OpenLIT

OpenLIT is an open-source platform for tracing and evaluating LLM and agent activity, managing prompts, and tracking costs. It is best suited to technically capable teams that want to self-host on OpenTelemetry; the trade-off is that hosted Cloud service is not yet available.

Overview

OpenLIT brings operational visibility and prompt workflows into one platform. It can capture model calls, tools and retrievals, then support evaluation, model comparison and cost review across production traffic and experiments. That breadth is useful for teams building agents as well as conventional LLM applications, but it also means the best fit is a team prepared to deploy and operate its own tooling.

The Apache 2.0 license and Docker Compose or Kubernetes Helm deployment give teams control over the environment. OpenLIT says self-hosting keeps prompts, traces and secrets within the user's environment. Teams that want a managed service instead will need to wait: Cloud is coming soon.

Key features

Tracing and evaluations

Tracing covers calls, tools and retrievals, with filters for user, session, cost, latency and metadata. Those dimensions help teams investigate a costly or slow interaction without losing the context of the agent's work. Evaluations can use an LLM judge, heuristics or human review, and run against production traffic or experiments. This range accommodates both automated checks and human judgment, though it asks teams to decide which evaluation method suits each use.

Prompt Hub and model comparison

Prompt Hub provides versioning, dynamic variables and API retrieval, with unlimited prompt fetching and AI-assisted improvement. It gives teams a way to manage changing prompts alongside observability rather than treating prompt deployment as an unrelated workflow. OpenGround compares models side by side on latency, tokens and cost, including comparisons using a team's own model keys; those measures make it useful when balancing performance against spend.

Agent monitoring, integrations and secrets

Coding agent monitoring tracks sessions, cost, tokens, repository activity and code impact, extending visibility beyond model calls to coding work. The 56+ integrations span LLMs, agent frameworks, vector databases and GPUs, including OpenAI, Anthropic, LangChain, Pinecone and NVIDIA NIM. Vault stores LLM API keys with AES-256-GCM encryption and supports key- and tag-based retrieval with API authentication. Together, these capabilities make OpenLIT relevant to teams with a varied AI stack, not just those using one model provider.

Pricing

OSS

OSS costs nothing. It includes unlimited self-hosted usage, users, projects and environments, plus community support through GitHub. There is no stated usage quota or seat cap, which makes it a strong fit for teams willing to manage deployment themselves. The trade-off is operational responsibility and community support rather than a hosted service. The plan lists Google, Azure AD and GitHub sign-in.

Cloud

Cloud is coming soon, with custom pricing and a hosted service whose pricing and feature set will be shared at launch. It is not a current option for teams that need a managed deployment today.

Platforms

OpenLIT supports API, Linux, macOS, self-hosted, web and Windows. Self-hosting is available through Docker Compose or Kubernetes Helm, and the platform accepts OpenTelemetry data through OTLP. Fleet Hub lists single-cluster collector monitoring, a meaningful limit for teams operating collectors across multiple clusters; the unlimited usage, users, projects and environments in the self-hosted plan do not remove that monitoring constraint.

Who it's for

OpenLIT suits engineering teams that want one self-hosted place to inspect LLM and agent traces, evaluate behavior, manage prompts and compare model costs. Its broad integrations and coding-agent monitoring are particular strengths for teams working across frameworks or building coding agents. It is less suitable for buyers who need managed hosting now, or organizations that require support beyond a GitHub community.

Pros and cons

• Pros: Unlimited self-hosted users, projects, environments and usage avoid plan quotas as a team grows.
• Pros: Tracing, evaluations, prompt versioning and model comparisons cover several connected development tasks in one platform.
• Pros: Apache 2.0 licensing, OTLP ingestion and deployment via Docker Compose or Helm give teams deployment flexibility and data control.
• Cons: Cloud is not yet available, so teams seeking hosted operations cannot adopt it as a managed service today.
• Cons: OSS support is community-based on GitHub, and self-hosting leaves deployment and operation to the team.
• Cons: Fleet Hub's collector monitoring is limited to a single cluster, which may constrain multi-cluster operations.

Alternatives

PetalTrace is another free option with API, Linux, macOS, self-hosted, web and Windows platforms; its maker describes itself as building modular, composable tools for agentic AI systems.

Choose W&B Weave if a hosted alternative with published limits is preferable: its free plan includes 1 GB/mo Weave data ingestion, 5 GB/mo storage, and AI application evaluations, tracing and scorers.

SigNoz offers a self-hosted community plan at no cost, with infrastructure, storage, scaling, upgrades and backups managed by the user.

Opik is an alternative whose core observability and evaluation features are available as open-source code to run locally.

Agenta may suit smaller teams seeking explicit entry-plan caps: its Hobby plan is free and includes 2 team members, 5,000 agent runs/month, 20 evaluations/month and 1-week trace retention.

Langfuse is another freemium option; its Hobby plan is free and includes 50k units / month, 30 days of data access and 2 users.

Traccia offers a free Hobby plan with 50K events and 7 days retention, while its Observe plan is $99 a month with 500K events.

TraceRoot is another freemium alternative for teams considering web-based tooling.

Verdict

Choose OpenLIT if your team can self-host and wants observability, prompt management and evaluation together without per-seat or usage caps in the OSS plan. Its combination of OpenTelemetry support, broad AI-stack integrations and data control is the central reason to choose it. Look elsewhere if you need hosted service or non-community support now; Cloud has not launched, and OSS expects your team to run the platform.