Large Language Models (LLMs) offer powerful capabilities, but they also introduce high infrastructure costs, unpredictable usage patterns, and potential for misuse. As enterprises integrate LLMs into customer-facing tools, internal copilots, and API platforms, the need for controlled and reliable access becomes critical. This is where rate limiting plays a key role.
In the context of LLM inference, traditional request-per-second (RPS) rate limiting is not enough. LLMs are resource-intensive, token-based, and highly variable in computational load. A single prompt to a 70B parameter model can consume thousands of tokens and significantly impact GPU latency. Without proper controls, shared infrastructure can quickly become unstable or cost-prohibitive.
This article explains how rate limiting works in an AI Gateway, why it is essential for scalable AI infrastructure, and how TrueFoundry enables it by default to ensure fair usage, cost efficiency, and production-grade performance across multi-tenant deployments.
What is Rate Limiting and Why It’s Essential for AI Gateway
Rate limiting is a mechanism used to control how many requests a client can send to a system within a specific time window. It ensures fairness, prevents overload, and maintains availability, especially in multi-user environments. Traditional APIs often apply simple limits like 100 requests per minute per user, which works well for standard REST services.
However, LLMs operate very differently. Each request can place a dramatically different load on the infrastructure based on the input size, model type, and expected output. For instance, a 20-token prompt to a 7B model might complete quickly, while a 2000-token request to a 65B model could block GPUs for several seconds. Even two identical requests to different models can vary by 5x or more in compute cost.
This makes request-based limits insufficient. Modern LLM gateways must adopt token-aware rate limiting, which accounts for the actual number of tokens processed and the compute burden per call.
Key factors considered in token-aware rate limiting include:
- Total tokens processed (input + output)
- Model size and architecture
- Request type (chat, embedding, or RAG)
Compared to fixed request limits, token-aware limits:
- Provide fairer enforcement across varied workloads
- Enable accurate cost tracking and billing
- Protect systems from noisy or abusive users
In generative AI workflows, rate limiting becomes even more critical. A single user can trigger large-scale backend processing through long-form prompts, document ingestion, or multi-step agents. Without controls, this can lead to GPU congestion, high latency, or unexpected costs.
Real-world usage is often unpredictable, driven by front-end apps, test loops, or automation. Rate limiting ensures these interactions remain stable and efficient, even when infrastructure is shared across users or tenants.
For any production-grade LLM deployment, intelligent rate limiting is not an optional feature, it is a foundational requirement for scalability, reliability, and cost control.
Why Platforms Implement Rate Limits
Rate limiting is more than a backend safeguard. For platforms serving LLMs — especially those offering public or multi-tenant APIs — it acts as a strategic layer for stability, governance, and business alignment. Whether it's OpenAI, Anthropic, or platforms built with TrueFoundry, rate limits serve several critical purposes.
Protect Infrastructure from Abuse
Generative AI inference is resource-intensive. A sudden burst of long prompts or concurrent requests can overwhelm GPU queues, increase latency, or even bring services down. Rate limits ensure traffic is processed in a controlled and prioritized manner, preventing resource exhaustion.
Enforce Fairness Across Users or Tenants
In multi-user systems, one client’s usage should not degrade performance for others. Rate limits help enforce isolation across users, teams, or API keys. This guarantees consistent service levels regardless of how many users are active at once.
Align Usage with Pricing Plans
Many Gen AI platforms monetize based on tokens or usage tiers. Rate limits help enforce those boundaries. For example:
- Free users might be capped at 10 requests per minute
- Paid tiers can access larger models or higher throughput
- Enterprise customers may receive custom quotas or burst capacity
Prevent Cost Surprises and Overruns
LLM usage can scale quietly and quickly. Without proper limits, token consumption and GPU utilization can balloon. Rate limiting helps platforms prevent unexpected infrastructure costs and maintain budget control.
Improve Reliability and User Experience
When usage is controlled, system queues remain stable. This leads to lower latency, higher success rates, and a more consistent user experience, especially important in production environments with SLAs.
Rateliming Options in AI Gateway
In LLM-based systems, not all requests have the same impact. A short prompt to a small model may use minimal resources, while a long query to a large model can consume significant GPU time. Because of this variability, modern platforms apply rate limits across multiple dimensions instead of relying only on request count.
Here are the most common dimensions used for effective rate limiting:
- By API Key or User: Limits can be applied to individual users or API tokens. This helps prevent abuse, enables user-level monitoring, and supports fair usage across different clients or applications.
- By Organization or Team: In multi-tenant environments, each team or customer can have its own quota. This prevents one tenant from consuming shared resources excessively and allows differentiated service levels for each organization.
- By Model Type: LLMs vary in size and computational cost. A 7B model is less demanding than a 65B model. Applying separate limits per model type helps manage cost and performance across different workloads.
- By Request Type: LLM gateways often serve multiple request types, such as chat completions, embeddings, and retrieval-based queries. Each type has a different resource profile. Rate limiting by request type helps control high-load operations without blocking lightweight traffic.
- By Token Count: Token-based limits offer more precision than request-based limits. By measuring input and output tokens, platforms can apply quotas based on actual resource usage. This helps ensure fair consumption and accurate cost management.
- By Region or Deployment Cluster: In globally distributed systems, rate limits may vary by location. This allows for better load balancing and avoids regional saturation by tailoring policies to the capacity of each cluster or zone.
Using these dimensions gives platform teams the flexibility to align resource usage with infrastructure constraints, user needs, and service-level expectations.
How TrueFoundry Implements Rate Limiting
TrueFoundry provides a robust and flexible rate-limiting system that allows platform teams to control access to LLM endpoints based on requests or token usage. This ensures fair allocation of compute, prevents abuse, and aligns usage with organizational policies or billing plans.
At the core of TrueFoundry’s rate-limiting mechanism is a rule-based configuration system that allows teams to define precise policies across users, teams, virtual accounts, models, and request metadata.
Rule-Based Configuration
Rate limiting in TrueFoundry is defined through a list of rules, each specifying:
- Subjects: Target identities such as users, teams, or virtual accounts
- Models: Specific model IDs the rule applies to
- Metadata: Optional filters like environment or custom tags
- Limit: The number of requests or tokens allowed
- Unit: The time window for enforcement (per minute, hour, or day)
Rules are evaluated in order, so more specific rules should be placed above broader ones to ensure correct matching.
Supported Limit Types
TrueFoundry supports both request-based and token-based limits across various time intervals:
- requests_per_minute, requests_per_hour, requests_per_day
- tokens_per_minute, tokens_per_hour, tokens_per_day
This allows enforcement of policies that reflect actual compute usage, especially important when serving variable-length prompts to models of different sizes.
Common Use Cases
The flexibility of the configuration system supports a wide range of use cases:
- Limit a specific user to 1,000 requests per day
- Cap token usage for a team across all models
- Restrict GPT-4 access while allowing unrestricted use of smaller models
- Define environment-specific limits (e.g., lower limits in staging vs. production)
Sample Configuration
name: ratelimiting-config
type: gateway-rate-limiting-config
rules:
- id: "specific-rule"
when:
subjects: ["user:bob@email.com"]
models: ["openai-main/gpt4"]
limit_to: 1000
unit: requests_per_day
This example limits a specific user’s GPT-4 usage to 1,000 requests per day. TrueFoundry’s rate-limiting system is designed to be both powerful and easy to manage. With token-aware controls, granular targeting, and clear YAML-based policies, teams can confidently scale LLM usage while maintaining control over infrastructure and cost.
How to Apply the Configuration
1. Install the TrueFoundry CLI:
pip install -U "truefoundry"
tfy login --host https://app.truefoundry.com
2. Place your config.yaml in your project directory.
3. Apply the configuration using:
tfy apply -f config.yaml
This declarative approach ensures rate limits are version-controlled, reproducible, and aligned with GitOps best practices.
Real-Time Feedback on Rate Limits
TrueFoundry’s rate-limiting system is designed to provide immediate, transparent feedback to clients when limits are breached or nearing exhaustion. This helps developers understand usage boundaries and handle throttling gracefully in their applications.
When a request exceeds the defined rate limit:
- The server responds with HTTP 429 (Too Many Requests), clearly indicating that the rate limit has been breached.
- Along with the response, headers are included to help the client determine how to proceed.
This feedback mechanism supports better client behavior, enables automated retry logic, and ensures that usage remains within quota boundaries without guesswork.
Dashboards & Alerts
TrueFoundry provides built-in observability to help platform teams monitor and optimize rate limit policies in real-time.
Using the LLM Gateway dashboard, you can track:
- Requests blocked due to rate limit breaches
- Top users, teams, or models by usage.
These insights help detect abuse, adjust limits proactively, and ensure high-value users receive consistent service.
How to Set Up Fallback on TrueFoundry
When LLM APIs fail, whether due to rate limits, internal errors, or temporary outages, fallback mechanisms keep your applications running smoothly. Instead of returning errors to the end user, TrueFoundry can automatically route the request to a backup model or provider, maintaining availability with minimal disruption.
Fallback rules are triggered based on specific conditions such as the model ID, the requesting user or team, and response codes like 429 or 500. When a request meets these conditions, it is routed to one or more alternative models specified in the fallback configuration. These fallback targets can optionally include overrides to parameters like temperature or max tokens, allowing the behavior to be fine-tuned depending on the model provider. Only the first matching rule is applied during evaluation, which ensures predictable, deterministic handling of failures.
A typical fallback rule in TrueFoundry includes the following components:
- Conditions (when): Define which requests the rule applies to, based on model IDs, request metadata like customer ID, or the source of the request, such as a user or team.
- Triggering status codes: Specify which response codes like 500, 503, or 429 should activate the fallback. These are typically recoverable errors.
- Fallback targets (fallback_models): List of backup models or endpoints to route traffic to, in the order they should be tried.
- Optional parameter overrides: Adjust request parameters such as temperature or max_tokens when forwarding to the fallback model, allowing customization for different model behavior.
Sample Fallback Config:
name: model-fallback-config
type: gateway-fallback-config
# The rules are evaluated in order and once a request matches one rule,
# the subsequent rules are not checked
rules:
# Fallback to gpt-4 of azure, aws if openai-main/gpt-4 fails with 500 or 503. The openai-main target also overrides a few request parameters like temperature and max_tokens
- id: "openai-gpt4-fallback"
when:
models: ["openai-main/gpt4"]
response_status_codes: [500, 503]
fallback_models:
- target: openai-main/gpt-4
override_params:
temperature: 0.9
max_tokens: 800
# Fallback to llama3 of azure, aws if bedrock/llama3 fails with 500 or 429 for customer1.
- id: "llama-bedrock-customer1-fallback"
when:
models: ["bedrock/llama3"]
metadata:
customer-id: customer1
response_status_codes: [500, 429]
fallback_models:
- target: aws/llama3
- target: azure/llama3
TrueFoundry’s LLM Gateway natively supports declarative fallback setup as part of its configuration system. This allows teams to define fault-tolerant routing policies and preserve uptime without manual intervention, especially when working with multiple providers. Together, smart rate limiting and automated fallback create the foundation for high-availability Gen AI services.
How to Manage Rate Limits in Multi-Tenant AI with TrueFoundry
In any multi-tenant AI platform, rate limiting is crucial for ensuring stability, fairness, and cost governance. It allows teams to define access boundaries not only for individual users but also across teams, virtual accounts, and specific models without the need for custom logic.
TrueFoundry’s Gateway supports declarative rate limiting via YAML configuration, where rules are evaluated in order. The first matching rule is applied, meaning more specific rules should be placed at the top, while more generic rules should be placed lower in the configuration. This structure ensures layered control while maintaining clean, readable configurations.
Each rule can include the following components:
- subjects: Specifies the entities subject to rate limits, such as individual users (e.g., user:bob@email.com), teams (e.g., team:backend), or virtual accounts (e.g., virtualaccount:va-james).
- Models: Filters rate limiting based on specific model IDs (e.g., openai-main/gpt4).
- metadata: An optional field for additional filtering, such as environment or customer ID.
limit_to and unit: Defines the numeric cap (tokens or requests) within a given time window.
Examples of Multi-Tenant Rate Limiting
Limit Specific User Request: Suppose you want to limit all requests to gpt4 model from the openai-main account for users bob@email.com and jack@email.com to 1000 requests per day:
- id: "user-gpt4-limit"
when:
subjects: ["user:bob@email.com", "user:jack@email.com"]
models: ["openai-main/gpt4"]
limit_to: 1000
unit: requests_per_day
Apply team-wide limits: If you want to restrict the total number of requests for the 'frontend' team to 5000 per day
- id: "team-frontend-limit"
when:
subjects: ["team:frontend"]
limit_to: 5000
unit: requests_per_day
Restrict virtual accounts: If you want to cap the number of requests for the virtual account 'va-james' at 1500 per day
- id: "va-james-limit"
when:
subjects: ["virtualaccount:va-james"]
limit_to: 1500
unit: requests_per_day
Set global caps across all users and models:
- id: "{user}-{model}-daily-limit"
when: {}
limit_to: 1000000
unit: tokens_per_day
This setup allows platform teams to segment usage across business units, enforce per-environment quotas, and protect expensive model endpoints while supporting scalable, reliable AI workloads.
Conclusion
Rate limiting is more than a backend control. It is a critical enabler for reliable, cost-efficient, and fair usage of LLM infrastructure at scale. Whether you are operating a multi-tenant platform, offering tiered access to customers, or running internal AI workloads across teams, implementing smart, token-aware rate limits ensures your system stays predictable under pressure.
Alongside rate limiting, features like fallback routing, real-time feedback, and granular configuration give engineering teams the tools to balance performance with control. TrueFoundry’s LLM Gateway brings these capabilities together with a declarative interface, allowing platform teams to define policies that are transparent, auditable, and aligned with organizational goals.
As generative AI adoption accelerates, systems that enforce intelligent access control without sacrificing user experience or uptime will define the next generation of infrastructure resilience. If you are building or scaling an AI gateway, rate limiting is not just something to consider. It is something to get right from day one.
