Latitude
UnclaimedThe complete LLM control plane for scaling AI products with reliability and confidence.
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TL;DR - Latitude
- Provides a control plane for building reliable and scalable LLM applications.
- Offers observability, evaluation, and experimentation tools to improve AI accuracy and reduce errors.
- Enables faster prompt iteration and cost optimization for production AI systems.
Pricing: Free plan available
Best for: Growing teams
Pros & Cons
Pros
- Significantly reduces critical errors in production AI systems.
- Accelerates prompt iteration cycles.
- Improves AI model accuracy efficiently.
- Offers a free open-source self-hosting option for core features.
- Provides comprehensive observability into live AI traffic and behavior.
Cons
- The pricing tiers for hosted solutions can become costly for high-volume usage.
- Advanced features like model distillation and unlimited optimizations are only available in higher-tier plans.
- Requires integration into existing AI infrastructure, which may have an initial setup overhead.
Preview
Key Features
Observability with full traces of AI behaviorUsage statistics for token usage and expense regulationHuman feedback integrationFailure discovery mechanismsPrompt playground for iterationEvaluations (Evals)A/B testing for AI modelsVersioning for prompts and models
Pricing Plans
Free TrialTeam
$299/month
- 5 prompt optimizations per month
- Prompt playground
- Evaluations
- Version control
- Observability
- Annotations
- Experiments + simulator
- Issue discovery
- Automatic eval alignment
- Dataset generator
- Team support
- 90-day log retention
- 200K traces/month
- Extra traces $10/100k
- 5K requests/minute
- Unlimited seats
- Support via dedicated Slack channel
Scale
$899/month
- All features from Team
- Unlimited prompt optimizations
- Model distillation
- Cost / latency reduced 2x-10x
- Permissions
- Dedicated onboarding + setup
- Priority support
- Unlimited log retention
- 1 million traces/month
- Extra traces $10/100k
- 10K requests/minute
- Unlimited seats
- Custom onboarding
- SOC2 & ISO27001 reports
Enterprise
Custom
- All features from Scale
- Deploy in your infrastructure
- Volume discounts
- Account manager
Open Source
Free
- Host Latitude yourself with the primary features
- Full control
What is Latitude?
Latitude provides an AI reliability platform designed to help teams build and scale production-ready LLM applications. It offers a comprehensive control plane that addresses common challenges in AI development, such as behavior drift, unexpected product breaks from prompt changes, and difficulty in identifying failure points.
The platform is built for AI engineering teams and developers who need to ensure the stability, performance, and cost-efficiency of their LLM-powered products. It enables faster prompt iteration, reduces critical errors in production, and improves AI model accuracy by providing tools for observability, human feedback, failure discovery, evaluation, and experimentation. Latitude aims to transform production failures into clear signals for fixes, allowing teams to ship AI products with greater confidence.
Key benefits include significantly fewer critical errors reaching production, faster prompt iteration using techniques like GEPA, and a measurable increase in AI accuracy within weeks. It helps teams move from hoping their AI system works to having a reliable, observable, and continuously improving AI product.
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Latitude FAQ
How does Latitude help reduce AI behavior drift and unexpected product breaks from prompt changes?
Latitude addresses AI behavior drift and unexpected breaks through its comprehensive observability, failure discovery, and evaluation features. It captures real inputs, outputs, and context from live traffic, providing full traces of your AI's behavior. This allows teams to identify exactly where and why small prompt changes might be causing issues, turning production failures into actionable signals for fixes, rather than relying on guesswork.
What is GEPA and how does Latitude leverage it for faster prompt iteration?
GEPA (Agrawal et al., 2025) is a technique that Latitude uses to enable 8x faster prompt iteration. While the specifics of GEPA are not fully detailed, Latitude integrates this method to streamline the process of optimizing prompts, allowing developers to quickly test and refine their LLM inputs for improved performance and reliability without extensive manual effort.
Can Latitude be deployed within a private infrastructure for high-volume or sensitive AI products?
Yes, Latitude offers an Enterprise plan that supports deployment within your own infrastructure. This option is designed for high-volume products or those with specific security and compliance requirements, providing full control over data and environment while still leveraging Latitude's reliability features.
What is the difference in log retention and trace limits between the Team and Scale plans?
The Team plan includes 90-day log retention and allows for 200,000 traces per month, with additional traces available at $10 per 100,000. The Scale plan, on the other hand, offers unlimited log retention and supports 1 million traces per month, also with extra traces at $10 per 100,000. This distinction caters to varying operational scales and data retention needs.
How does Latitude's 'Issue discovery' feature function to pinpoint problems in LLM applications?
Latitude's 'Issue discovery' feature works by analyzing the captured inputs, outputs, and context from live traffic, combined with human feedback and evaluation results. It automatically identifies patterns and anomalies that indicate failures or suboptimal performance in your LLM system, helping teams quickly pinpoint the root cause of problems and prioritize fixes.
Is it possible to self-host Latitude for free, and what features are included in the open-source version?
Yes, Latitude offers an open-source option that allows users to self-host the primary features of the platform free of charge. This provides full control over the deployment environment and includes core functionalities necessary for managing and observing LLM applications, making it suitable for teams who prefer an on-premise solution or want to explore the platform's capabilities without an immediate financial commitment.
Source: latitude.so