Best LiteLLM alternative for enterprises

TL;DR | Bifrost | LiteLLM (4 workers) | | |---|---|---| | p50 at 100 RPS | 1.01 ms | 5.84 ms | | Success rate at 1,000 RPS | 100% | 94.9% | | p99 at 1,000 RPS | 2.62 ms | 33,766 ms | | Published container runs as | UID 1000 | root | | SSRF guard on the configured provider URL | on by default | not applied to the configured api_base | | License of the open repo | Apache-2.0 | MIT, except enterprise/ | | Memory at rest | about 60 MiB | 2.08 GiB | Verdict: for enterprise use, Bifrost is the better LiteLLM alternative. Stay on LiteLLM if your team extends it in Python or runs it far below saturation. If you are evaluating a LiteLLM alternative for a company rather than for a side project, you are choosing less for features than for what shows up in a postmortem: tail latency when a burst arrives, the defaults you inherit without reading them, the license your legal team signs off on, the dependency tree your security team owns, and what happens when a provider returns a 500. An AI gateway sits in front of every model call you make. Security and operations are my work, so that is where I looked hardest: I measured the latency, checked the defaults, the license and the dependencies, and read the failover path in the code. The question is when another gateway is a better enterprise choice than LiteLLM, and on what evidence. The alternative I tested is Bifrost, the open-source gateway from Maxim AI, whose code is in its GitHub repository. Here is what held up. Latency benchmark I did not build my own benchmark. I used maximhq/bifrost-benchmarking , the open-source benchmarking tool maintained by the Bifrost team at Maxim AI, including its mocker : a fake LLM provider that isolates gateway overhead from real provider latency. I used no provider keys. Both gateways ran in containers on one Docker network, against the same mocker. Two details of the method affect the result. I alternated the runs, one gateway then the other, instead of running all of Bifrost's rounds and then all of LiteLLM's: a machine gets slower over a long session, and running them in blocks would have handed that slowdown to whichever gateway went second. Each number below is the median of three 20-second runs. The test machine was a shared-CPU VPS, not a dedicated server: 4 vCPU on an AMD EPYC 9354P, 15.6 GiB of RAM, Ubuntu 26.04.1 LTS and Docker 29.8.0. I set no CPU or memory limits on the containers. The load generator, both gateways and the mocker shared those four cores. That is a worse setup than a dedicated cloud instance, and it holds the absolute numbers down. Maxim's published figures come from dedicated AWS t3.medium and t3.xlarge instances, so the numbers here are not directly comparable to those. | Requested RPS | Gateway | Success | p50 | p99 | max | |---|---|---|---|---|---| | 100 | Bifrost | 100% | 1.01 ms | 1.84 ms | 4.90 ms | | 100 | LiteLLM | 100% | 5.84 ms | 9.33 ms | 16.10 ms | | 500 | Bifrost | 100% | 0.75 ms | 1.59 ms | 5.77 ms | | 500 | LiteLLM | 100% | 13.29 ms | 40.30 ms | 86.34 ms | | 1,000 | Bifrost | 100% | 0.79 ms | 2.62 ms | 10.35 ms | | 1,000 | LiteLLM | 94.9% | 1,456.80 ms | 33,766 ms | 36,212 ms | That table says something more specific than "faster". Below saturation the p50 gap is 5.8x at 100 RPS, widening to 17.6x at 500 RPS. Between 500 and 1,000 RPS the two stop doing the same job: Bifrost serves every request with a p50 under 1 ms, while LiteLLM drops 5% of them and its p99 goes to 33.8 seconds. I also did not find Bifrost's ceiling. Its p50 was 0.79 ms at 1,000 RPS against 0.75 ms at 500 RPS, and 1,000 RPS was the highest rate I ran. Maxim publishes its own overhead figures, 59 µs on a t3.medium and 11 µs on a t3.xlarge at 5,000 sustained RPS. LiteLLM worker count The official LiteLLM image does not set --num_workers , and the CLI default is 1: one Python process, against a Go server with all four cores available. That comparison produced a ratio in the tens of thousands. I reran with --num_workers 4 , one worker per core, and checked that four multiprocessing-fork children existed inside the container: | LiteLLM at 500 RPS | 1 worker | 4 workers | |---|---|---| | Success rate | 89.1% | 100% | | p50 | 20,446 ms | 13.29 ms | Three orders of magnitude from one flag. If you run LiteLLM in production and have never set --num_workers , check that before you read any further. Memory at rest I read docker stats once a minute for the first 15 minutes after startup, with LiteLLM on four workers. The table shows the values once both had settled: | Gateway | Memory at rest | |---|---| | Bifrost | about 60 MiB | | LiteLLM (4 workers) | 2.08 GiB | Each LiteLLM worker is a full Python process. The difference matters most if you run many small replicas. Security defaults Both gateways ship with authentication for inference disabled. For a security reviewer that is the most important sentence here. Bifrost defines the field in framework/configstore/tables/clientconfig.go with a default of false (a test pins it there): EnforceAuthOnInference bool gorm:"default:false" json:"enforce_auth_on_inference" LiteLLM's shape is the same with a different mechanism: master_key is None unless it is set under general_settings in the config file or LITELLM_MASTER_KEY is in the environment, and the config file wins. A gateway holds the provider keys you configure, so until that line is flipped, anything that can reach the port can spend those keys. Both ship it the same way, and on both it is one line. Flip it before the first deploy rather than after the first invoice. Where the two differ: | Criterion | Bifrost | LiteLLM | |---|---|---| | Published container runs as | UID 1000 | root | | SSRF guard on the configured provider URL | on by default | not applied to the configured api_base | | License of the open repo | Apache-2.0, all of it | MIT, except enterprise/ | | Direct dependencies, audited manifests | 180 | 215 | | Repository age, as of September 13, 2026 | 17 months | 37 months | The license row needs a footnote, because both projects have a paid tier. LiteLLM keeps its paid tier inside the repository under enterprise/ with its own license, and GitHub labels the repository's license NOASSERTION. Bifrost keeps its paid tier outside: the open code is Apache-2.0 without exceptions, and Bifrost Enterprise is a separate distribution that, from v2.0.0, requires a provisioned license.bif in BIFROST_LICENSE on every node. The container user takes one command to check: docker image inspect maximhq/bifrost --format '{{.Config.User}}' # -> 1000:0 docker image inspect ghcr.io/berriai/litellm:main-latest --format '{{.Config.User}}' # -> root Bifrost blocks RFC 1918 private addresses as provider base URLs. Point it at the mocker on a private Docker address and it answers: 502 "connection to private IP 172.18.0.2 is not allowed" To allow a private destination, set allow_private_network: true in that provider's network_config . Link-local addresses (169.254.x.x ) stay blocked whatever that setting says. Both are documented on the provider configuration page of the Bifrost docs. LiteLLM, pointed at the same mocker on a private Docker address, connected with no extra setting. In its source at the tested commit, the SSRF validator in litellm/litellm_core_utils/url_utils.py applies to user-supplied URLs such as image_url and file_url , and the api_base an operator configures is treated as trusted. Bifrost has the better defaults on the image and the outbound path. The dependency row comes from Trail of Bits' supply-chain-risk-auditor collector, run on Bifrost's core, framework, CLI and transports Go modules plus its UI package.json , and on LiteLLM's root manifests plus its dashboard. Fallbacks and retries Bifrost's fallback chain is sequential and error-type-aware. The decision to continue lives in one function, shouldContinueWithFallbacks , and it stops in exactly three cases: the attempt succeeded, the request was canceled, or the provider marked the error AllowFallbacks: false . That third case is the design choice worth reading the code for. The provider layer can declare that retrying elsewhere will not help, and the chain honors it. Fallbacks are built per request type rather than by replaying the same bytes at another endpoint (prepareFallbackRequest ). Non-streaming and streaming requests run the same fallback helpers in two separate loops (line 5351 and line 5489 of core/bifrost.go ), and a streaming request can only fall back before output starts: the docs say errors after that point "remain stream errors". When LiteLLM is still the right answer Stay on LiteLLM if your team works in Python and uses the gateway as an extension point. It is a Python SDK as well as a proxy, and if you subclass its handlers or import it into a service, a Go binary does not replace that. Stay if your throughput is nowhere near saturation: at 100 RPS the p50 is 5.84 ms against 1.01 ms, and a real model call adds its own latency on top, which this benchmark excludes by design. Where Bifrost fits: governance, scalability and reliability Bifrost is built for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. It serves as a centralized AI gateway to route, govern, and secure all AI traffic across models and environments with ultra low latency. Bifrost unifies LLM gateway, MCP gateway, and Agents gateway capabilities into a single platform. Designed for regulated industries and strict enterprise requirements, it supports air-gapped deployments, VPC isolation, and on-prem infrastructure. It provides full control over data, access, and execution, along with robust security, policy enforcement, and governance capabilities. On the measurements above, it is the better enterprise fit when you want one Apache-2.0 license across the open repository, a non-root container out of the box, an SSRF guard on outbound provider URLs that is on before you ask for it, a p99 under 3 ms at 1,000 RPS (LiteLLM's reached