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Mistral Makes Large 4 Available as Open Weights29/10/26 · Mistral AI|Gemini 4 Carbon tops the Artificial Analysis Intelligence Index v529/10/26 · Google|Mistral Large 4 now available on Hugging Face29/10/26 · Mistral AI|Google Introduces "Gemini 4 Carbon" as Next Step in Its Model Lineup28/10/26 · Google|DeepMind unveils Genie 4, a new generation of world model27/10/26 · Google DeepMind|Anthropic files IPO prospectus, with risk disclosures taking center stage26/10/26 · Anthropic|Robot policy trained only in simulation matches top real-world systems26/10/26|OpenAI unveils GPT-6.1 Terra, a model built for computer use26/10/26 · OpenAI|New benchmark checks whether LLMs actually test their temporal medical claims29/10/26|An ODE-based sampling method speeds up discrete diffusion models29/10/26|OpenAI revises its Model Spec with five guiding values for AI behavior29/10/26 · OpenAI|Inside Proton's internal clash over its AI pivot29/10/26 · Proton|Mistral Makes Large 4 Available as Open Weights29/10/26 · Mistral AI|Gemini 4 Carbon tops the Artificial Analysis Intelligence Index v529/10/26 · Google|Mistral Large 4 now available on Hugging Face29/10/26 · Mistral AI|Google Introduces "Gemini 4 Carbon" as Next Step in Its Model Lineup28/10/26 · Google|DeepMind unveils Genie 4, a new generation of world model27/10/26 · Google DeepMind|Anthropic files IPO prospectus, with risk disclosures taking center stage26/10/26 · Anthropic|Robot policy trained only in simulation matches top real-world systems26/10/26|OpenAI unveils GPT-6.1 Terra, a model built for computer use26/10/26 · OpenAI|New benchmark checks whether LLMs actually test their temporal medical claims29/10/26|An ODE-based sampling method speeds up discrete diffusion models29/10/26|OpenAI revises its Model Spec with five guiding values for AI behavior29/10/26 · OpenAI|Inside Proton's internal clash over its AI pivot29/10/26 · Proton|
AppRisingofficial publisher

Bifrost

App : A product you use directly

AI gateway in Go, very low latency, also serving as an MCP gateway.

The high-load challenger to LiteLLM for demanding workloads.

GitHub · 9,083 ★
Overall score
8.4 /10
Security: nothing to flag
Utility
7.5
Reliability
10.0
Security
No caveat on record
Ease
Technical · Technical

Security reflects where data goes, requested permissions and incidents on record at the verification date. It is not a guarantee of zero risk: check the deployment mode and the connections you enable.

What it does, concretely

Managing multiple AI providers in parallel often means maintaining as many separate integrations, with different API formats and no automatic failover mechanism when a provider goes down. Bifrost solves this by exposing a single OpenAI-compatible API that centralises access to more than 23 providers, including OpenAI, Anthropic, AWS Bedrock, Google Vertex, and Azure.

In practice, Bifrost installs in seconds via Docker or NPX, then is configured through a built-in web interface. It automatically handles failover between providers, load balancing across multiple API keys, and semantic caching of responses to reduce costs and latency. Measured overhead under load is under 15 microseconds per request.

Bifrost is aimed at technical teams deploying AI applications in production who need reliability, cost control, and observability. It also suits Go developers who want to embed the gateway directly into their code using the native SDK.

It is not suited to non-technical users looking for a consumer-facing interface, nor to those who only need a single provider without high-availability requirements. Advanced features such as clustering, guardrails, and the MCP gateway are reserved for enterprise deployments.

What it offers

  • ·Unified access to more than 23 AI providers through a single OpenAI-compatible API.
  • ·Automatic failover between providers and models in case of outage, with zero downtime.
  • ·Intelligent load balancing across multiple API keys and providers.
  • ·Semantic response caching to reduce costs and latency on similar requests.
  • ·Support for the Model Context Protocol (MCP) to connect models to external tools such as filesystems, web search, or databases (paid).
  • ·Budget management via virtual keys, teams, and customers, with fine-grained access control.
  • ·Native observability with Prometheus metrics, distributed tracing, and request logging.
  • ·Multi-node clustering and custom plugins for large-scale deployments (paid).

In practice

Price
Freemium
Commercial use
Commercial use: yes
Available on
docker, linux, cli
Licence
Apache-2.0

Security and data

Rating
Security: nothing to flag
Your data
Data stays local
Maintenance
Active
Last release
21 October 2026

Links and repository checked on 25 October 2026 · added on 1 June 2025

How to install Bifrost

1. Start Bifrost via Docker: docker run -p 8080:8080 -v $(pwd)/data:/app/data maximhq/bifrost 2. Or use NPX without prior installation: npx -y @maximhq/bifrost 3. Open the web interface at http://localhost:8080 to configure your providers. 4. Add your API keys via environment variables rather than in plain text in the configuration. 5. Make a first test call with curl to http://localhost:8080/v1/chat/completions to verify the gateway responds correctly.
docker run -p 8080:8080 maximhq/bifrost

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