Top 5 LiteLLM Alternatives for High Throughput Workloads

Teams evaluate LiteLLM alternatives for one recurring reason: the proxy that worked at 50 requests per second stops keeping up at 2,000, and adding replicas turns a single component into a fleet. LiteLLM is a capable proxy with wide provider coverage, and for development and prototyping the tradeoffs rarely surface. They surface under sustained concurrency, where the runtime and the request path determine the ceiling more than the feature list does. Bifrost, the open-source AI gateway written in Go by Maxim AI, is the best overall choice for enterprise teams whose primary constraint is throughput, because a compiled request path and goroutine-based concurrency raise that ceiling rather than moving it. This post compares five alternatives on throughput mechanics and covers how to benchmark before migrating.

Why LiteLLM Throughput Becomes a Constraint

The limitation is architectural rather than a defect. LiteLLM is a Python application, and the global interpreter lock in CPython constrains how much a single process can do concurrently. Production deployments work around this by running multiple worker processes behind a load balancer, which works, but changes the operational picture:

• Horizontal scaling starts earlier. Capacity is added by provisioning instances rather than by using the ones you have more efficiently.
• State moves out of process. Rate limits, budgets, and caches need shared infrastructure such as Redis and Postgres once more than one worker exists, adding components to patch and monitor.
• Tail latency widens under load. Request queuing inside a saturated worker shows up in p99 well before average latency moves.

None of this matters at low volume. It matters when AI traffic becomes a primary workload rather than a feature, which is the point at which most teams start looking at LiteLLM alternatives.

What Actually Caps LLM Gateway Throughput

Four properties determine a gateway's ceiling, and they are worth checking before comparing provider counts.

• Concurrency model. Whether the runtime handles thousands of in-flight requests inside one process, or requires a process per unit of parallelism. This is the largest single difference between candidates.
• Per-request overhead. The time the gateway itself adds. It is small relative to model inference, but it consumes the same CPU that serves other requests, so it sets how many concurrent requests a machine can hold.
• Connection reuse. Whether upstream provider connections are pooled and kept alive. Re-establishing TLS per request is expensive at volume.
• Blocking work in the hot path. Synchronous logging, metrics export, or cache lookups on the request path convert into queuing delay under load.

A gateway that keeps all four out of the way scales vertically before it scales horizontally, which is the difference between one instance and twelve. Bifrost publishes its benchmarking methodology against these properties.

Top 5 LiteLLM Alternatives Compared

The split that matters here is the last column. Two of these raise your throughput ceiling by giving you a faster process to run; two raise it by removing the process from your infrastructure entirely, at the cost of sending traffic through a vendor. Bifrost is the option that does the former without giving up governance or data residency.

1. Bifrost

Bifrost is an open source AI gateway written in Go that unifies access to 1000+ models behind a single OpenAI-compatible API. It is a drop-in migration target from LiteLLM: existing SDK code changes only its base URL.

Throughput Characteristics

Bifrost adds 11 microseconds of overhead per request at 5,000 requests per second in published performance benchmarks, with methodology available so teams can reproduce the numbers on their own hardware. Goroutine scheduling means a single process holds a large number of concurrent in-flight requests without a worker-per-core arrangement, and telemetry collection runs asynchronously so metrics never enter the request path.

Key Capabilities

• Drop-in replacement for OpenAI, Anthropic, Bedrock, Google GenAI, LangChain, and LiteLLM SDKs
• Automatic failover and load balancing with weighted distribution across keys and providers
• Semantic caching that removes repeat work from the upstream path entirely
• Virtual keys carrying budgets, rate limits, and model permissions in-process
• Clustering with automatic service discovery and zero-downtime deploys when horizontal scaling is eventually needed

Limitations

Provider coverage, while broad, is not the widest in the category. Teams whose requirement is reaching an unusual provider on day one may find a longer catalog elsewhere, at the throughput cost described above.

Best for: 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.

2. Envoy AI Gateway

Envoy AI Gateway adds LLM-aware routing to Envoy, the C++ data plane behind Istio and most Kubernetes service meshes. Throughput inherits from Envoy itself, which is proven at high request volumes in non-AI workloads.

Key Capabilities

• Mature, high-performance data plane with efficient connection management
• Kubernetes Gateway API configuration and service mesh integration
• Token-based rate limiting and usage tracking
• Endpoint Picker for routing to self-hosted model servers

Limitations

It is early in its release cycle, with narrower provider coverage than mature options and no semantic caching or virtual key budget hierarchy in the current open source release. The xDS configuration model is a significant learning curve outside the Envoy ecosystem, and there is no single-binary path: throughput gains arrive bundled with a Kubernetes control plane.

Best for: Platform teams already operating Envoy or Istio who want AI routing inside the existing mesh.

Where Kubernetes is not already a prerequisite, the Bifrost AI gateway reaches comparable request-path efficiency as a single process.

3. Kong AI Gateway

Kong AI Gateway extends Kong Gateway with AI Proxy plugins. The NGINX and Lua core is a well-understood high-throughput foundation, and the plugin ecosystem covering OIDC, mTLS, and rate limiting is mature.

Key Capabilities

• Multi-provider LLM routing through AI Proxy plugins
• Established Kubernetes, Helm, and Docker deployment tooling
• Broad non-AI plugin ecosystem for auth and traffic management
• Self-hosted binary alongside a managed control plane

Limitations

The Lua processing layer adds more per-request work than a purpose-built AI gateway, so raw proxy throughput does not translate directly into AI request throughput. More consequentially for teams migrating off open source, several AI-relevant capabilities including semantic caching and advanced analytics sit behind the commercial tier.

Best for: Organizations already running Kong for API management that want AI traffic on the same control plane.

By contrast, Bifrost keeps caching, governance, and telemetry inside the freely self-hostable Apache 2.0 distribution.

4. Cloudflare AI Gateway

Cloudflare AI Gateway is a managed proxy running across Cloudflare's global edge network. It adds caching, rate limiting, logging, retries, and model fallback with minimal integration work, and core features are available on every plan.

Key Capabilities

• Edge-distributed routing with low end-to-end latency for globally distributed users
• Exact-match caching, analytics, and rate limiting included at no additional cost
• OpenAI-compatible endpoints alongside a universal REST interface
• Native access to Cloudflare's own model hosting for teams already on the platform

Limitations

It is not self-hostable, which removes it from consideration for regulated workloads with data residency requirements. Routing is rule and budget based rather than content-aware, guardrails and data loss prevention are still in beta, and throughput is governed by a vendor's plan rather than by hardware you control.

Best for: Teams already standardized on Cloudflare serving a geographically distributed user base with no data residency constraint.

Where residency is a requirement, Bifrost delivers the same routing and caching behavior inside your own network.

5. Vercel AI Gateway

Vercel AI Gateway is a hosted unified endpoint covering a large model catalog, with automatic retry, fallback, load balancing, and spend monitoring. It is the default provider in the Vercel AI SDK, so routing through it requires effectively no configuration for teams already using that SDK.

Key Capabilities

• Automatic failover and load balancing across providers
• Bring-your-own-key support with provider pricing passed through
• Native integration with the Vercel AI SDK and Next.js deployments
• Usage and spend analytics without operating infrastructure

Limitations

Managed only, with the same residency and control tradeoffs as any hosted gateway. The tight SDK coupling that makes setup trivial for TypeScript teams becomes friction for polyglot estates, and throughput characteristics are the vendor's to determine.

Best for: Next.js and TypeScript teams already building on the Vercel AI SDK.

For teams that need the same routing behavior inside their own network, the open-source Bifrost gateway provides equivalent failover with OTLP telemetry exported to existing backends.

How to Benchmark Before You Migrate

Vendor benchmarks, including any published by a gateway's own team, are tuned to the publisher's strengths. Run your own with these controls:

• Use your traffic shape. Prompt length, streaming versus non-streaming, and tool-call frequency change results substantially. Synthetic uniform payloads overstate every gateway.
• Mock the provider. Measuring against a live provider measures the provider. Point the gateway at a stub that returns a fixed response so you are measuring gateway overhead.
• Report p99, not average. Concurrency problems appear in the tail first. An unchanged mean with a doubled p99 is a saturated worker pool.
• Hold hardware fixed and raise concurrency. The number you want is where added concurrency stops adding throughput, not the best figure at a comfortable load.
• Count the surrounding infrastructure. A gateway needing Redis and Postgres to enforce a rate limit has a different real cost than one enforcing it in-process.

The LLM Gateway Buyer's Guide includes a scoring framework covering these axes alongside governance and deployment.

Migrating from LiteLLM

The migration is usually smaller than teams expect, because both speak an OpenAI-compatible API. Pointing an existing SDK at Bifrost changes the base URL and nothing else, which makes a side-by-side benchmark against production traffic straightforward before committing. The LiteLLM migration guide covers configuration mapping, and the LiteLLM alternatives comparison covers the capability differences in full.

Among LiteLLM alternatives, the one worth choosing is the one that changes your scaling curve rather than your vendor. To see throughput behavior against your own traffic, book a demo with the Bifrost team.

Frequently Asked Questions

What is the best LiteLLM alternative for high throughput?

Bifrost, for teams that need to self-host. A compiled Go request path with 11 microseconds of overhead at 5,000 requests per second raises the per-instance ceiling rather than requiring more instances. Managed options such as Cloudflare and Vercel remove the throughput problem from your infrastructure but rule out data residency control.

Is LiteLLM slow?

Not in absolute terms, and gateway overhead is small next to model inference either way. The constraint is concurrency: Python's execution model means throughput scales by adding worker processes and the shared state they require, rather than by using a single process more fully.

How hard is migrating off LiteLLM?

Low, in most cases. Both expose OpenAI-compatible APIs, so applications typically need only a base URL change. The work that remains is mapping provider configuration, budgets, and rate limits to the new gateway's equivalents.

Do I need to self-host an AI gateway?

Only if data residency, air-gapped operation, or full control over the request path are requirements. Where they are, managed options are eliminated regardless of throughput, and the comparison narrows to self-hostable gateways.

Does a faster gateway actually reduce user-visible latency?

Marginally on any single request, since inference dominates. The larger effect is capacity: lower per-request overhead means more concurrent requests per machine, which keeps tail latency stable under load instead of degrading as workers saturate.