TrueFoundry vs Bifrost: Why We Chose Specialization Over an All-in-One MLOps Platform
Source: Dev.to
The Platform Tax
You’ve seen this pattern before:
| What you need | What you get | What you actually use | What you pay for |
|---|---|---|---|
| A reliable way to route requests to OpenAI / Anthropic / Bedrock | “Here’s a complete MLOps platform that also includes an AI gateway, model training, fine‑tuning, Kubernetes orchestration, GPU management, agent deployment…” | The gateway | Everything else |
This is the platform tax, and for AI gateways it’s steep.
TrueFoundry – A Kubernetes‑native MLOps platform
TrueFoundry does a lot:
- Model training infrastructure
- Fine‑tuning workflows
- GPU provisioning & scaling
- Model deployment orchestration
- AI gateway (one component among many)
- Agent orchestration
- Full Kubernetes cluster management
When does TrueFoundry make sense?
- If you need all of the above – training, fine‑tuning, deployment, gateway, etc.
- If you already run Kubernetes and have a dedicated DevOps team.
When does it become a tax?
- If you only need a gateway – you’re paying for a full platform you don’t use.
Typical onboarding timeline (TrueFoundry)
| Day | Activity |
|---|---|
| Day 1 | Provision Kubernetes cluster (EKS / GKE / AKS) |
| Day 2 | Install TrueFoundry platform components |
| Day 3 | Configure networking, security, RBAC |
| Day 4 | Deploy the gateway component |
| Day 5 | Configure provider integrations |
| Day 6 | Test & debug platform issues |
| Week 2 | Actually use the gateway |
Bifrost – A lightweight AI gateway
docker run -p 8080:8080 \
-e OPENAI_API_KEY=your-key \
-e ANTHROPIC_API_KEY=your-key \
maximhq/bifrostDone. Production‑ready in ~60 seconds.
Visit → Add keys → Start routing.
- No Kubernetes. No platform. No DevOps team required.
Performance & cost highlights
| Metric | TrueFoundry (platform) | Bifrost (gateway) |
|---|---|---|
| Latency overhead | Variable, depends on platform load | TrueFoundry does not have semantic caching. |
Intelligent Failover
# OpenAI rate limit hit
# Bifrost automatically routes to Anthropic
# User sees zero downtime
# **You don’t need Kubernetes.** That’s the point.Migration: TrueFoundry gateway → Bifrost
| Week | Activity |
|---|---|
| Week 1 – Parallel Deployment | Deploy Bifrost alongside TrueFoundry; configure identical providers; test with 10 % traffic. |
| Week 2 – Traffic Shift | Gradually shift traffic: 10 % → 50 % → 100 %; monitor performance; keep TrueFoundry as fallback. |
| Week 3 – Full Cutover | Route all traffic through Bifrost; decommission TrueFoundry gateway; celebrate ≈ 40 % cost savings. |
Most teams complete migration in 2‑3 weeks.
Bottom line
- TrueFoundry = full‑stack MLOps platform (great if you need the whole stack).
- Bifrost = lightweight, OpenAI‑compatible gateway (ideal for the majority of teams).
Pick the tool that matches the problem you actually need to solve.
Why It Matters
1. Specialization Wins
Purpose‑built tools outperform platform components. Every time.
2. Performance Matters
- Most teams don’t need a full MLOps platform.
They need a reliable way to access multiple LLM providers without the operational overhead. That’s why we built Bifrost.
Get Started
Self‑hosted (free, open source)
docker run -p 8080:8080 maximhq/bifrostManaged Cloud
Resources
- GitHub – ⭐ Star it!
- Documentation
- Benchmarks
Questions?
Drop a comment below. I’m happy to chat about:
- Gateway architecture
- Performance optimization
- Why we chose Go over Python
P.S. If you’re building a full ML platform from scratch and need training + deployment + gateway, TrueFoundry is solid. This isn’t a hit piece—it’s about choosing the right tool for the job.
But if you just need a gateway? Save yourself weeks of Kubernetes headaches and use a specialized tool.