LiteLLM vs Alternatives in 2026: How the Top LLM Gateways and Proxies Compare

TL;DR

The main LiteLLM alternatives in 2026 are Future AGI Agent Command Center, Portkey (now Prisma AIRS AI Gateway), Cloudflare AI Gateway and OpenRouter, plus the newer open-source gateways Bifrost and agentgateway. vLLM and Ollama serve models behind a gateway rather than replace one. LiteLLM had two supply chain compromises in 2026, so read the security section before you pick.

Within the managed AI gateway with observability and evaluators category, Future AGI is the top pick. LiteLLM is the strongest open-source gateway option. vLLM and Ollama are inference runtimes, not gateways, and pair with either one. Bifrost and agentgateway, compared below, are newer open-source gateways for teams that want to stay self-hosted.

What is LiteLLM in 2026?

LiteLLM is an open-source proxy and Python SDK that exposes an OpenAI-compatible interface for a wide range of providers. For a primitives-level explainer, see what LiteLLM is. The project’s README lists 100+ supported providers, including OpenAI, Azure OpenAI, Anthropic, Google Vertex AI, AWS Bedrock, Cohere, Hugging Face, Mistral, Together AI, Groq, Fireworks, NVIDIA NIM, Replicate, Ollama, and many more.

Two deployment modes:

• LiteLLM Proxy Server. A FastAPI-based gateway you self-host. Supports virtual keys, spend tracking, rate limits, load balancing, fallbacks, retries, and logging hooks (Langfuse, S3, Datadog, OpenTelemetry).
• LiteLLM Python SDK. A client library that gives you a single completion() call across providers. Same OpenAI-style API, different model string.

Its design priorities are:

• Provider breadth. One unified API, many providers.
• Lightweight integration. Drop-in OpenAI-compatible interface so swapping providers is a config flip.
• Self-host control. MIT-licensed core (code under enterprise/ has its own license), run on your own infrastructure.

Top LLM gateways and proxies ranked for 2026

Where Future AGI competes (managed AI gateway plus observability and evaluators), this is the ranked list. Criterion: best production gateway in 2026 for a team that wants routing plus the observability layer above it. vLLM and Ollama are left out because they serve models rather than route between providers.

1. Future AGI Agent Command Center

The gateway plus observability stack. The Agent Command Center is an OpenAI-compatible gateway, in Future AGI Cloud or self-hosted, that sends each request’s trace, cost, routing decision and guardrail outcome into Future AGI Observe and Evaluate, with fi.evals evaluators (code, LLM-as-judge and agent).

Traces, scores, spend, and routing decisions sit in the same dashboard, so a failed call is debuggable from prompt to provider to evaluator score. Pair it with traceai-* (traceAI repo, Apache 2.0) for LangChain, LangGraph, OpenAI Agents SDK, CrewAI, and direct provider SDKs. Cloud judge tiers run on turing_flash (1 to 2 seconds), turing_small (2 to 3 seconds), and turing_large (3 to 5 seconds).

Pick Future AGI when you want a managed gateway with observability and evaluators built in, and when you do not want to run the security and dependency hygiene of a self-hosted proxy. Teams that already run LiteLLM can keep it and add Future AGI tracing and evals on top, as the pairing section below shows.

2. LiteLLM

The strongest open-source option, maintained in the BerriAI/litellm repository. Maximum provider breadth and full self-host control. The trade-off is that you own the security, dependency hygiene, and observability stack. After the March 24 and October 21, 2026 supply chain compromises, the bar for safely running LiteLLM in production rose: pin versions, scan dependencies, isolate the proxy in a hardened segment, and pair with a dedicated observability tool.

3. Portkey

Managed AI gateway and observability platform, now sold as Prisma AIRS AI Gateway after Palo Alto Networks closed its acquisition of Portkey on May 29, 2026. Strong on routing rules and guardrails, and its open-source gateway is MIT-licensed. Its homepage lists gateway, observability, guardrails, governance and prompt management, with no evaluation suite among them.

4. Cloudflare AI Gateway

Managed edge proxy with caching, analytics, and rate limiting. It also ships spend limits, dynamic routing by user segment or geography with fallbacks, guardrails and data loss prevention, all four marked beta in its features docs. Its Evaluations feature is deprecated and no longer supported for new accounts.

5. Helicone

Open-source (Apache-2.0) observability platform with an AI Gateway for 100+ providers (routing, fallbacks, caching, rate limits). Strong on the “drop in one URL, get usage analytics” workflow. Its scores docs say it does not run evaluations; it stores scores computed elsewhere. Mintlify acquired Helicone on March 3, 2026 and moved it to maintenance mode; Helicone’s homepage now says it is no longer part of Mintlify.

6. OpenRouter

Hosted unified API across providers with a single token. A quick way to test many providers from one account. It passes through provider pricing with no inference markup and charges a fee when you buy credits. Bring-your-own-key usage above a monthly allowance also carries a fee.

When LiteLLM still wins

• You have a mature security and ops team that can manage self-hosted infrastructure.
• You need every provider under one API and the broadest possible model list.
• You are budget-constrained and the managed gateways do not fit.
• Your stack already has strong observability and you just need a routing layer.

When to move off LiteLLM (or skip it)

• You do not want to own gateway security after the March and October 2026 incidents.
• You want routing, evaluators, observability, and guardrails in one place.
• You need agent-level eval (task completion, tool call accuracy, function calling) on the same traces as your routing logs.
• You are scaling fast and the managed gateway cost is lower than building a hardened self-host pipeline.

LiteLLM vs OpenRouter, Portkey and Bifrost

LiteLLM vs OpenRouter

LiteLLM is software you run: an MIT-licensed proxy and Python SDK (code under its enterprise/ folder has a separate license) that keeps keys, logs and routing inside your network. OpenRouter is a hosted service you call with one key, so there is nothing to host or patch, while a LiteLLM proxy leaves patching, image pinning and key rotation to your team.

LiteLLM vs Portkey

Portkey’s open-source gateway is MIT-licensed and written in TypeScript, while LiteLLM is a Python proxy and SDK. Portkey’s homepage now says Portkey is Prisma AIRS AI Gateway, generally available for all enterprises. It fits teams whose security stack already runs on Palo Alto Networks.

LiteLLM vs Bifrost and agentgateway

Bifrost is an Apache-2.0 gateway written in Go by Maxim AI, and its repository describes it as an LLM gateway, MCP gateway and agent gateway behind one OpenAI-compatible API. The agentgateway project is an Apache-2.0 proxy written in Rust for AI agents and MCP servers, and its v1.6.0 release on October 2, 2026 added import of LiteLLM upstream request headers.

Technical architecture of LiteLLM

Core components

The LiteLLM Proxy is a FastAPI-based gateway. Its main pieces:

• Proxy server. Routes incoming requests to providers, handles key management, tracks spend, enforces rate limits.
• Python SDK. The litellm package gives a unified completion() call across providers.
• Router. Handles load balancing, retries, fallbacks across multiple deployments.
• Admin UI. Provides model management, usage views, and configuration.

API design

• Unified endpoints. Supports OpenAI-style /chat/completions, /embeddings, /images/generations.
• Pass-through routes. Lets you hit provider-specific endpoints directly through the proxy.
• Custom authentication. Plug in your own auth middleware.
• Configuration flexibility. YAML config for routing rules, rate limits, budgets, guardrails.

Model support

LiteLLM ships adapters for OpenAI, Azure OpenAI, Anthropic, Google Vertex AI, AWS Bedrock, Cohere, Hugging Face Inference, Mistral, Together AI, Groq, Fireworks, NVIDIA NIM, Replicate, Ollama, and others. Custom providers are added via the adapter interface. Every provider sits behind the same OpenAI-format call, so a swap is a change to the model string.

Features and functionality of LiteLLM

Logging and spend tracking

LiteLLM ships logging hooks for Langfuse, S3, Datadog, OpenTelemetry, and more. Project-level spend tracking surfaces per-team, per-model, and per-key usage. For evaluator-grade observability (faithfulness, hallucination rate, latency by route, tool-use correctness), pair LiteLLM with a dedicated eval and observability layer such as fi.evals plus traceai-*.

Virtual keys and access management

Virtual keys let teams grant scoped access to specific models or model groups. Admins can define model-access groups, assign keys to teams, schedule key rotation (an Enterprise feature), and revoke compromised keys. Combined with rate limits and budgets, this gives a fairly complete identity-and-access surface for an LLM proxy.

Load balancing and rate limiting

The router distributes incoming requests across multiple model deployments and provider keys. Rate limits can be enforced per-key, per-team, or per-model in RPM and TPM units. Fallbacks and retries ride out provider outages. Budgets can cap spend per key or team on the same proxy.

LiteLLM performance on a self-hosted proxy

LiteLLM advertises minimal added latency compared to direct provider calls (2 ms p50 gateway overhead on its four-instance benchmark). With load balancing turned on across multiple keys or deployments, end-to-end throughput typically improves because retries and fallback routes reduce tail-latency on provider outages. Always benchmark on your own infrastructure: network distance to the provider, CPU and memory of the proxy host, and concurrency settings all matter more than reference numbers.

LiteLLM vs vLLM vs Ollama

The three are often grouped together but solve different problems.

• LiteLLM sits in front of providers and unified endpoints.
• vLLM is the engine you put behind LiteLLM (or directly) when you host open-weight models at scale.
• Ollama runs local models on a laptop or single server, often through a LiteLLM Ollama adapter.

A common production stack is LiteLLM (routing) plus vLLM (self-hosted open-weight model serving) plus a managed observability tool. A simpler stack is Future AGI Agent Command Center (managed routing plus observability) plus optional vLLM behind it for self-hosted models.

LiteLLM vs LangChain

LangChain is an agent framework that composes a model with tools, prompts and middleware, while LiteLLM standardizes how you call model providers and routes between them. They work together: the langchain-litellm package gives LangChain a ChatLiteLLM chat model and a ChatLiteLLMRouter that uses LiteLLM’s Router for load balancing and fallbacks. With Future AGI, traceAI-langchain traces the LangChain side and traceAI-litellm traces direct LiteLLM calls.

LiteLLM security and how to harden a self-hosted proxy

LiteLLM had two supply chain compromises in 2026, both carrying credential-stealing malware: PyPI releases 1.82.7 and 1.82.8 on March 24, and 1.107.0 on PyPI and the Docker latest, main-latest and main-stable tags on October 21. The minimum bar for running LiteLLM in production:

• Pin versions. Lock the litellm package version in requirements.txt. Update only after reviewing the changelog and dependencies.
• Pull images by digest. The October attack published malicious images under mutable Docker tags, so reference an exact image digest instead of a tag.
• Patch known CVEs. Check LiteLLM’s GitHub security advisories before every upgrade. CVE-2026-114612 (9.9 on CVSS 3.1, rated by GitHub) lets an authenticated internal user become proxy admin, works in the default configuration after 1.91.0, and is fixed in 1.100.4, 1.101.3, 1.102.2 and 1.103.1. CVE-2026-49468 (Host header auth bypass, fixed in 1.84.0) and CVE-2026-42208 (unauthenticated SQL injection, fixed in 1.83.7) both score 9.8 in NVD. The October 27 advisory GHSA-w4c7-34wj-hqm8 (10.0 on CVSS 3.1) covers an unauthenticated file read that leaks the master key on proxies running an Enterprise license, fixed in 1.103.10, 1.104.8, 1.105.6 and 1.106.5.
• Scan dependencies. Run pip-audit, Dependabot, or similar against every release.
• Isolate the proxy. Run it in a hardened network segment with outbound allowlists to provider endpoints only.
• Rotate keys aggressively. Treat every provider key in the proxy as a high-value secret.
• Log every request. Pair with an observability tool so any anomaly is visible quickly.
• Read the incident response guide. See LiteLLM Compromised 2026: Incident Response and Gateway Migration for the detailed step-by-step.

If the hardening overhead is not worth it for your team, a managed gateway shifts that operational load onto the vendor. The LiteLLM alternatives roundup ranks the gateways teams migrate to and explains what each one fixes after the March 2026 PyPI incident.

How Future AGI’s Agent Command Center pairs with or replaces LiteLLM

The Agent Command Center is the gateway alternative for teams that want routing plus everything that sits above it. During a migration, shadow experiments mirror production traffic to a second model so you can compare responses, latency and token use before you switch. Two adoption paths:

• Replace LiteLLM proxy. Run the Agent Command Center as your gateway, in Future AGI Cloud or self-hosted. BYOK to your providers, point your OpenAI SDK base_url at it with an sk-agentcc- key, and get traces, costs and guardrail scores in Observe and Evaluate.
• Keep LiteLLM proxy. Run LiteLLM for routing and instrument the app that calls it: traceAI-litellm traces LiteLLM SDK calls, and the traceAI package for your framework covers the rest. Score the spans with fi.evals on a sample. To trace the proxy process itself, its OpenTelemetry callback can send one trace per request to Future AGI (see the LiteLLM Proxy tracing guide, in beta).

Either way, the eval and observability layer is consistent: fi.evals for scoring (agent-learning-kit, Apache-2.0, pip install agent-learning-kit) and traceai-* for spans (Apache 2.0), sent to Future AGI Observe. Env vars are FI_API_KEY and FI_SECRET_KEY. The snippet below sets up tracing for the keep-LiteLLM path.

from fi_instrumentation import register
from fi_instrumentation.fi_types import ProjectType
from traceai_litellm import LiteLLMInstrumentor

tracer_provider = register(
    project_type=ProjectType.OBSERVE,
    project_name="gateway-prod",
)
LiteLLMInstrumentor().instrument(tracer_provider=tracer_provider)

After the snippet runs, every litellm.completion() call your app makes is traced to Future AGI with no code change (setup in the traceAI LiteLLM docs). Pair the spans with fi.evals evaluators such as Groundedness and Tool Call Accuracy to score a sampled slice.

Picking a gateway after LiteLLM’s 2026 incidents

LiteLLM still puts one OpenAI-style API in front of many providers, and its MIT-licensed core remains a strong self-hosted option. After two supply chain compromises in 2026, the choice is whether your team owns the patching, image pinning and key rotation or hands that work to a managed gateway. Either way, trace and score every request so you can see what happens when a user sends a prompt.

Route your first request through the Future AGI Agent Command Center and see traces, costs and guardrail results in one place.

Sources

• NVD entry for CVE-2026-114612
• LiteLLM advisory GHSA-7hp6-4w63-5g45 (CVE-2026-114612 fixed versions)
• NVD entry for CVE-2026-49468
• LiteLLM advisory GHSA-4xpc-pv4p-pm3w (CVE-2026-49468 fixed version)
• NVD entry for CVE-2026-42208
• LiteLLM advisory GHSA-r75f-5x8p-qvmc (CVE-2026-42208 fixed version)
• LiteLLM virtual keys docs (key rotation as an Enterprise feature)