I built AegisFlow because I needed an LLM proxy and kept running into things with LiteLLM that didn't work for me. This isn't a takedown. LiteLLM has a huge community, 100+ providers, and a lot of teams use it in production. But I wanted something different, so I wrote my own in Go. Here's what I found after using both.
Where LiteLLM is the better choice
Provider count. LiteLLM supports over 100 providers. AegisFlow supports 10. If you need Sagemaker, VertexAI, or NVIDIA NIM out of the box, LiteLLM is the answer and it's not close.
Python. If your whole team writes Python, LiteLLM is pip install litellm and you're running. AegisFlow is a Go binary. Your Python devs can talk to it over HTTP, but the project itself isn't in their language.
Endpoint coverage. LiteLLM handles embeddings, images, audio, batches, reranking. AegisFlow only does chat completions and model listing. If you need multimodal endpoints, LiteLLM has them.
Community. LiteLLM has thousands of users. AegisFlow just got its first external contributors last week. There's no comparison on ecosystem maturity.
Where AegisFlow is the better choice
Speed. AegisFlow runs at 58K requests per second with a 1.1ms median latency. LiteLLM's Python import alone takes 3-4 seconds on a decent machine because the init file has over 1,200 lines of imports. In production under load, this gap widens.
Deployment. AegisFlow is one binary. Or docker pull saivedant169/aegisflow. No Python runtime, no pip, no virtualenv, no dependency conflicts. LiteLLM in production means owning uptime for the proxy process, PostgreSQL, and Redis, plus managing Python dependencies and security patches.
Security out of the box. AegisFlow has a policy engine (keyword blocking, regex, PII detection, WASM plugins for custom filters), RBAC with three roles per API key, and audit logging with a SHA-256 hash chain for tamper detection. All of this is in the open-source version. LiteLLM's RBAC and SSO are behind an enterprise paywall.
Worth mentioning: in March 2026, LiteLLM had a supply chain incident where a compromised PyPI package (v1.82.8) contained code that stole SSH keys, cloud credentials, and K8s secrets. AegisFlow is a compiled Go binary, so that category of attack doesn't apply.
Canary rollouts. AegisFlow lets you gradually shift traffic from one provider to another (5% then 25% then 50% then 100%) and automatically rolls back if error rates or latency spike. LiteLLM has fallbacks, but not gradual rollouts with health-based promotion.
Budget enforcement. AegisFlow enforces budgets at global, per-tenant, and per-model levels. Alerts at 80%, warning headers at 90%, hard block at 100%. LiteLLM tracks spend but enforcement at the tenant level is enterprise-only.
Anomaly detection. AegisFlow has built-in statistical anomaly detection that compares the last 5 minutes against a 24-hour baseline and fires alerts when things deviate. LiteLLM doesn't have this natively.
Dashboard. AegisFlow ships with a 13-page real-time dashboard covering traffic, policies, live feed, violations, cache, rollouts, analytics, alerts, budgets, audit log, providers, tenants, and federation. LiteLLM relies on external tools for most of this visibility.
Federation. AegisFlow has a control-plane/data-plane architecture where one instance distributes config to others and aggregates metrics back. LiteLLM doesn't have multi-cluster support.
Quick comparison table
| LiteLLM | AegisFlow | |
|---|---|---|
| Language | Python | Go |
| Providers | 100+ | 10 |
| Performance | Python-speed | 58K req/s, 1.1ms p50 |
| Deploy | pip + PostgreSQL + Redis | Single binary or Docker |
| Endpoints | Chat, embeddings, images, audio, batches | Chat completions, models |
| Policy engine | No | Keyword, regex, PII, WASM plugins |
| RBAC | Enterprise only | 3 roles, open source |
| Audit logging | No | SHA-256 hash chain |
| Canary rollouts | No | Yes, auto-rollback |
| Anomaly detection | No | Statistical baseline |
| Budget enforcement | Enterprise only | Global, per-tenant, per-model |
| Dashboard | External tools | 13 pages built in |
| Federation | No | Control plane + data planes |
| Community | Thousands of users | Just getting started |
When to pick which
Use LiteLLM if you're a Python team that needs 50+ providers and multimodal endpoints, and you're comfortable managing Python in production. The ecosystem is big and the community support is real.
Use AegisFlow if you care about latency, want security and governance built in without an enterprise license, and prefer deploying a single binary over managing a Python stack. AegisFlow covers fewer providers but goes deeper on the operational side.
Most teams would honestly be fine with either. It depends on whether Python flexibility or Go performance and security defaults matter more for your setup.
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AegisFlow
Open-Source AI Gateway + Policy + Observability Control Plane. Route, secure, observe, and control all your AI traffic from a single Go binary.
AegisFlow
Open-Source AI Gateway + Policy + Observability Control Plane
Route, secure, observe, and control all your AI traffic from a single gateway
Quickstart | Features | Architecture | Configuration | API Reference | Contributing
What is AegisFlow?
AegisFlow is a production-grade AI gateway built in Go that sits between your applications and LLM providers. It gives you a single control plane to manage routing, security policies, rate limiting, cost tracking, and observability across OpenAI, Anthropic, Ollama, and any OpenAI-compatible provider.
Point any OpenAI SDK at AegisFlow by changing one line:
# Before
client = OpenAI(api_key="sk-...")
# After - all traffic now flows through AegisFlow
client = OpenAI(base_url="http://localhost:8080/v1", api_key="aegis-test-default-001")
Why AegisFlow?
Teams running AI in production face real problems:
- Vendor lock-in -- different SDKs, different formats, different billing
- No fallback -- when OpenAI goes down, your product…
docker pull saivedant169/aegisflow
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