LiteLLM Alternative — Self-Host and Verify It
Use a hosted or self-managed LiteLLM alternative with provider failover, privacy routing, OpenAI-compatible APIs, open source code, and verifiable hardware attestation.
LiteLLM lets you self-host. TrustedRouter completes its provider map.
LiteLLM is a strong open-source control layer for your own platform. Keep one or two direct providers where you have contracts, credits, or special terms. Then add TrustedRouter as the final provider and fallback for 600+ additional models across more than 80 providers.
The combination gives you local policy and spend controls without maintaining every long-tail integration. TrustedRouter adds managed rollover and a hardware-attested prompt path through one OpenAI-compatible upstream.
client = OpenAI(
base_url="https://api.trustedrouter.com/v1",
api_key="sk-tr-v1-..."
)
# Drop-in for any LiteLLM proxy integration.
# Self-host TR too — your built image hash is the
# hash your enclave reports, so attestation holds.Run LiteLLM in your VPC.
Keep internal policy, budgets, virtual keys, and direct-provider order under your control.
Add one universal upstream.
TrustedRouter fills model and provider gaps without dozens of new accounts and adapters.
Keep sensitive fallback inspectable.
The final upstream runs through an attested gateway whose source and running image can be checked.
Use LiteLLM in front of TrustedRouter.
Put direct provider relationships first, then route everything else to TrustedRouter. Your team owns the policy layer while one final upstream supplies catalog breadth, provider rollover, and privacy with proof.
Read the full argument: “Attestation is All You Need”.
Current routes, prices, privacy, and measured performance.
Catalog facts come from the routes currently configured in TrustedRouter. Performance uses the same cached metadata snapshot as the public leaderboard. Prompts and outputs are not part of these measurements.
| Model | Providers | Context | Input | Output | Privacy | Measured route |
|---|---|---|---|---|---|---|
Anthropic: Claude Opus 4.8anthropic/claude-opus-4.8 |
|
1,000,000 | $5.275/1M | $26.375/1M | varies 5 cited scores | 1538 ms TTFT anthropic · 91 tok/s · 100.00% available · n=3 |
OpenAI: GPT-5.5openai/gpt-5.5 |
|
1,050,000 | $5.275/1M | $31.65/1M | ZDR 3 cited scores | Warming up |
Google: Gemini 3.5 Flashgoogle/gemini-3.5-flash |
+1
|
1,048,576 | $1.5825/1M | $9.495/1M | ZDR | 1278 ms TTFT google-vertex · 107 tok/s · 100.00% available · n=7 |
MoonshotAI: Kimi K2.7 Codemoonshotai/kimi-k2.7-code |
+10
|
262,144 | $0.70685/1M to $1.13096/1M | $3.587/1M to $4.90575/1M | ZDR 5 cited scores | 2262 ms TTFT kimi · 61 tok/s · 100.00% available · n=25 |
Z.ai: GLM 5.2z-ai/glm-5.2 |
+24
|
1,048,576 | $0.7174/1M to $2.434307/1M | $1.5825/1M to $7.029062/1M | E2EE 4 cited scores | 1806 ms TTFT baseten · 128 tok/s · 100.00% available · n=144 |
MiniMax: MiniMax M3minimax/minimax-m3 |
+9
|
524,288 | $0.24265/1M to $0.633/1M | $1.0128/1M to $2.532/1M | ZDR 4 cited scores | 987 ms TTFT fireworks · 220 tok/s · 100.00% available · n=186 |
Browse every modelReview provider policiesOpen the full leaderboardSnapshot 2026-09-17T14:11:35.322Z