# SLA and Uptime at LLM Providers: What's Realistic?

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# SLA and Uptime at LLM Providers: What's Realistic?

 Published on api.llmnet.nl | Category: Reliability & Operations
 
 
 When building business-critical applications on large language models (LLMs), organizations increasingly rely on external API services. Many developers and IT architects silently assume that a Service Level Agreement (SLA) from an LLM provider offers the same guarantees as a traditional IaaS or SaaS service. In practice, this assumption often leads to disappointment. The physical and logical characteristics of generative AI infrastructure differ fundamentally from classic REST APIs.

 In this article, we analyze what an SLA from an LLM provider actually promises, which forms of outage or performance loss fall outside the official agreements, and why public status pages rarely show the full picture. We also look at how financial compensation schemes work, how to measure a model's actual usability yourself, and how a well-designed architecture minimizes your dependency on a single provider.

 
## What an LLM SLA Does and Doesn't Promise

 A Service Level Agreement is a legal and operational framework in which a service provider sets out commitments about performance and availability. For traditional cloud services, an SLA usually guarantees that the HTTP endpoint responds correctly with a successful status code within an agreed percentage of the time. When it comes to LLM providers, however, the legal safety net reaches less far than a business's operational needs.

 Most SLAs guarantee only network reachability of the API gateway endpoint. Once the gateway is reachable and returns a valid HTTP response, this counts as availability under the contractual definition. This means many forms of functional unusability are formally not recorded as downtime.

 
### System Degradation versus Total Outage

 In the domain of generative AI, the line between availability and unusability is fluid. An LLM endpoint can return an HTTP 200 status code while internal processing time doubles or increases tenfold. Extreme increases in response time (latency) and delays in generating the first token (Time To First Token) almost never fall under the contractual definition of an outage.

 For a real-time conversational interface or an automated workflow, a ten-second delay is effectively the same as a timeout. Yet the provider records this as a successful call. Only by measuring it yourself and setting strict limits can you clearly map out this distinction. See the guidelines for [timeouts and cancellation](https://api.llmnet.nl/en/timeouts-en-cancellation).

 
### Capacity Limits and Rate Limiting (HTTP 429 & 503)

 A common phenomenon with LLM services is capacity shortages on the inference clusters. This manifests as HTTP 429 (Too Many Requests) or HTTP 503 (Service Unavailable) errors. Although the application cannot process requests at that moment, most providers exclude errors caused by capacity constraints from the SLA calculation. The provider argues that the gateway is functioning, but that the specific capacity is temporarily saturated.

 
### Preview, Beta, and Experimental Models

 Providers release new model versions at a rapid pace, often labeled preview, beta, or experimental. In nearly all SLA documents, these specific endpoints are explicitly excluded from any form of availability guarantee. If a company ties a critical process to a preview model, the customer bears the full risk of unexpected outages or abrupt changes.

 
### Deprecation and Changes to Model Versions

 A unique challenge with LLMs is the lifecycle of model versions. When a provider retires or replaces a model, the output characteristics often change as well. Formally, discontinuing a model version falls outside the SLA, provided the provider observes the notice period. More information about the impact of this can be found in the overview of [model versions and deprecation](https://hub.llmnet.nl/en/modelversies-en-deprecatie).

 
## Endpoint Uptime versus Model Usability

 Separating network availability from functional usability is essential for assessing LLM performance. An endpoint can be perfectly reachable while the model is unusable in terms of content or technical output.

 The table below gives an overview of situations where API status and actual usability diverge:

 
 
 
 Scenario | 
 API Status (SLA Perspective) | 
 Application Behavior | 
 Counts as Downtime in SLA? | 
 

 
 
 
 HTTP 500 Server Error | 
 Unavailable | 
 Total outage | 
 Yes | 
 

 
 Extreme Latency (TTFT > 30s) | 
 Available (200 OK) | 
 Unusable (Timeout) | 
 No (excluded) | 
 

 
 HTTP 429 Rate Limit ex-quota | 
 Available | 
 Request denied | 
 No | 
 

 
 Model returns invalid JSON | 
 Available (200 OK) | 
 Functional error | 
 No | 
 

 
 

 
## What Status Pages Show and Why You Need to Measure It Yourself

 Nearly every major AI provider maintains a public status page. While these pages are useful for a general overview during large-scale network outages, they're unsuitable as a tool for precise quality monitoring.

 Status pages are typically fed by aggregated measurement points or manual input from the provider's incident team. Small, regional, or account-specific outages rarely show up on them. [Assumption: status pages mainly show outages only once a significant percentage of global traffic fails for an extended period.] Moreover, these pages often measure only simple health-check requests, which are not representative of complex prompts.

 To get a realistic picture of performance, your own monitoring is essential. By setting up synthetic probes and measuring latency and error rates in your own production environment, you build an objective record. Practical guidelines for setting up these measurements can be found via the page on [measuring speed](https://benchmark.llmnet.nl/en/snelheid-meten).

 
## How Service Credits Work and Their Limitations

 When a provider fails to achieve the guaranteed uptime, the SLA's compensation scheme kicks in. In nearly all cases, this compensation consists of Service Credits — discounts on future invoices.

 It's important to understand that Service Credits offer very limited coverage. The size of the credit is tied to the monthly spend with that provider for that specific endpoint. If a company spends a modest monthly amount and the endpoint goes down for hours, the credit often amounts to no more than a small percentage of that specific monthly bill.

 The actual business damage — such as lost revenue, reputational damage, or halted business processes — is explicitly excluded by the provider in the terms and conditions. An SLA is therefore a tool for risk spreading at the invoice level, not insurance against business damage. For a legal deep dive into these agreements, we refer to the analysis of [AI contracts and SLAs](https://consultancy.llmnet.nl/en/ai-contracten-en-sla).

 
## Designing Your Own SLA and Error Budget

 Since the provider's SLA is inadequate for guaranteeing overall application quality, you as an engineer must set your own standard. This starts with defining an error budget and precisely specifying what your system considers a valid response.

 Determine which response times are acceptable for your application. An effective definition of an outage covers not only hard network errors (HTTP 5xx), but also latency overruns, capacity denials, and content invalidity. Don't calculate availability solely on a monthly basis; use shorter, rolling measurement windows of, for example, 24 hours. See also the guide on [observability and logging](https://api.llmnet.nl/en/observability-en-logging).

 
## Architecture Patterns to Reduce SLA Dependency

 The most effective way to deal with the uncertainties of provider SLAs is to design a resilient software architecture. Never rely on the assumption that a single provider is 100% available.

 Brief network interruptions or temporary load spikes can often be absorbed with a well-designed retry mechanism using exponential backoff. For critical processes, a multi-provider strategy is also recommended. As soon as the primary provider returns errors or exceeds the agreed latency threshold, the application automatically switches to a secondary provider.

 
## Checklist: Questions for Your LLM Provider

 When negotiating business terms or choosing a suitable plan with an LLM provider, ask the following questions to assess the real value of the contract:

 
 
- What is the precise contractual definition of downtime in the SLA, and are latency increases included in it?
 
- Are HTTP 429 (Rate Limit) and HTTP 503 (Capacity Exceeded) errors excluded from the uptime calculation?
 
- Does the guaranteed uptime apply to all offered models, or only to specifically designated Stable/GA models?
 
- What is the minimum notice period for deprecating or changing a specific model version?
 
- What is the maximum payout of Service Credits for prolonged outages, and how must a claim be substantiated?
 
- Does the provider offer guaranteed throughput with specific latency commitments, and what are the additional costs for this?
 
 
 By Ivo Donker - compiled with AI assistance (Claude & Gemini) - Last updated: August 2, 2026