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    LiteLLM LLM Gateway - Self Hosted (requires Private Offer)

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    Sold by: LiteLLM 
    ***REQUIRES PRIVATE OFFER*** To purchase LiteLLM Enterprise Self-Hosted, please reach out to sales@berri.ai for a Private Offer. LiteLLM is an OpenAI compatible Proxy Server (LLM Gateway) to call 2,000+ LLM APIs using the OpenAI format Bedrock, Huggingface, VertexAI, TogetherAI, Azure OpenAI, OpenAI, etc. Get started with Opensource LiteLLM here: https://github.com/BerriAI/litellm (40,000+ Github Stars)
    4.5

    Overview

    REQUIRES PRIVATE OFFER

    With LiteLLM Proxy Server Self-Hosted, you'll get access to a Proxy Server to call 2,000+ LLMs in a unified interface where you'll be able to track spend, set budgets per virtual key and users.

    You'll be able to set budgets & rate limits per project, API key, and model on OpenAI Proxy Servers.

    You can also translate inputs to the provider's completion, embedding, and image_generation endpoints as well as retry/fallback logic across multiple LLM deployments.

    Highlights

    • Contact sales@berri.ai for a Private Offer to purchase. Call 2,000+ LLM APIs in OpenAI format https://models.litellm.ai/
    • Track metrics on Prometheus and send logs to s3, GCS Bucket, Langfuse, OpenTelemetry, Datadog https://docs.litellm.ai/docs/proxy/logging
    • Get started with Opensource LiteLLM here: https://github.com/BerriAI/litellm (33,000+ Github Stars)

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    Pricing

    LiteLLM LLM Gateway - Self Hosted (requires Private Offer)

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    Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
    Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator  to estimate your infrastructure costs.

    12-month contract (1)

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    Dimension
    Description
    Cost/12 months
    LiteLLM Enterprise
    All features under LiteLLM Enterprise License, SSO sign on, Feature Prioritization, Proffesional Support, Custom SLAs
    $0.01

    Vendor refund policy

    All fees are non-cancellable and non-refundable except as required by law.

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    Software as a Service (SaaS)

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    For dedicated support, please send an email to support@berri.ai 

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    Overview

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    AI generated from product descriptions
    Multi-Provider LLM Integration
    Supports calling 2,000+ LLM APIs through a unified interface compatible with OpenAI format, including Bedrock, Huggingface, VertexAI, TogetherAI, Azure OpenAI, and OpenAI.
    Cost and Rate Management
    Enables setting budgets and rate limits per project, API key, model, and virtual key with spend tracking capabilities.
    Observability and Logging
    Integrates with Prometheus for metrics tracking and supports log delivery to S3, GCS Bucket, Langfuse, OpenTelemetry, and Datadog.
    Request Translation and Routing
    Translates inputs to provider-specific completion, embedding, and image_generation endpoints with retry and fallback logic across multiple LLM deployments.
    Self-Hosted Deployment
    Provides self-hosted proxy server architecture for on-premises deployment and control of LLM gateway infrastructure.
    Prompt Engineering and Comparison
    Side-by-side comparisons between multiple prompts, parameters, models, and model providers across test cases for optimization.
    Workflow Orchestration
    Ability to prototype and deploy AI workflows that chain business logic, data, APIs, and dynamic prompts for various use cases.
    Evaluation and Testing Framework
    Creation of test case banks to evaluate and identify optimal prompt and model combinations across multiple scenarios.
    Semantic Search and Retrieval
    Document retrieval capability to extract company-specific data and use it as context in LLM calls.
    Monitoring and Proxy Infrastructure
    Reliable proxy layer connecting applications to model providers with request tracking for debugging and quality monitoring.
    AI Gateway and Request Routing
    AI Gateway with load balancing capabilities between multiple language models and fallback mechanisms for production reliability
    Observability and Monitoring
    Full visibility and observability over AI applications with comprehensive logging and monitoring through a centralized dashboard
    Prompt Management
    Centralized prompt management system for versioning, organization, and control of prompts across AI applications
    Security and Compliance
    Enterprise-grade security with ISO, SOC2, HIPAA, and GDPR compliance certifications and security policy enforcement
    Guardrails and A/B Testing
    Built-in guardrails for output validation and A/B testing capabilities for comparing model performance and behavior

    Contract

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    Standard contract

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    Ratings and reviews

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    4.5
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    1 external reviews
    External reviews are from PeerSpot .
    Akashkhurana Hirana

    Centralized AI routing has strengthened data security and simplified multi-model workflows

    Reviewed on Jun 13, 2026
    Review provided by PeerSpot

    What is our primary use case?

    Our main use case for LLM Gateway  is that our company has partnerships with multiple LLM providers including OpenAI, Claude, and Gemini . LLM Gateway  acts as an interface between all three providers. I would describe it as a router that functions as middleware between our application and the AI providers so that we do not need to give or share API keys to each team.

    Our team calls LLM Gateway from their application, and all the keys and routing configurations are present in LLM Gateway. Its responsibility is to connect with Claude, OpenAI, or Gemini  based on the request we receive.

    We have an application in which users can ask anything. For example, if a user is asking a general question, we call LLM Gateway and pass the model name as ChatGPT. It internally uses ChatGPT itself. If the question is related to the application we created, it internally uses RAG and goes to Claude. LLM Gateway is responsible for redirecting the request based on context.

    LLM Gateway also has an additional feature where if one of the models is unavailable at a time, it automatically redirects the request to another model, so there is no downtime in the application. The automatic failover feature ensures that if one model is not available, LLM Gateway redirects the request to another model.

    What is most valuable?

    The best features LLM Gateway offers include multi-provider AI access and the ability to access around 200 plus models available in the market. We just need to pass our key and set up this one, and it can access all the available models. Apart from this, it automatically routes the request based on context if we set it in LLM Gateway. Another feature is the automatic failover functionality where if something goes wrong, it redirects the request to another model. LLM Gateway also provides usage analytics with a dashboard where we can check the current usage of each model and see how many requests are going to each model. It persists data for around 30 days, so we can review usage over the last month. LLM Gateway can be self-hosted as well, which is beneficial for large companies with security concerns.

    I find multi-provider access and failover to be the most valuable features day-to-day. Multi-provider access integrates all available models, acting as a router between the application and LLM Gateway. If my application is using four different models, I only need to call LLM Gateway, which manages everything. We also do not need to share sensitive API keys, as the developer can directly call LLM Gateway, which handles everything seamlessly. The failover feature automatically redirects requests if something goes wrong in one model, and it is incredibly easy to configure. It does not take more than a minute to set up.

    One positive impact of LLM Gateway on my organization is reducing security risk. If we give API keys to everyone, they can misuse them outside the organization. However, we no longer share API keys, as users just need to call our LLM Gateway, and the API keys remain secret and contained within our on-premises setup. Security-wise, it has significantly reduced our organization's risk.

    What needs improvement?

    Regarding improvements, I think the pricing can be more competitive. LLM Gateway takes 5% of the token usage, which feels a bit high. While they do have a free tier, the costs for the enterprise edition are somewhat high. As a new product in the market, it should charge less compared to competitors. However, I think the cost is comparable or slightly higher.

    For how long have I used the solution?

    I have been using LLM Gateway for around 1.5 years. It is a new product in the market.

    What do I think about the stability of the solution?

    LLM Gateway is stable. It is a new product, but it is heading in the right direction.

    What do I think about the scalability of the solution?

    Currently, we are using around 20 models, and it works fine. LLM Gateway claims integration with around 200 models, but we have only utilized 20 in our organization so far.

    How are customer service and support?

    The customer support and documentation for LLM Gateway are pretty good. Although the community is a bit sparse because of its newness, the available support is very effective. I rate the customer support a perfect 10.

    Which solution did I use previously and why did I switch?

    I previously used Portkey  in a different organization but have not switched from Portkey  to LLM Gateway within the same organization.

    How was the initial setup?

    I usually start with the free tier, which is very good. For the enterprise version, LLM Gateway charges as other products do, but the setup time is quick, under a minute, with no cost for the free tier. After using LLM Gateway, we see that our security risks have reduced. In my organization, we do not only look at ROI; we also consider security threats. Since adopting LLM Gateway, the complexity of our projects has decreased, and the security concerns have lessened. I estimate it has saved us around 20-30% of our time, and around 30% sounds reasonable.

    What was our ROI?

    After using LLM Gateway, we see that our security risks have reduced. In my organization, we do not only look at ROI, but we also consider security threats. Since adopting LLM Gateway, the complexity of our projects has decreased, and the security concerns have lessened. I estimate it has saved us around 20-30% of our time, and around 30% sounds reasonable.

    What's my experience with pricing, setup cost, and licensing?

    I usually start with the free tier, which is very good. For the enterprise version, LLM Gateway charges as other products do, but the setup time is quick, under a minute, with no cost for the free tier.

    Which other solutions did I evaluate?

    I have not evaluated other options. The setup cost, time, and the free tier availability made LLM Gateway an easy choice for us.

    What other advice do I have?

    The accuracy of LLM Gateway's output is quite good. If one model is down, it automatically redirects requests to another model, which is a very beneficial feature. My advice for others looking into using LLM Gateway is to start with it. It takes very little time to set up and has a user-friendly dashboard that displays model usage. You can also set thresholds, specifying the number of tokens or costs for each model, which is very convenient. Depending on the product size, since it is new in the market, our usage has been satisfactory. I have given this review a rating of 9 out of 10.

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