Latitude

Latitude is built for teams running AI agents in production and needing a disciplined way to investigate failures. It suits engineering and AI product groups that want observability tied directly to regression testing and remediation, rather than a standalone prompt editor. The trade-off is that meaningful production scale requires watching credit and retention allowances or moving to a custom deployment.

Overview

Latitude connects live agent traffic to diagnosis, monitoring, and repeatable testing. It captures interactions as traces, organizes multi-turn activity into sessions, and helps teams turn problematic examples into regression cases. Because it is MIT-licensed and offered as managed cloud or self-hosted software, it can fit both hosted workflows and infrastructure teams that need to run the platform themselves.

API access, multiple-model support, prompt testing, and collaboration make Latitude suitable for a shared engineering workflow. Its focus is operational improvement of deployed agents, not simply generating or polishing prompts.

Key features

Tracing and observability

Interactions are recorded as spans and grouped into sessions, giving reviewers a coherent view of multi-turn conversations. Observe surfaces providers, models, token use, latency, cost, errors, tool calls, user activity, and agent memory. That combination supports cost investigations and technical debugging in the same place, although teams interested only in prompt drafting may find the operational scope unnecessary.

Finding recurring failures

Semantic search, behavior clustering, and signals help identify repeated production failure patterns instead of forcing teams to inspect traffic one conversation at a time. Monitors can watch signals, saved searches, or raw traffic and notify users in the app, by email, or through Slack. This is useful for teams that need an alerting loop around agent quality; it also means someone must define useful searches and signals to avoid noisy monitoring.

Regression and remediation workflow

Failing traces can become dataset cases for local simulation and regression testing, while evaluations can watch live traffic for regressions. Latitude also says it can dispatch Claude Code or Cursor with issue context and sample traces to implement a fix and open a pull request. That closes more of the loop than an observability dashboard alone, particularly for teams already using those coding agents.

SDKs, integrations, and data controls

The setup guide names TypeScript and Python SDKs and recommends an OpenTelemetry exporter for other languages. Documented integrations include Slack and GitHub, and setup can connect Claude Code, Cursor, Codex, OpenCode, Hermes, or another coding agent. SDKs redact common security-sensitive attributes by default, with configurable additional patterns. Hosted customer data and Latitude-managed inference are processed in AWS eu-central-1 in Frankfurt. Latitude states it is SOC II certified; its security documentation says ISO 27001 audit work and GDPR readiness review were in progress, with completion expected by the end of May 2026.

Pricing

Starter

Starter is free at $0.00 per month and includes 20K credits per month, 30-day data retention, unlimited seats, and general channel Slack support. It is a sensible entry point for teams establishing AI infrastructure or evaluating the workflow, but the credit allowance and short retention window can constrain sustained production monitoring.

Pro

Pro costs $99.00 per month, billed monthly. It raises the allowance to 100K credits per month, keeps unlimited seats, extends retention to 90 days, and adds priority support. Extra credits cost $20 per 10K. Pro also includes SOC2 and ISO27001 reports, making it the practical tier for collaborative AI product teams that need longer history and faster support without per-seat pricing.

Enterprise

Enterprise uses custom pricing and custom credit volume and retention. It adds a choice of on-premises or cloud deployment, RBAC, team training, SAML SSO, SOC2 and ISO27001 reports, uptime and support SLAs, and dedicated support. High-volume products and organizations with formal access-control or deployment requirements are the clearest fit.

Platforms

Latitude runs as an API, web application, or self-hosted deployment. Managed cloud is available alongside documented single-machine Docker Compose and Kubernetes cluster deployments, giving infrastructure teams a path to operate it within their own environment.

Who it's for

Choose Latitude when your team operates production agents and needs one workflow for tracing, finding repeated failures, alerting, testing fixes, and collaborating on remediation. Starter suits early infrastructure work; Pro is better for active teams that need 90-day history and priority support; Enterprise is aimed at high-volume or tightly governed deployments. It is less appropriate if your requirement is limited to prompt creation without production telemetry.

Pros and cons

• Pros: Joins trace-level observability with semantic failure discovery and regression datasets, so investigations can lead directly to repeatable checks.
• Pros: Unlimited seats on Starter and Pro avoid seat-based expansion costs, while Slack, GitHub, and coding-agent connections support team workflows.
• Pros: MIT licensing, self-hosting, configurable redaction, and EU-based AWS processing provide deployment and data-control options.
• Cons: Starter's 20K credits and 30-day retention may be insufficient for busy production traffic.
• Cons: Extra Pro usage adds $20 per 10K credits, so heavy traffic can raise the monthly bill.
• Cons: The deepest governance and support features require custom-priced Enterprise.

Alternatives

Agenta is a better fit when a smaller free allowance with explicitly capped runs and evaluations is sufficient, or when its open-source option matches your deployment preference. Vellum suits readers seeking a broader multi-platform prompt and AI workflow product with a $30-per-month Mighty plan. Google Cloud Agent Evaluation is the alternative when pay-as-you-go evaluation through Google Cloud is preferable. PromptWizard is a free, self-hosted option for prompt optimization. OpenPrompt is a free open-source prompt-learning framework for Python and PyTorch environments. ChainForge is a free, open-source self-hosted and web alternative. Prompt Engine targets prompt optimization with a free hobby tier and low-cost Pro plan. PromptEval fits teams wanting bounded web evaluations and prompt-linting quotas. For a wider directory of prompt-focused tools, see AI Prompt Generators.

Verdict

Latitude is a strong choice for teams treating agent quality as an ongoing production engineering problem: it links observability, failure pattern discovery, alerts, regression evaluation, and fix handoff in one platform. Start with Starter for a low-risk evaluation, move to Pro when retention and support matter, and look elsewhere if you only need prompt authoring or want predictable high-volume costs without custom planning.