Latitude
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About
Latitude serves as a comprehensive LLM control plane designed to bridge the gap between initial AI prototypes and reliable production systems. The platform helps engineering teams manage the inherent unpredictability of large language models by providing a centralized hub for monitoring, testing, and refinement. By transforming production failures into actionable signals, the tool enables developers to systematically reduce critical errors and improve the accuracy of their AI products over time. It provides a structured environment where production data is used to drive iterative improvements in model performance. The platform operates through a "reliability loop" that integrates several core features. It begins with observability, using OpenTelemetry-compatible instrumentation to capture full traces, inputs, outputs, and metadata from live traffic. Users can then apply human judgment via annotations to create high-quality feedback datasets. An automated error analysis engine groups failures into recurring patterns, which are subsequently used to generate continuous evaluations. These evaluations run automatically to catch regressions before they reach end-users, while a prompt manager and optimizer use the GEPA algorithm to automatically test and refine prompt variations against established benchmarks. Latitude is best suited for AI engineers, product managers, and developers working at organizations that are scaling their AI features. It is built to support complex environments, integrating seamlessly with major model providers like OpenAI, Anthropic, and Google Gemini, as well as orchestration frameworks like LangChain and the Vercel AI SDK. This makes it an effective solution for teams needing a unified source of truth for prompt versioning and performance metrics across multiple models and applications, regardless of whether they use hosted APIs or local models via Ollama. What distinguishes Latitude from standard logging tools is its focus on closing the loop between observing a problem and deploying a fix. Features such as model distillation help reduce latency and costs, while the GEPA-powered optimizer speeds up iteration cycles by up to eight times. Additionally, the availability of a self-hosted open-source version provides a critical advantage for security-conscious industries, allowing teams to maintain complete data sovereignty while still utilizing a professional suite of AI engineering tools.
Pros & Cons
Provides an open-source version for teams requiring full infrastructure control and data privacy.
Supports a wide array of providers including OpenAI, Anthropic, and local models via Ollama.
Utilizes the GEPA algorithm to achieve up to 8x faster prompt iteration compared to manual methods.
Includes automated error analysis that identifies and groups recurring failure patterns in production.
Offers a 30-day free trial for the Team plan to test features without upfront commitment.
The base paid tier starts at $299 per month, which may be prohibitive for smaller startups.
Log retention is restricted to 90 days on the entry-level Team plan.
Advanced features like model distillation and priority support are only available on higher-priced tiers.
Self-hosting requires more technical expertise to maintain compared to the managed cloud version.
Use Cases
AI Engineers can use the prompt playground and GEPA optimizer to reduce critical errors and improve accuracy during development.
Product Managers can leverage human annotations and feedback loops to align AI responses with business intent and user expectations.
DevOps teams can monitor live traffic with OTEL-compatible telemetry to track token expenses and identify latency issues across multiple models.
Platform
Task
Features
• prompt version control
• multi-provider support
• model distillation
• failure mode grouping
• automated evaluations
• human feedback annotations
• prompt optimization (gepa)
• otel-compatible telemetry
FAQs
What models and providers does Latitude support?
Latitude integrates with a wide range of providers including OpenAI, Anthropic, Azure, Google AI, and Mistral. It also supports local models through Ollama and frameworks like LangChain and the Vercel AI SDK.
How does the prompt optimizer work?
The platform uses the GEPA algorithm to automatically test different prompt variations against real evaluations. This process allows teams to iterate on prompts up to 8x faster and identifies the best-performing version to reduce errors.
What are traces in the context of Latitude?
Traces capture the full interaction between your application and the LLM, including real inputs, outputs, and context from live traffic. This provides deep visibility into what the system is actually doing versus expected behavior.
Is there a way to host Latitude on our own infrastructure?
Yes, Latitude provides an open-source version available on GitHub that allows for self-hosting. This gives teams full control over their data and infrastructure while utilizing the platform's primary features.
Pricing Plans
Team
USD299.00 / per month• 30-day free trial
• 90-day log retention
• 200k traces per month
• 5 prompt optimizations
• Prompt playground
• Evaluations
• Version control
• Issue discovery
• Unlimited seats
• Slack support
Scale
USD899.00 / per month• Unlimited log retention
• 1 million traces per month
• Unlimited prompt optimizations
• Model distillation
• Reduced latency and cost
• Permissions
• Dedicated onboarding
• Priority support
• SOC2 and ISO27001 reports
Enterprise
Unknown Price• Deploy in your infrastructure
• Volume discounts
• Account manager
• Custom limits
• All Scale features
Open Source
Free Plan• Self-host on your infra
• Primary features
• Full data control
• Open source code
• Community support
Job Opportunities
There are currently no job postings for this AI tool.
Ratings & Reviews
No ratings available yet. Be the first to rate this tool!
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