LLM routing has caught serious attention lately. In June 2025, OpenRouter raised $40 million and hit a $500 million valuation. That’s not just startup money; it shows investors think there’s real value in switching between AI models intelligently.
Meanwhile, Accenture decided to back and partner with Martian, another routing company. When big consulting firms start making moves, you know something’s happening in the market.
Open-source alternatives are gaining equal momentum. LiteLLM’s proxy tool has surpassed 470,000 downloads, reflecting widespread adoption among developers and the appeal of self-hosted routing solutions.
The market makes it clear: there’s a lot of interest in LLM routing. The ability to switch models for every user prompt based on its complexity or type of task, as well as the cost of generating a good answer, helps enterprise teams obtain better answers from their genAI copilots, reduce infrastructure costs, and improve service reliability through multi-provider redundancy.
However, choosing the right routing solution requires understanding the trade-offs between different deployment models, feature sets, and operational requirements. The decision impacts not only current performance but also future scalability, compliance capabilities, and total cost of ownership.
We’ll compare OpenRouter and LiteLLM across critical enterprise dimensions: deployment architecture, model ecosystem support, routing intelligence, security features, and operational performance.
The comparison framework also works for evaluating other routing tools, as more options continue to appear.
But let’s briefly go over the basics first.
What is LLM routing?
When OpenAI released GPT-5, a lot of users were unhappy with the company’s choice to deploy “a real-time router that quickly decides which model to use based on conversation type, complexity, tool needs, and your explicit intent”.
Though it took a slice of end-users’ freedom away, from OpenAI’s perspective, routing was actually a reasonable implementation that helped make sure users don’t spend too many resources on queries that could be answered just as well by a less “intelligent” model.
It’s already well known that models are not equal in their capabilities. State-of-the-art reasoning models are capable of solving nearly any task with fairly high accuracy, but they lose to smaller purpose-fit algorithms designed for a specific task.
For example, although GPT is fairly competent in answering medical questions, proprietary models like OpenEvidence’s algorithm trained on credible medical data are much more accurate.
On the other hand, OpenEvidence would not have GPT’s coding or math skills because it was not trained for that purpose.
Model routing helps enterprise teams choose the most cost-effective and most accurate LLM for each prompt instead of locking themselves into a single provider.
This is a win-win for engineering teams in terms of striking a balance between infrastructure spend and performance.
How LLM routers make routing decisions
LLM routers analyze each incoming prompt and categorize it for assignment to different model tiers
High-performance models like Claude Sonnet, Gemini Pro, or GPT-5 handle complex reasoning tasks. These models consistently score higher across standard benchmarks but cost more per token.
Efficient models like Mistral’s mixture-of-experts variants or open-source models like DeepSeke and KImi K2 excel at simpler queries while maintaining lower operational costs.
The routing decision relies on both deterministic rules and probabilistic algorithms. Routers evaluate factors like prompt complexity, expected response quality (using metrics like BART score), and cost per token to determine the optimal model assignment.
Most routing platforms allow engineering teams to customize these decisions based on their specific constraints, whether that’s budget limits, latency requirements, or quality thresholds
Why do enterprises use LLM routers?
The benefits of LLM routers in the enterprise boil down to three fundamental benefits: cost reduction, performance improvement, and reliability by design. Understanding these benefits helps explain why organizations are investing in routing infrastructure despite the added complexity.
Routing is a way to cut inference costs
Training complex LLMs like GPT or Claude is more expensive than building smaller models because running them requires a lot of computing power and specialized hardware. Besides, large language models use auto-regressive generation, producing text sequentially where each token depends on all previously generated tokens in the sequence.
This token-by-token generation means that complex queries requiring detailed responses can accumulate substantial costs, particularly when routed exclusively to premium models.
Smaller models like Mistral have lower inference costs, but they are also less powerful than Claude, Gemini, or GPT. The challenge lies in determining which queries require advanced reasoning capabilities and which can be handled effectively by more economical alternatives.
That’s how, according to Lmsys.org benchmarks, dynamically routing models reduces costs by over 85% while reaching GPT-like performance on MT bench, an evaluation of an AI application’s ability to create engaging interactions.
At the same time, smaller models still reach 95% of the performance that GPT-4 is capable of.
Routing systems analyze prompt characteristics and route requests appropriately based on complexity requirements. According to Lmsys.org benchmarks, this approach can reduce inference costs by over 85% on certain evaluation sets while maintaining 95% of the output quality compared to using premium models exclusively.
Routing gives you the ability to balance price and performance”. Save the big models for high-value, complicated tasks and use the smaller, cheaper models for easy tasks that don’t require hundreds of billions of parameters.
Kate Soule, generative AI program director at IBM Research
Routing improves performance by helping engineers discover powerful domain-specific models
There are currently over 700,000 large-language models of different sizes and purposes on Hugging Face alone, each optimized for different capabilities and domains. This diversity creates opportunities for performance optimization beyond simple cost considerations.
Rather than relying on general-purpose models for all applications, routing enables organizations to leverage domain-specific models that excel in particular areas.
Medical applications can route clinical queries to models trained on medical literature, while development workflows can direct coding questions to models optimized for software engineering tasks.
Research from the Shanghai Artificial Intelligence Laboratory demonstrated this approach with their Avengers Pro router, which achieved 66.6% accuracy across multiple benchmarks by routing to task-optimized models, compared to 62.25% when using a single high-performance model for all queries.
Routing ensures 24/7 uptime and enterprise-grade reliability
No large language model is immune to server outages and downtime.
In September 2025, Anthropic had to address four notable Claude response issues.
GPT also had to grapple with the “ChatGPT not displaying responses” error in early September and more outages in the previous month.
For organizations running mission-critical AI applications, these outages translate directly into business disruption and potential revenue impact. Single-provider dependencies create unnecessary vulnerability in enterprise architecture.
Bringing multiple models into a single interface and re-routing to a different LLM when the first-choice model is unavailable helps ensure 24/7 uptime and avoid vendor lock-in.
Peer-reviewed studies document both latency reduction and uptime lifts when teams switch from using a single LLM to intelligent routing.
A recent study on service-level objective attainment for LLM routing showed a 5-time improvement in SLO attainment and 31%.6 latency reduction after the team implemented request routing. Serverless routing helped cut latency by up to 200 times and drastically reduced timeouts.
Popular LLM routers: OpenRouter and LiteLLM
There are several known commercially-licensed LLM routers currently on the market, and dozens of open-source projects published on GitHub. Comparing all of them in a single article would not be feasible and requires a deeper grasp of project-specific considerations.
That’s why we are honing in on two popular routers, each representing a different category of tool: OpenRouter, which operates as a managed SaaS platform, and LiteLLM, which provides open-source self-hosted capabilities.
OpenRouter
OpenRouter is a unified API gateway that gives engineering teams access to over 500 models. The platform abstracts individual provider APIs into a single interface, enabling teams to switch between models without changing integration code.
The service includes both programmatic API access and a web-based chat interface for model testing and comparison. This dual approach allows developers to integrate routing into applications while providing non-technical stakeholders with direct model evaluation capabilities.
LiteLLM
LiteLLM is an open-source router that provides a unified interface to over 100 LLM APIs. At the time of writing, LiteLLM’s GitHub repository has over 28,800 GitHub stars and over 4,000 forks.
Netflix, Lemonade, and RocketMoney are among LiteLLM’s enterprise customers, indicating production-scale viability.
LiteLLM’s architecture allows teams to maintain complete control over data flows and infrastructure while accessing the routing capabilities typically available only through managed services.
Deployment model: SaaS platform vs self-hosted router
OpenRouter and LiteLLM represent fundamentally different approaches to deployment, each with distinct advantages depending on your team’s technical capabilities and organizational requirements.
OpenRouter operates as a managed SaaS platform. Teams sign up for an account, obtain API credentials, and immediately start routing requests through OpenRouter’s infrastructure.
The platform handles all backend operations, including server management, model provider integrations, scaling, and maintenance.
availability. However, this convenience comes with less control over data flows and dependence on OpenRouter’s operational decisions.
LiteLLM allows for self-hosted deployment on your own infrastructure. Teams can install it on cloud instances, on-premise servers, or local development machines for testing and experimentation.
Self-hosting provides complete control over the routing infrastructure, data handling, and configuration. Your IT department manages resource allocation, security policies, and system updates.
This approach often aligns better with enterprise compliance requirements and data governance policies.
However, self-hosting also means your team handles operational responsibilities:
- Infrastructure scaling and maintenance
- Model provider API integration and updates
- Security patching and system monitoring
- Performance optimization and troubleshooting
That’s why teams can also use the LiteLLM Python SDK to remove the need for self-hosting.
Decision factors
The choice between managed and self-hosted deployment typically depends on several organizational factors:
Technical expertise – Teams with strong DevOps capabilities often prefer the control and customization possible with self-hosted solutions. Organizations without dedicated infrastructure teams may find managed platforms more practical.
Compliance requirements – Industries with strict data handling regulations (healthcare, finance, government) often require self-hosted solutions to maintain data sovereignty and meet audit requirements.
Cost structure preferences – Managed platforms offer predictable operational costs but less control over infrastructure spending. Self-hosted solutions require upfront infrastructure investment but provide more granular cost management.
Integration complexity – Organizations with complex existing infrastructure may find self-hosted solutions easier to integrate with internal systems and security policies.
Both deployment models can scale to enterprise requirements, but the operational trade-offs differ significantly in terms of control, responsibility, and resource requirements.
LLM support
OpenRouter and LiteLLM treat LLM support differently – the former uses a single Model API that connects to over 400 models, while the latter lets machine learning engineers access over 100 LLMs via a unified interface.
These architectural differences affect how teams discover, evaluate, and integrate new models into their applications.
OpenRouter: Models API
After the development team confirms the specs of a new model, it’s added to the Model API registry.
Engineers can compare the parameters of over 400 registered LLMs at the time of writing, such as:
- Input/output capabilities: Text, images, video, and other modalities
- Context windows: Maximum token limits for each model
- Pricing structure: Per-token costs for inputs and outputs
- Special features: Web search integration, reasoning capabilities, and function calling support
- Performance metrics: Response times and availability statistics
This is not a comprehensive list of supported parameters; refer to the OpenRouter documentation for a more detailed description.
LiteLLM: Proxy Server and Python SDK
LiteLLM supports engineers with a unified interface that enables them to access over 100 LLMs. There are two ways to call a model:
LiteLLM proxy server functions as a centralized service that your team deploys and manages. It provides model access along with usage tracking, cost monitoring, and configurable guardrails.
This approach works well for teams that want centralized control over model access and usage policies.
LiteLLM Python SDK enables direct integration into application code, allowing developers to call models programmatically without running a separate proxy service.
This method suits teams that prefer embedded routing logic and minimal infrastructure overhead.
For detailed instructions and a step-by-step guide on installing and managing the LLM Gateway and Python SDK, go to this page in LiteLLM’s official documentation.
Both platforms abstract away provider-specific API differences, but OpenRouter provides more comprehensive model metadata and comparison tools, while LiteLLM offers more flexibility in how teams integrate and deploy routing capabilities.
Platform costs
It makes sense to analyze the costs of LLM routers on two levels: the cost of using the software and the fee engineering teams pay for using models.
OpenRouter
OpenRouter operates on a fee-based model and charges a 5.5% fee when they purchase credits.
If teams use their own provider API keys, they are charged a 5% fee, deducted from OpenRouter credits.
No markup on provider pricing – you pay the same per-token rates you would pay directly to OpenAI, Anthropic, or other model providers.
This approach means your total cost equals the base model cost plus the platform fee. For teams using multiple providers, the convenience of unified billing and routing may justify the overhead, especially when factoring in the operational time saved.
LiteLLM
Like OpenRouter, LiteLLM does not add a per-token markup, so engineers pay the same fee they would pay to the model provider directly. The software itself is open-source and free to use.
There’s also an enterprise edition available through AWS Marketplace at approximately $30,000 annually, which includes custom SLAs, SSO integration, and dedicated support.
Since you host LiteLLM yourself, factor in server costs, monitoring, and maintenance overhead. A typical deployment might cost $200-500 monthly in cloud infrastructure, depending on traffic volume and redundancy requirements. However, the LiteLLM Proxy Server allows machine learning engineers to just use the SDK.
Total cost of ownership comparison
The true cost difference depends on usage patterns and organizational preferences:
For high-volume usage: LiteLLM’s elimination of platform fees can result in significant savings, especially for teams processing millions of tokens monthly. The 5-5.5% OpenRouter fee compounds with the usage scale.
For moderate usage: OpenRouter’s managed infrastructure may cost less than running and maintaining LiteLLM, particularly when factoring in engineering time for setup, monitoring, and maintenance.
For enterprise deployments: LiteLLM Enterprise’s $30,000 annual fee becomes cost-effective for organizations with substantial token usage or strict compliance requirements that make self-hosting necessary.
Example calculation: Example for a team processing 10 million tokens monthly at $0.01 per 1,000 tokens ($1,000 base cost):
- OpenRouter: $1,000 + $50-55 platform fee = $1,050-1,055
- LiteLLM: $1,000 + estimated infrastructure costs ($100-400 monthly depending on deployment) = $1,100-1,400
- LiteLLM Enterprise: $1,000 + $2,500 monthly license fee ($30,000 annually) = $3,500
Note: AI Infrastructure costs vary significantly based on deployment configuration, traffic patterns, and redundancy requirements.
Routing capabilities
OpenRouter has two routing approaches: provider-level and model-level routing.
Provider-level routing
OpenRouter helps engineers find the best provider for their chosen LLM by customizing routing rules. By default, all provider requests are load-balanced.
The system monitors provider health and routes traffic away from providers experiencing recent availability issues, with a 30-second monitoring window for outage detection. Among these, OpenRouter chooses the cheaper option and recommends it to the user.
If engineers have project-specific requests, they can customize provider-level routing. Here are some of the criteria teams can use to refine provider selection:
- Order: manually set up a specific order of providers: e.g., OpenAI as first-choice, Anthropic as second-choice, etc.
- Require_parameters: choose providers that support the data types of your request (e.g., booleans).
- Data_collection: allow or deny the use of model providers that store data.
To optimize infrastructure costs, engineers can disable fallback options and use the most cost-effective provider for all requests.
Model-level routing
Engineering teams can also optimize LLM selection by customizing model-level routing.
By default, OpenRouter’s selection is powered by Not Diamond: another industry-standard LLM router, backed by OpenAI, Databricks, Google, Hugging Face, and many other frontier machine learning and data engineering companies. It will choose the model that most successfully balances the cost and output quality for a user’s prompt.
To customize model selection, users can set up an order in which the router chooses the best-fit model using the models parameter.
Compared to LiteLLM’s more elaborate custom routing, the customization of OpenRouter’s routing logic, both on the model and provider levels, is much more limited.
LiteLLM’s custom routing
LiteLLM has a wider range of routing strategies, including the ability to build fully custom routing strategies.
By default, LiteLLM documentation recommends using the “weighted pick” approach. It sorts models by performance and latency overhead. Developers can set the weight parameter to choose which models get picked under specific circumstances.
Latency-based routing continuously measures response times and directs traffic to models with the lowest observed latency. This approach optimizes for speed but may sacrifice quality for performance-sensitive applications.
The rate limit-aware approach chooses models with the lowest tokens per minute usage. According to the official docs, LiteLLM uses Redis to track usage across all deployments.
The system monitors both requests per minute (RPM) and tokens per minute (TPM) against provider-specific rate limits, with some providers, like Azure OpenAI (RPM = TPM /6), having different rate-limiting approaches.
Least-busy routing chooses a deployment with the lowest number of ongoing calls.
Lowest-cost routing calculates the cost of deploying a model and chooses the most cost-efficient option.
Custom routing allows engineers to build a tailored routing strategy and set limits on parallel calls, retries, or cooldowns (removing unreliable deployments from the list of available options).
Enterprise features
Since OpenRouter is a managed platform, user data passes through the platform’s infrastructure. To give enterprise teams more control over privacy and governance, there’s a robust suite of enterprise features.
The cloud version of LiteLLM also has managed privacy controls. For self-hosted instances of the tool, engineers have full control over data security.
Data sharing and privacy
For every API call, OpenRouter stores request metadata: timestamps, deployed models, and token usage.
The prompt or the model’s response is not stored in the system unless teams opt in to do so in return for a 1% discount. Besides, OpenRouter monitors data retention settings for all supported model providers to help teams choose the most compliant option.
LiteLLM Cloud encrypts user data with the client’s key and transmits encrypted data using TLS. Security teams can also create a white list of IPs allowed to access Cloud LiteLLM.
For self-hosted deployments, teams maintain complete control over data flows since no information passes through external systems. This approach provides maximum security for organizations with strict data sovereignty requirements.
API key management
OpenRouter provides endpoints for creating, distributing, or rotating API keys.
The platform’s Provisioning API enables SaaS teams to create unique instances for each customer, rotate keys for security compliance, track usage, and detect anomalies, allowing them to disable keys that exceed set limits.
LiteLLM also allows creating virtual keys for the proxy, tracking spend for teams and individual users. Teams can monitor spend by individual users or teams, implement rate-limiting policies, and modify routing behavior on a per-key basis.
This approach allows upgrading specific requests to premium models based on user permissions or usage patterns.
Access controls
OpenRouter has two roles, Administrators and Members, with clearly defined access permissions.
Administrators can view all API keys created by the organization, edit, disable, and delete them. They also have full access to API usage data.
Members can create API keys and view or manage the keys they created (but not those created by other users). Members can use all keys within the organization, and the API usage from those keys will be billed to the shared organization credit pool.
LiteLLM also implements role-based access control, where each role has a set list of permissions.
Admins have access to all capabilities and manage other users across multiple organizations. They can view all keys, track spend, create or delete keys, and add new API users.
Organizations manage teams and users within their specific organization and maintain control over organizational keys and spending.
Internal users handle their own keys and monitor personal usage, but cannot add new users or access organizational controls.
Compliance and audit capabilities
OpenRouter provides detailed usage logs and maintains SOC 2 compliance for its infrastructure. The platform offers data processing agreements and can accommodate specific compliance requirements for regulated industries.
Self-hosted LiteLLM deployments enable organizations to implement custom compliance controls aligned with their specific regulatory requirements. Teams can configure detailed audit logging, implement custom authentication systems, and maintain complete data residency control.
LiteLLM Cloud provides standard compliance features, while self-hosted deployments offer unlimited customization for organizations with specialized compliance needs.
The choice between platforms often depends on whether teams prefer managed compliance (OpenRouter) or customizable compliance controls (LiteLLM self-hosted).
Performance
Latency overhead represents a critical consideration for production AI applications, where additional routing delays can impact user experience and system responsiveness.
OpenRouter
According to the platform’s documentation, the router adds about 40 ms of latency to a user’s LLM requests. To reduce latency, the development team uses Cloudflare Workers for edge computing and caches API and user data at the edge.
Keep in mind that OpenRouter’s caches are typically cold in the first few minutes after deployment in a new location. That’s why users initially experience higher latency that goes down as the cache warms up.
The platform’s caching algorithms become more aggressive when account credits approach low thresholds (under $10) or near API limits. Maintaining credit balances in the $10-20 range helps ensure optimal caching performance.
LiteLLM
LiteLLM’s self-hosted architecture creates different performance dynamics compared to managed routing services:
Performance benchmarks show median response times of 100-110ms, though this includes both routing overhead and network transit time.
More granular analysis reveals that 50%of requests add ~3 ms of routing overhead. Only 10% of requests add over 17 ms in overhead, and only 1% of requests exceed 31ms in overhead.
These metrics indicate that LiteLLM’s routing logic adds minimal processing time for most requests, with higher overhead only affecting the slowest percentile of responses.
Performance comparison and considerations
OpenRouter adds a consistent 40ms to requests, while LiteLLM typically adds only 3ms for most queries, though this can increase to 31ms for the slowest responses.
The key difference is that LiteLLM’s performance depends on your infrastructure setup. Teams that optimize their deployment properly can achieve better performance than OpenRouter’s managed service. However, OpenRouter provides more predictable results without requiring infrastructure expertise.
For latency-critical applications, LiteLLM’s lower overhead provides an advantage, assuming your team has the resources to deploy and optimize it correctly. For most enterprise use cases, OpenRouter’s consistency and managed approach may justify the additional latency.
Integrations
Both routers are well-integrated with standard machine learning tools and MCP servers.
Frameworks
OpenRouter integrates with all popular AI frameworks, like OpenAI SDK, LangChain, PydanticAI, VercelAI, and others. These integrations work out-of-the-box with minimal configuration, allowing developers to replace existing API endpoints with OpenRouter’s unified interface.
The native compatibility means existing applications can integrate OpenRouter routing with minimal code changes, making it attractive for teams migrating from single-provider setups.
LiteLLM does not have out-of-the-box integrations with AI frameworks, but users can use popular orchestrators by connecting the router to third-party solutions.
For instance, users can connect LiteLLM to MLflow to use LangChain ChatLiteLLM. The entire process is described step by step in LiteLLM’s documentation.
MCP (Model Context Protocol) support
OpenRouter is compatible with MCP servers. It converts Anthropic tool definitions to OpenAI-compatible definitions. You can follow the full implementation guide in OpenRouter’s documentation entry on MCP compatibility.
LiteLLM Proxy also has an MCP Gateway that engineers use to connect to servers and control MCP access within their teams.
The table below is a quick summary of the feature-by-feature comparison of OpenRouter and LiteLLM.
| Area | OpenRouter | LiteLLM |
|---|---|---|
| Deployment | Managed SaaS with zero infra. | Self-hosted (Proxy/SDK) or enterprise cloud. |
| LLM support | One API to 400–500+ models; public registry. | Unified interface to 100+ models across providers. |
| Routing | Model auto-router (NotDiamond) + provider filters (price, outages, data rules). | Multiple strategies (weighted, latency, rate-limit, least-busy, cost) + fully custom routing. |
| Privacy | Stores metadata only by default; opt-in prompt logging; provider data-retention controls. | - Cloud: encrypted + IP allow-list - Self-hosted: you control data/telemetry. |
| Keys and orgs | - Provisioning API - Key rotation - Org Admins/Members roles. | - Virtual keys, budgets, rate limits - RBAC for org/teams/users. |
| Performance | ~25–40 ms gateway overhead - Edge cache (brief warm-up in new regions). | ~100 ms median; gateway overhead ~3 ms P50 / 17 ms P90 / 31 ms P99. |
| Pricing | No token markup; platform fee on credits (~5–5.5%). | - Open-source free (self-host) - No token markup; enterprise edition available. |
| Frameworks | - OpenAI-compatible - Works with Vercel AI, LangChain, etc. | - Python SDK + Proxy - Hooks for Langfuse/MLflow - Works with builders (e.g., Flowise) |
| MCP | - Compatible - Converts Anthropic MCP tools to OpenAI format. | - MCP Gateway in Proxy - Key/team/org permissions. |
| Best fit | Minimal ops, managed routing and governance. | Full control, deep custom routing, strict internal policies. |
OpenRouter vs LiteLLM: Which one is right for you?
OpenRouter and LiteLLM are both well-embedded into the LLM ecosystem, have integrations with popular orchestrators and MCP servers, and offer a reliable enterprise service suite.
The biggest difference between the two is deployment: managed for OpenRouter and self-hosted for LiteLLM.
The decision typically comes down to whether your team prefers managed convenience or self-hosted control, each with distinct advantages for different organizational contexts.
Little experience with LLM-based use cases or limited IT talent: choose OpenRouter
If your organization does not yet have best practices for LLM adoption and has little experience managing proprietary models, it will move faster with OpenRouter.
Using a fully managed router reduces your DevOps overhead and delegates scaling or SLA management to the vendor. Predictable SaaS pricing makes it easier to budget for prototypes and pilot projects.
OpenRouter’s 400+ model catalog lets you test different options without managing multiple provider accounts.
Experience in maintaining self-hosted architectures and strict compliance requirements: go for LiteLLM
LiteLLM is an excellent fit for teams that need to build LLM applications with strict compliance in mind. A self-hosted router allows engineers to store sensitive data on-premises or on a private network.
Security teams can enforce even stricter compliance by setting up custom middleware and routing rules and creating strict observability guardrails.
As for cost, a self-hosted LLM router is a higher upfront cost investment, but high-volume applications can eliminate the 5-5.5% platform fees through self-hosting, though this requires accounting for infrastructure and operational costs.
Both OpenRouter and LiteLLM keep expanding their enterprise capabilities, model support, and ecosystem integrations. Over time, this comparison may no longer reflect the current state of both models.
The routing capabilities themselves unlock significant value regardless of platform choice – access to hundreds of high-performing models, cost optimization through intelligent selection, and improved reliability through multi-provider redundancy.
