

If you were self-hosting LiteLLM, you can now:

• Shut down your LiteLLM proxy instances
• Remove Redis and PostgreSQL if they were only used by LiteLLM
• Update your CI/CD pipeline to remove LiteLLM deployment steps
• Cancel cloud resources allocated to LiteLLM infrastructure

Step 6: Test and Deploy

Test your application with the new Bring AI endpoint:

# Quick smoke test
curl https://bring.buatjalan.xyz/v1/chat/completions \
  -H "Authorization: Bearer bring-ai...ey" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o",
    "messages": [{"role": "user", "content": "Hello, Bring AI!"}]
  }'

See the Bring AI docs for the full API reference and models page for the complete model list.

Other LiteLLM Alternatives

Beyond Bring AI, several other AI API gateways are worth considering:

Portkey

Portkey is a TypeScript-based AI gateway with 12.8K GitHub stars. It pioneered the “AI Gateway Pattern” and offers:

• TypeScript/Node.js backend: Better than Python for JS teams, but still not Go.
• Self-hosted or managed: Both options available.
• Strong observability: Detailed request logging and analytics.
• Fallback and load balancing: Similar to LiteLLM and Bring AI.

Best for: JavaScript/TypeScript teams who want self-hosting.

Bifrost

Bifrost is a Go-based AI gateway — the closest architectural match to Bring AI:

• Go backend: Same performance benefits as Bring AI.
• Single binary deployment: No external dependencies.
• Open source: Apache 2.0 license.
• Smaller model coverage: Fewer providers than LiteLLM or Bring AI.

Best for: Go teams who need open-source self-hosting.

OpenRouter

OpenRouter is a managed service with 500+ models:

• Largest model catalog: 500+ models from dozens of providers.
• Managed only: No self-hosting option.
• Strong model comparison page: Easy to compare pricing and capabilities.
• Node.js backend: Not Go-based.

Best for: Teams who want maximum model variety and don’t need self-hosting.

Together AI

Together AI positions itself as an “AI Native Cloud”:

• VC-backed: Well-funded with enterprise focus.
• Hosted open-source models: Specializes in hosting Llama, Mistral, etc.
• Fine-tuning platform: Includes fine-tuning capabilities.
• Not a general gateway: Focused on their hosted models, not multi-provider routing.

Best for: Teams who want hosted open-source models with fine-tuning.

FAQ

What is the best LiteLLM alternative for Go developers?

Bring AI is the best LiteLLM alternative for Go developers because it’s built in Go, offers 200+ models, supports both OpenAI and Anthropic SDKs, and requires no infrastructure to manage. For Go developers who need open-source self-hosting, Bifrost is also worth considering.

Is Bring AI open source?

No, Bring AI is a managed service. If you need an open-source, self-hosted gateway, consider LiteLLM (Python) or Bifrost (Go). Bring AI is ideal for teams who want a managed solution without infrastructure overhead.

Can I migrate from LiteLLM to Bring AI without code changes?

Almost. You need to change the base_url to https://bring.buatjalan.xyz/v1 and update your API key. Model names may need minor adjustments (removing provider prefixes). See the migration guide above.

How does Bring AI’s performance compare to LiteLLM?

Bring AI’s Go backend typically uses 4-10x less memory and handles more concurrent connections per CPU core than LiteLLM’s Python backend. Routing overhead is comparable (<200ms for Bring AI vs ~50-100ms for LiteLLM, though LiteLLM’s overhead can increase under load).

Does Bring AI support self-hosting?

No. Bring AI is a managed service only. If self-hosting is a requirement, LiteLLM (Python) or Bifrost (Go) are better choices. However, many teams find that managed services reduce operational burden significantly.

What are the payment options for Bring AI?

Bring AI supports QRIS (for Indonesian users), cryptocurrency (USDT, USDC, and others), and credit/debit cards. This makes it accessible globally, including in regions where traditional payment processors are unavailable. See pricing for details.

How many models does Bring AI support?

Bring AI provides access to 200+ models including GPT-4o, Claude 3.5 Sonnet/Haiku, Gemini 2.0 Flash/Pro, DeepSeek V3, GLM-4, Kimi, Llama 3.3, and many more. See the models page for the full list.

Conclusion

LiteLLM is an excellent tool — its 57K GitHub stars are well-earned. But for Go developers and performance-sensitive applications, a Go-based gateway like Bring AI offers compelling advantages: lower memory usage, higher throughput, simpler deployment, and dual SDK compatibility.

If you’re currently using LiteLLM and considering a switch, the migration is straightforward — change your base URL, update model names, and remove your LiteLLM infrastructure. Explore Bring AI docs, browse available models, or read about the best AI API gateways in 2026 to learn more.

Related articles:

• Best AI API Gateway in 2026
• OpenAI API Proxy: Complete Setup Guide
• What Is an AI API Gateway?