AI visibility report
LiteLLM ranks #7 in LLM Observability Evals & Gateways AI search.
Outside the top three on 20 of the 25 prompts buyers actually ask.
Braintrust is cited on 17 of those losses.
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Start free trial#7 among 11 vendors · still absent from 94.4% of tracked prompt responses
Top-3 citations across 125 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
ChatGPT
Gemini Search
Google AI Mode
Perplexity
Bing CopilotNarrower footprint, stronger tone. LiteLLM ranks #7 on presence but #3 on sentiment. That means the brand is framed well when it appears, but still needs broader prompt-response coverage.
Where LiteLLM is losing
Prompts where competitors are visible and LiteLLM is not.
These prompt-level losses are the first prompts to track and repair.
Where LiteLLM is winning2
What are the most production-hardened LLM gateway options for an enterprise team needing 99.9% uptime with circuit-breaker support?
Avg # 1.0 · 1 platform
Which LLM gateways add the least latency overhead when routing between LLM providers — safe to use in production for sub-500ms SLAs?
Avg # 1.0 · 1 platform
Where LiteLLM is losing5
I'm looking for an LLM observability platform with a great team collaboration workflow — where engineers and PMs can both review trace quality without SQL knowledge.
Competitors on 5 platforms
Track this promptWhich LLM eval platforms support async evaluation at scale without blocking the inference path or adding latency for end users?
Competitors on 5 platforms
Track this promptWhich LLM tracing platforms export trace data to a data warehouse so analysts can run custom eval queries alongside product metrics?
Competitors on 5 platforms
Track this promptWhich LLM observability platforms stay reliable under traffic spikes from batch eval jobs running thousands of LLM calls simultaneously?
Competitors on 4 platforms
Track this promptWhich LLM observability platforms can a small team get running against a production RAG pipeline in under a day?
Competitors on 4 platforms
Track this prompt
Track LiteLLM daily before the next report refresh.
Track these gapsResearch dossierCapabilities, use cases, sources, reviews, pricing, and FAQ
Overview
BerriAI's LiteLLM is an open-source AI Gateway and Python SDK that provides a single, unified, OpenAI-compatible interface for calling 100+ large language model (LLM) providers, including OpenAI, Anthropic, Azure, AWS Bedrock, Google Vertex AI, Cohere, and Mistral. Founded in 2023 and backed by Y Combinator (W23), LiteLLM is offered both as a lightweight Python library for developers and as a self-hosted proxy server for platform teams managing LLM access across organizations. Core capabilities include multi-provider load balancing and fallbacks, virtual key management, per-user/team/org spend tracking, rate limiting, LLM guardrails, and observability integrations. With over 45,000 GitHub stars, 240 million Docker pulls, and 1 billion requests served, it is one of the most widely adopted open-source LLM infrastructure tools.
LiteLLM (by BerriAI) is an open-source AI Gateway and Python SDK that standardizes access to 100+ LLM providers under a single OpenAI-format API. It can be used as an embedded Python library or deployed as a standalone FastAPI proxy server with virtual keys, spend tracking, guardrails, load balancing, observability integrations, and an admin dashboard — enabling platform teams to give developers governed LLM access at scale.
Key Facts
- Founded
- 2023
- HQ
- San Francisco, CA, US
- Founders
- Krrish Dholakia, Ishaan Jaffer
- Employees
- 11-50
- Funding
- $2.1M
- ARR
- ~$2.5M
- Status
- Private
Target users
Key Capabilities10
- Unified OpenAI-format API across 100+ LLM providers
- AI Gateway / Proxy Server with virtual keys and admin dashboard UI
- Spend tracking and budget enforcement per user, team, org, and tag
- Load balancing and automatic fallbacks across LLM deployments
- LLM guardrails for content filtering and PII masking
- Rate limiting and per-key/team/project quota management
- LLM observability via OpenTelemetry, Langfuse, Arize, and others
- MCP (Model Context Protocol) Gateway for tool-augmented LLM calls
- A2A Agent Gateway supporting multi-agent protocol routing
- 8ms P95 latency at 1,000 RPS (self-reported benchmark)
Key Use Cases8
- Platform and ML teams providing centralized LLM access to large developer organizations
- Multi-provider LLM routing with automatic failover for production reliability
- Cross-provider cost tracking and AI budget governance
- Self-hosted LLM gateway for regulated or air-gapped environments
- Day-0 model access enablement when new LLM providers launch
- Standardizing LLM API calls across heterogeneous provider stacks
- Centralized prompt management and observability logging
- MCP and A2A agent traffic routing and access control
LiteLLM customer outcomes
Saved months of engineering work on provider integration
Netflix's GenAI platform team uses LiteLLM to provide developers with Day-0 access to new LLM models within a day of release, eliminating the need to transform inputs and outputs across providers.
Lemonade's GenAI platform uses LiteLLM alongside Langfuse to streamline the complexity of managing multiple LLM models across their platform.
Recent Trend
How AI describes LiteLLM3
Its instrumentation covers LLM providers, vector databases, LangChain/LangGraph, LlamaIndex, CrewAI, LiteLLM, OpenAI Agents, MCP, etc. \[1\] So the architecture can simply be: `application → OpenLLMetry → OTel Collector → existing tracing backend` Th...
Which LLM observability tools work with OpenTelemetry-compatible backends so we can consolidate LLM traces alongside existing service traces?
...utm_source=chatgpt.com) | ✅ | ✅ | ✅ Explicit Trace ID across all attempts | Best overall for this requirement | | LiteLLM | ✅ | ✅ | ✅ Via OpenTelemetry / observability integrations | Best open-...
Which LLM gateways handle multi-provider fallback and automatic retries while preserving full trace context across the switch?
It specifically supports LangChain, LangGraph, LiteLLM, Pydantic AI, OpenAI, Anthropic, Bedrock, etc. B Braintrust So if by "without rewriting existing code"...
What's the fastest LLM tracing platform for instrumenting a Python-based agent framework without rewriting existing code?
Most cited sources8
6LiteLLM — Open-Source AI Gateway & LLM Proxy
litellm.ai·Landing Page
- D5
OpenTelemetry v1 - LiteLLM
docs.litellm.ai·Documentation
- 5
AI Gateway for Agents, MCPs & LLM Routing | LiteLLM
litellm.ai·Product Page
- 4
Enterprise AI Gateway for Secure LLM Access | LiteLLM
litellm.ai·Landing Page
- D2
Fallbacks | liteLLM
docs.litellm.ai·Documentation
- D2
Benchmarks | liteLLM
docs.litellm.ai·Documentation
Alternatives in LLM Observability Evals & Gateways6
LiteLLM positions itself as the de facto open-source AI Gateway and Python SDK for unified multi-LLM access management.
- Its primary differentiation is an OpenAI-format-compatible proxy layer that abstracts 100+ LLM providers behind a single, standardized interface, contrasting with pure observability tools (Arize, Langfuse) or evaluation platforms (Galileo, Confident AI).
- Against managed gateway competitors like Portkey and Helicone, LiteLLM competes on open-source auditability, self-hosted deployment, and breadth of provider coverage (100+ LLMs).
- With 45K+ GitHub stars and 240M+ Docker pulls, it occupies a high-volume developer mindshare position, while its enterprise tier targets platform teams needing SSO, audit logs, and custom SLAs at scale.
Reviews
Praised
- Unified API across 100+ LLM providers with no SDK juggling
- Drop-in OpenAI compatibility — swap providers without rewriting code
- Easy load balancing and fallback configuration
- Cost tracking and spend management per team/user/org
- Open-source and self-hostable for compliance and control
- Pairs well with observability tools like Langfuse
- Fast release cadence and large contributor community
- Minimal latency overhead vs. direct provider calls
Criticized
- Production proxy requires managing Redis and PostgreSQL infrastructure
- SSO, RBAC, and audit logs paywalled behind Enterprise license
- Advanced/conditional routing logic is limited
- 2025 supply chain security incident (quickly remediated)
- Documentation can lag behind rapid release cadence
- Not suitable for complex agent orchestration without pairing with LangChain/LlamaIndex
- Open-source code quality concerns raised in community discussions
LiteLLM has no verified reviews on G2 (unclaimed profile as of research date). On Product Hunt, reviewer sentiment is strongly positive, with practitioners from companies including Budibase, JDoodle.ai, Crossnode, and Athina AI praising its multi-provider abstraction, OpenAI-compatible proxy, caching, load balancing, and seamless pairing with observability tools like Langfuse. Critical feedback in technical community discussions (Hacker News, Medium, TrueFoundry blog) centers on the operational complexity of running the proxy in production (Redis + PostgreSQL dependencies), enterprise feature paywalling (SSO/RBAC), and limited support for highly custom routing logic.
Pricing
LiteLLM follows an open-core model. The open-source tier is free (MIT license) and includes 100+ LLM provider integrations, virtual keys, budgets, teams, load balancing, RPM/TPM limits, LLM guardrails, and logging integrations (Langfuse, Arize Phoenix, LangSmith, OpenTelemetry). The Enterprise tier (cloud or self-hosted) is priced on a custom, contact-for-pricing basis and adds SSO/SAML, JWT Auth, audit logs, enterprise support with custom SLAs, and a 30-day trial. No public per-seat or usage-based pricing is disclosed.
Limitations
- Production deployment of the LiteLLM proxy server requires managing external dependencies (Redis for caching/rate limiting, PostgreSQL for spend logs and API keys), adding infrastructure operational burden.
- Enterprise features including SSO/SAML, RBAC, audit logs, and JWT auth are locked behind a paid Enterprise license, making governance at scale costly.
- Advanced model routing logic (e.g., prompt-content-conditional routing, highly custom post-processing) is limited compared to custom-built orchestrators.
- SQLite and broader database backends are not supported by the proxy (by stated design).
- In early 2025, LiteLLM experienced a supply chain security incident in which compromised versions (1.82.7–1.82.8) were published to PyPI via a hijacked Trivy CI dependency; the affected packages were removed within approximately three hours and the team issued a public security townhall.
Frequently asked questions
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | Gemini Search | Google AI Mode | Perplexity | ChatGPT | Bing Copilot |
|---|---|---|---|---|---|
Capability2/5 cited (40%) | |||||
Which LLM observability tools handle PII redaction and data masking in traces for teams with HIPAA or GDPR compliance requirements? | Your brand was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which LLM evaluation platforms support custom rubric-based scoring for domain-specific correctness beyond generic faithfulness metrics? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Looking for an eval platform that supports automated safety and toxicity scoring on LLM outputs at scale — what are my options? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
What platforms support end-to-end tracing of multi-agent pipelines including tool calls, retrieval steps, and sub-agent spawning? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Which LLM gateways handle multi-provider fallback and automatic retries while preserving full trace context across the switch? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited |
Developer Experience0/5 cited (0%) | |||||
What LLM observability tools do ML engineering teams typically use to annotate and review production traces for quality feedback? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
I'm looking for an LLM observability platform with a great team collaboration workflow — where engineers and PMs can both review trace quality without SQL knowledge. | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Which LLM tracing platforms make it easiest to replay a failed multi-step agent run and pinpoint exactly where reasoning went wrong? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which LLM eval platforms have the best prompt playground experience for iterating on system prompts against a saved test dataset? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which LLM gateway tools give developers the best real-time cost and token usage visibility across multiple LLM providers during development? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Integrations & Ecosystem1/5 cited (20%) | |||||
Which LLM observability tools work with OpenTelemetry-compatible backends so we can consolidate LLM traces alongside existing service traces? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which LLM observability platforms integrate natively with the most popular agent frameworks so traces appear automatically with no manual instrumentation? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
I'm evaluating LLM eval platforms — which ones integrate with version control to tie prompt regressions back to specific code or config changes? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Which LLM tracing platforms export trace data to a data warehouse so analysts can run custom eval queries alongside product metrics? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
What LLM gateway tools integrate best with secret managers and internal auth systems for enterprise teams rolling out to multiple product teams? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited |
Performance & Reliability2/5 cited (40%) | |||||
Which LLM observability platforms stay reliable under traffic spikes from batch eval jobs running thousands of LLM calls simultaneously? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
What are the most production-hardened LLM gateway options for an enterprise team needing 99.9% uptime with circuit-breaker support? | Neither your brand nor a competitor was cited | Your brand was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What LLM tracing platforms handle high-throughput production workloads — millions of traces per day — without degrading query performance? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which LLM eval platforms support async evaluation at scale without blocking the inference path or adding latency for end users? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Which LLM gateways add the least latency overhead when routing between LLM providers — safe to use in production for sub-500ms SLAs? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand was cited | Neither your brand nor a competitor was cited |
Setup & First Run2/5 cited (40%) | |||||
Which LLM observability platforms can a small team get running against a production RAG pipeline in under a day? | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
What are the best OpenTelemetry-compatible tracing backends for LLM apps that work out of the box without custom span parsing? | A competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | A competitor was cited | Neither your brand nor a competitor was cited |
What's the fastest LLM tracing platform for instrumenting a Python-based agent framework without rewriting existing code? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
I'm evaluating LLM gateway solutions for a startup — which ones have the simplest self-hosted setup with a working UI on day one? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited |
Which evaluation platforms for LLM outputs are easiest to plug into an existing CI pipeline for a five-engineer team? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Braintrust | 32.0% | 21.8% | 7.2% | 0.0% | 44.0% | #3.3 | +0.51 |
| 2 | LangChain | 26.4% | 18.8% | 8.8% | 0.0% | 48.0% | #3.3 | +0.42 |
| 3 | Langfuse | 21.6% | 14.3% | 8.0% | 4.0% | 55.2% | #3.1 | +0.59 |
| 4 | Confident AI | 18.4% | 14.7% | 0.0% | 0.0% | 15.2% | #4.9 | +0.43 |
| 5 | Arize AI | 16.0% | 10.9% | 0.0% | 4.8% | 15.2% | #4.4 | +0.51 |
| 6 | Galileo | 12.0% | 6.8% | 0.0% | 12.0% | 10.4% | #2.8 | +0.40 |
| 7 | LiteLLM | 5.6% | 4.1% | 3.2% | 0.0% | 0.0% | #3.4 | +0.52 |
| 8 | Traceloop | 4.0% | 3.8% | 0.0% | 2.4% | 6.4% | #3.6 | +0.25 |
| 9 | Portkey | 4.0% | 3.4% | 0.8% | 0.0% | 21.6% | #5.3 | +0.45 |
| 10 | Helicone | 2.4% | 1.5% | 1.6% | 0.8% | 25.6% | #5.8 | +0.83 |
| 11 | Patronus AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.8% | — | — |
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