OpenLIT

OpenLIT is an open-source platform for observing LLM and agent applications, with tracing, evaluations, prompt management, and cost tracking. It suits teams prepared to run their own OpenTelemetry-based tooling. Its broad self-hosted feature set is compelling, but teams seeking a managed service must wait for Cloud.

Overview

OpenLIT brings operational visibility and prompt workflows together: teams can inspect calls, tools, and retrievals, evaluate traffic, compare models, and track costs in one platform. That breadth can help teams working across both LLM applications and coding agents avoid splitting these tasks across separate tools. The trade-off is operational responsibility: deployment is self-hosted, using Docker Compose or Kubernetes Helm.

The Apache 2.0 license supports using and running the software under an open-source license. OpenLIT says self-hosting keeps prompts, traces, and secrets in the user's environment, a useful property for teams that want control over their data. Its Cloud plan is coming soon, so it is not a current choice for teams that want a hosted service.

Key features

Tracing and evaluations

Tracing can capture model calls, tools, and retrievals, with filters for user, session, cost, latency, and metadata. These views help teams narrow an investigation to a particular interaction or cost and performance concern. Evaluations support LLM-as-a-judge, heuristics, and human review on production traffic or experiments. That range accommodates automated checks as well as human assessment, though teams will need to choose an evaluation approach suited to their use case.

Prompt workflows and model comparison

Prompt Hub provides versioning, dynamic variables, API retrieval, unlimited prompt fetching, and AI-assisted improvement. Those capabilities support teams that want prompts managed as part of the development workflow rather than handled separately. OpenGround compares models side by side on latency, tokens, and cost, and supports comparison with a bring-your-own key. It gives teams a way to weigh these measures, rather than treating model choice as a cost-only decision.

Agent visibility and integrations

Coding agent monitoring tracks sessions, cost, tokens, repository activity, and code impact, extending observability beyond model calls. The site lists more than 56 integrations across LLMs, agent frameworks, vector databases, and GPUs, including OpenAI, Anthropic, LangChain, Pinecone, and NVIDIA NIM. That range can suit teams with varied components, though an integration count alone does not establish how deeply any particular integration fits a workflow.

Deployment and data controls

OpenLIT accepts OpenTelemetry data through OTLP and can be deployed with Docker Compose or Kubernetes Helm. Vault stores LLM API keys with AES-256-GCM encryption, key- and tag-based retrieval, and API authentication. The OSS plan supports sign-in with Google, Azure AD, and GitHub. These controls add useful options for self-hosted teams; operating and securing the deployment remains their responsibility.

Pricing

OSS: free. It includes unlimited self-hosted usage, unlimited users, unlimited projects and environments, and community support on GitHub. This is the strongest fit for teams comfortable running the platform themselves and relying on community support. Fleet Hub lists single-cluster collector monitoring, a notable boundary for teams managing collectors across multiple clusters.

Cloud: custom pricing. The plan is coming soon, with a fully hosted service and pricing and feature set to be shared at launch. There is no current hosted plan to choose, so teams needing managed deployment now should consider another product.

The OSS plan's unlimited usage, users, projects, and environments make it unusually unconstrained on those dimensions for a self-hosted option. That does not remove the work of hosting it, and GitHub community support may not suit teams that need a formal support channel.

Platforms

OpenLIT supports API, Linux, macOS, web, and Windows, and offers self-hosted deployment. Its documented deployment methods are Docker Compose and Kubernetes Helm, with OTLP for incoming OpenTelemetry data.

Who it's for

OpenLIT is a good match for engineering teams that want a self-hosted view across LLM and agent traces, evaluations, prompts, and costs, especially when OpenTelemetry and data control matter. Coding-agent monitoring and model comparison broaden its use beyond basic tracing. It is less suitable for teams that need a hosted service today, or that require support beyond the OSS plan's GitHub community.

Pros and cons

• Pros: Unlimited self-hosted users, projects, environments, and usage make the OSS plan practical for teams that want to expand without per-seat or project caps.
• Pros: Traces, evaluations, prompt workflows, model comparison, and coding-agent monitoring cover several related needs in one platform.
• Pros: Apache 2.0 licensing and self-hosting give teams control over deployment and, according to OpenLIT, keep prompts, traces, and secrets in their environment.
• Cons: Cloud is not yet available, ruling out teams that need a hosted option now.
• Cons: OSS support is through GitHub community support, which may not meet teams' need for a dedicated support channel.
• Cons: Fleet Hub's single-cluster collector monitoring may constrain teams managing more than one cluster.

Alternatives

W&B Weave is worth considering if you want a free plan with published ingestion and storage allowances: it includes 1 GB/mo Weave data ingestion and 5 GB/mo storage, alongside evaluations, tracing, and scorers. OpenLIT instead offers unlimited self-hosted usage, users, projects, and environments.

LangWatch may suit teams seeking an Apache 2.0 open-source option with a free Developer plan capped at 50k events/month and 14-day data access. OpenLIT offers unlimited self-hosted usage and projects, but its support is community-based on GitHub.

Opik is another option for teams who want open-source observability and evaluation features they can download and run locally; OpenLIT adds prompt management and cost tracking to its stated scope.

Agenta may fit a small team that prefers a free Hobby plan with defined limits of 2 team members, 5,000 agent runs/month, 20 evaluations/month, and 1-week trace retention. OpenLIT's OSS plan instead lists unlimited users and usage for self-hosted deployments.

Arize AX is a hosted SaaS alternative with a free plan that includes 25k trace spans/month, 1 GB ingestion/month, and 15-day retention. Choose it over OpenLIT if those published SaaS limits suit you better than operating a self-hosted service.

Arize Phoenix is a free, open-source, self-hosted option for teams seeking user-managed trace spans, ingestion, and projects.

Braintrust offers a free Starter plan with 1 GB processed data, 10,000 scores, and 14-day retention; it may suit teams whose needs fit those defined allowances.

Confident AI has a free plan capped at 2 user seats, 1 project, 5 test runs per week, and 1 GB-month of trace spans. It may suit a small evaluation workflow that fits those caps; OpenLIT's OSS plan is less restrictive on users and projects.

Browse AI LLM Observability Tools, AI Agent Observability Tools, Model Monitoring Software, and LLM Observability Tools for more options.

Verdict

Choose OpenLIT if you want a wide set of LLM and agent observability tools in an Apache 2.0 self-hosted platform, with unlimited OSS users, projects, environments, and usage. The central reason to look elsewhere is equally clear: Cloud is not yet available, and GitHub community support is the OSS plan's support channel.