Bifrost
Fast open-source LLM gateway with sub-millisecond overhead at scale
Overview
Bifrost is an open-source, Apache 2.0 LLM gateway built in Go by Maxim AI, unifying access to 1000+ models across providers through a single OpenAI-compatible API. It adds adaptive load balancing, cluster mode, automatic failover, semantic caching, guardrails and built-in OpenTelemetry observability, with the team claiming far lower overhead than alternatives. It can be self-hosted via Docker or started in seconds with NPX, with a managed enterprise option.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 4 Jun 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.
ASEAN Perspective
Bifrost in Southeast Asia
ASEAN-region availability and pricing notes coming soon. Drop the editorial team a note via /contact/ if you can supply local context (Singapore/Malaysia/Indonesia/Thailand/Vietnam).
Bifrost targets the performance ceiling of LLM gateways, with the Go implementation and benchmarks claiming dramatically lower overhead than LiteLLM at high RPS. As fully open-source Apache 2.0 software with cluster mode and built-in OTel observability, it is well suited to teams that want a self-hosted, high-throughput proxy.
It pairs naturally with Maxim AI's evaluation platform but works standalone. For organisations that prefer self-hosting over a markup-based SaaS gateway, Bifrost is one of the strongest open options available.
About this listing
This entry was compiled from publicly available data including Bifrost's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with Bifrost unless explicitly stated.
Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
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