AI visibility report for BerriAI (LiteLLM)
Vertical: AI/ML Infrastructure & LLM Tools
AI search visibility benchmark across 5 platforms in AI/ML Infrastructure & LLM Tools.
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Presence Rate
Top-3 citations across 125 prompt × platform pairs
Sentiment
Peer Ranking
Key Metrics
Platform Breakdown
Gemini Search
Google AI Mode
Perplexity
Grok
ChatGPTOverview
LiteLLM, built by BerriAI (Y Combinator W23, 2023), is an open-source AI gateway and Python SDK that provides a unified, OpenAI-compatible interface for calling 100+ large language model providers, including OpenAI, Anthropic, Azure, Google Vertex AI, AWS Bedrock, and more. Designed primarily for platform and ML infrastructure teams, it centralizes LLM access governance with virtual keys, spend tracking, rate limiting, load balancing, automatic fallbacks, guardrails, and an admin dashboard. It also serves as an MCP gateway and A2A agent gateway. With over 45,000 GitHub stars, 240 million Docker pulls, and more than one billion requests served, LiteLLM is used by organizations including Netflix, Adobe, Stripe, and Lemonade. Available as a free open-source project or an enterprise-grade self-hosted or cloud deployment with SSO, audit logs, and custom SLAs, it is headquartered in San Francisco.
LiteLLM is an open-source AI gateway (proxy server) and Python SDK that gives developers and platform teams a single, OpenAI-compatible endpoint to access and govern 100+ LLM providers. Core capabilities include multi-provider routing with fallbacks, virtual key management, fine-grained cost and spend tracking per key/user/team/org, rate and budget enforcement, LLM guardrails, and integrations with observability tools. It also functions as an MCP gateway and A2A agent gateway. The enterprise edition adds SSO, audit logs, custom SLAs, and professional support.
Key Facts
- Founded
- 2023
- HQ
- San Francisco, CA, USA
- Founders
- Krrish Dholakia, Ishaan Jaffer
- Employees
- 10-19
- Funding
- $1.6M
- Status
- Private
Target users
Key Capabilities10
- Unified OpenAI-compatible API across 100+ LLM providers
- Virtual keys with per-key, per-team, per-user, and per-org budget enforcement
- Automatic cost and spend tracking across all integrated providers
- Load balancing and automatic fallback across multiple LLM deployments
- LLM guardrails for content filtering, PII masking, and safety policy enforcement
- Self-hosted AI gateway (proxy server) with admin dashboard UI
- MCP gateway for connecting MCP servers to any LLM
- A2A agent gateway for invoking LangGraph, Vertex AI, and other A2A agents
- SSO/SAML, JWT auth, and audit logs (enterprise tier)
- OpenTelemetry-compatible observability callbacks and Prometheus metrics
Key Use Cases8
- Platform/ML teams centralizing LLM access governance across an organization
- Cost tracking and chargeback across teams, projects, and business units
- Multi-provider LLM routing with automatic fallbacks for production reliability
- Rate limiting and budget enforcement for internal developer LLM usage
- Swapping or testing LLM providers without application code changes
- Deploying a self-hosted, OpenAI-compatible gateway for compliance-sensitive environments
- Connecting MCP tools and A2A agents through a unified secured gateway
- LLM observability via integration with Langfuse, MLflow, Helicone, and OpenTelemetry
BerriAI (LiteLLM) customer outcomes
Netflix uses LiteLLM to give developers day-0 LLM access, with new models available to internal users usually within a day of release. A staff software engineer credited LiteLLM with saving the team months of work by eliminating the need to transform inputs and outputs across pro
Lemonade's GenAI platform team uses LiteLLM alongside Langfuse to streamline the complexities of managing multiple LLM models, with the company's Principal Architect describing the experience as 'outstanding.'
Recent Trend
How AI describes BerriAI (LiteLLM)
No concise AI response excerpt is available for this brand yet.
Most cited sources3
Alternatives in AI/ML Infrastructure & LLM Tools6
LiteLLM positions itself as the leading open-source AI gateway for platform and ML infrastructure teams that need to centralize LLM access governance across an organization.
- Its primary differentiation is breadth of provider support (100+ LLMs in a unified OpenAI-compatible format), a permissive open-source core with an enterprise tier, and a focus on operational concerns—spend tracking, virtual keys, load balancing, guardrails—rather than LLM orchestration or evaluation.
- It competes most directly with other LLM proxy/gateway tools (Helicone, Portkey) and overlaps with LLM observability platforms (Langfuse, Braintrust, MLflow).
- Unlike LLM inference providers such as Together AI or Fireworks AI, LiteLLM is provider-agnostic and routes to them rather than competing for compute workloads.
- Its open-source flywheel (45k+ GitHub stars, 1,000+ contributors) and enterprise self-hosted model allow it to land in developer teams and expand into enterprise platform contracts.
Reviews
Praised
- Unified API across 100+ LLM providers in one interface
- Drop-in OpenAI-compatible replacement — no code changes when switching models
- Eliminates vendor lock-in across LLM providers
- Caching and load balancing between multiple AI services
- Clean integration with Langfuse for observability and prompt monitoring
- Fast time-to-value and easy initial setup
- Active open-source community with frequent releases
Criticized
- March 2026 supply chain attack on PyPI packages 1.82.7 and 1.82.8
- Multiple security CVEs disclosed in 2026, including authentication bypass
- Full observability requires separate third-party tools
- Enterprise pricing is opaque and requires direct sales contact
- High open GitHub issue count (~1,200 open issues)
- Occasional reliability and configuration complexity at enterprise scale
LiteLLM has no scored reviews on G2 (unclaimed profile, 0 reviews as of April 2026). On Product Hunt, the tool earns consistently strong qualitative praise, with users and makers from companies including Budibase, JDoodle.ai, Crossnode, and Athina AI highlighting its ability to unify multi-provider LLM access, eliminate vendor lock-in, and integrate cleanly with observability tools like Langfuse. No negative product functionality reviews were identified on public platforms; however, the March 2026 supply chain incident generated substantial negative press coverage in the security community.
Pricing
LiteLLM offers a free, open-source tier available via GitHub and PyPI that includes 100+ LLM provider integrations, virtual keys, budgets, load balancing, rate limiting, Langfuse/OpenTelemetry logging, and LLM guardrails. An Enterprise tier (cloud-hosted or self-hosted) adds JWT auth, SSO/SAML, audit logs, custom SLAs, enterprise support, and dedicated Slack/Discord support; pricing is not publicly listed and requires a direct inquiry or a 30-day trial request. The Enterprise tier is also available via AWS Marketplace under a private offer model.
Limitations
- LiteLLM does not offer native LLM evaluation or prompt experimentation features, requiring integration with separate tools (Langfuse, Braintrust, MLflow) for full observability.
- In March 2026, versions 1.82.7 and 1.82.8 were subject to a supply chain attack via a compromised CI/CD pipeline (linked to the Trivy security scanner compromise), exposing users who installed those PyPI packages to a credential-stealing payload; the team subsequently released a clean v1.83.0 and overhauled its CI/CD pipeline.
- Additional security CVEs disclosed in April 2026 include an authentication bypass (CVE-2026-35030, Critical, affecting only deployments with JWT auth explicitly enabled) and a privilege escalation issue (CVE-2026-35029, High).
- Enterprise pricing is not publicly listed, requiring direct contact.
- The open-source edition has over 1,200 open GitHub issues.
- Full enterprise compliance features (SSO, audit logs, custom SLAs) are gated behind the paid tier.
Frequently asked questions
Topic Coverage
Prompt-Level Results
| Prompt | Gemini Search | Perplexity | Grok | ChatGPT | Google AI Mode |
|---|---|---|---|---|---|
Capability1/5 cited (20%) | |||||
I'm evaluating managed LLM inference platforms versus self-hosted GPU instances for a high-traffic workload — what are the key trade-offs and what should I look at? | |||||
Which serverless GPU platforms support model fine-tuning jobs, not just inference — what are the practical compute limits to know about? | |||||
What ML platforms handle dataset versioning alongside model versioning so you can reliably reproduce a training run from six months ago? | |||||
Which AI observability tools are best at detecting prompt injection attempts and guardrail violations in production LLM apps? | |||||
Which LLM orchestration frameworks handle long-running multi-agent workflows reliably — including surviving infrastructure restarts when a task takes hours? | |||||
Developer Experience0/5 cited (0%) | |||||
Which LLM observability platforms handle prompt versioning well — can you roll back to a previous prompt version and compare outputs side by side? | |||||
What ML experiment tracking tools handle multi-user collaboration well — so multiple data scientists can work on the same project without stepping on each other's runs? | |||||
Which AI infrastructure platforms support running the same orchestration logic locally against a mock LLM before deploying to production? | |||||
What are the best tools for debugging a multi-step AI agent pipeline — specifically tracing which tool call or LLM response caused a failure? | |||||
Looking for an LLM evaluation platform a solo engineer can get running in a day without deep ML expertise — what are my options? | |||||
Integrations & Ecosystem0/5 cited (0%) | |||||
What tools support automatically running LLM evals on every pull request as part of a CI/CD pipeline before deploying prompt changes to production? | |||||
Which AI/ML platforms have the best compliance story for SOC 2 and data residency — ensuring training data and model outputs stay in a specific region? | |||||
Which LLM observability platforms support exporting trace data to BigQuery or Snowflake for custom analysis? | |||||
Which ML experiment tracking platforms integrate best with PyTorch training loops — minimal code changes to start logging runs? | |||||
What AI infrastructure platforms handle multi-model setups well — letting you switch between LLM providers and open-source models without rewriting application code? | |||||
Performance & Reliability1/5 cited (20%) | |||||
Which managed LLM inference platforms handle cold starts well — is there a way to keep a model warm without paying for idle GPU time? | |||||
Which LLM proxy gateway tools add observability without significant latency overhead — worth it for latency-sensitive production apps? | |||||
What LLM gateway or routing tools support automatic fallback when a primary model provider goes down in production? | |||||
What monitoring tools should you set up for a production LLM pipeline to catch quality regressions like answer relevance drift or rising hallucination rates? | |||||
What LLM infrastructure platforms give the best cost-to-latency balance for a high-throughput app doing 10,000 requests per hour? | |||||
Setup & First Run0/5 cited (0%) | |||||
What's the easiest LLM gateway to set up that adds caching, rate limiting, and cost tracking across multiple model providers without custom code? | |||||
What tools let you set up a RAG pipeline evaluation framework to measure retrieval quality and answer accuracy before going to production? | |||||
Which LLM orchestration frameworks are best for onboarding a software engineering team with no ML background — what's realistic for the first week? | |||||
What platforms can affordably serve a fine-tuned 7B parameter model with low latency for a production app without requiring a dedicated ML team? | |||||
What are the best ML experiment tracking tools for a team currently logging metrics to spreadsheets — which ones get you value fast with minimal setup? | |||||
Strengths1
What LLM gateway or routing tools support automatic fallback when a primary model provider goes down in production?
Avg # 3.0 · 1 platform
Gaps5
What tools support automatically running LLM evals on every pull request as part of a CI/CD pipeline before deploying prompt changes to production?
Competitors on 2 platforms
What are the best tools for debugging a multi-step AI agent pipeline — specifically tracing which tool call or LLM response caused a failure?
Competitors on 2 platforms
What monitoring tools should you set up for a production LLM pipeline to catch quality regressions like answer relevance drift or rising hallucination rates?
Competitors on 2 platforms
Which ML experiment tracking platforms integrate best with PyTorch training loops — minimal code changes to start logging runs?
Competitors on 2 platforms
What's the easiest LLM gateway to set up that adds caching, rate limiting, and cost tracking across multiple model providers without custom code?
Competitors on 1 platform
Vertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Braintrust | 14.4% | 39.8% | 0.8% | 0.0% | 13.6% | #8.2 | +0.23 |
| 2 | LangChain | 9.6% | 19.4% | 3.2% | 0.0% | 8.8% | #11.1 | +0.19 |
| 3 | Weights & Biases | 4.8% | 8.7% | 0.8% | 0.0% | 4.0% | #6.6 | +0.15 |
| 4 | Langfuse | 4.8% | 11.7% | 0.0% | 1.6% | 4.8% | #9.9 | +0.56 |
| 5 | Modal Labs | 4.0% | 8.7% | 1.6% | 3.2% | 4.0% | #8.0 | +0.00 |
| 6 | MLflow | 3.2% | 4.9% | 0.0% | 0.0% | 3.2% | #6.0 | +0.00 |
| 7 | Anyscale | 1.6% | 2.9% | 1.6% | 0.8% | 1.6% | #17.7 | +0.00 |
| 8 | BerriAI (LiteLLM) | 1.6% | 2.9% | 1.6% | 0.0% | 1.6% | #17.7 | +0.00 |
| 9 | Comet ML | 0.8% | 1.0% | 0.0% | 0.0% | 0.8% | #10.0 | +0.80 |
| 10 | Fireworks AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 11 | Helicone | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 12 | Replicate | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 13 | Together AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
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