liteLLM
Generative AI software
Large language model operationalization (LLMOps) software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
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What is liteLLM
liteLLM is an open-source LLM gateway and SDK that standardizes how applications call multiple large language model providers through an OpenAI-compatible interface. It is used by developers and platform teams to route, load-balance, and apply policies (such as keys, budgets, and rate limits) across model endpoints. The product focuses on provider abstraction and operational controls rather than end-user generative AI experiences. It is commonly deployed as a service in front of applications to centralize model access and governance.
Unified API across providers
liteLLM provides an OpenAI-compatible API layer that reduces application changes when switching or adding model providers. This abstraction helps teams avoid provider-specific SDK lock-in and simplifies multi-provider experimentation. It is particularly useful when different teams or environments require different model backends. The approach aligns with platform-style LLM enablement rather than single-application tooling.
Gateway controls and routing
The gateway supports operational features such as routing requests to different models, basic load balancing, and centralized key management. These capabilities help platform teams enforce consistent access patterns across multiple internal applications. Centralizing traffic also makes it easier to apply organization-wide limits and policies. This is a differentiator versus products focused mainly on end-user content creation or embedded assistants.
Open-source deployability
As open source, liteLLM can be self-hosted to meet internal security, network, and data-handling requirements. Teams can inspect the codebase and adapt it to custom authentication, logging, or deployment constraints. Self-hosting can also reduce dependency on a single SaaS control plane for model access. This is relevant for regulated environments that need tighter infrastructure control.
Requires engineering to operate
liteLLM is primarily a developer and platform component, not a turnkey end-user application. Organizations typically need to provision infrastructure, integrate authentication, and maintain deployments. Ongoing operations (monitoring, upgrades, incident response) remain the customer’s responsibility when self-hosted. This can be heavier than adopting a managed, end-user-oriented AI product.
Not a full LLMOps suite
While it provides gateway and abstraction functions, it does not replace broader LLMOps needs such as dataset management, evaluation pipelines, prompt lifecycle management, or end-to-end experiment tracking. Teams may need additional tools for testing, governance workflows, and model quality measurement. As a result, it often becomes one layer in a larger stack rather than the complete operational platform. Buyers should validate which controls are native versus requiring integrations.
Provider feature parity gaps
A unified interface can lag behind provider-specific features (for example, new parameters, tool-calling variants, or emerging modalities) until adapters are updated. Some advanced capabilities may require provider-specific configuration or may not map cleanly to an OpenAI-compatible schema. This can create edge cases where teams must bypass the abstraction for certain workloads. Organizations should assess how quickly updates track the providers they rely on.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Open Source (OSS) | $0 — Free | 100+ LLM provider integrations; Langfuse, Arize Phoenix, Langsmith, OpenTelemetry logging; Virtual Keys, Budgets, Teams; Load balancing, RPM/TPM limits; LLM guardrails; self-hosted OSS (no telemetry sent when self-hosting). |
| Enterprise (Cloud or Self-Hosted) | Custom pricing — Get a quote / Contact sales | Everything in OSS plus Enterprise support and custom SLAs; JWT auth, SSO, Audit Logs; All enterprise features; Cloud-hosted option available. |
Seller details
BerriAI
San Francisco, California, United States
2023
Private
https://www.litellm.ai/
https://x.com/LiteLLM
https://www.linkedin.com/company/berri-ai
