OpenLIT vs Preloop: Which is Better in 2026?
A comprehensive comparison of OpenLIT and Preloop covering features, pricing, use cases, and which tool is the right choice for your needs.
⚡ Quick Verdict
Choose OpenLIT if:
- →You need opentelemetry-native tracing — spans go to your existing collector and backend or instruments gpus, llms, mcp servers, vector dbs and coding agents
Choose Preloop if:
- →You want more affordable paid plans (from $29/mo)
- →You need a broader feature set (6 features vs 5)
- →You need mcp firewall with allow, deny and require-approval rules on tool access or ai model gateway with per-agent budgets and cost attribution
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OpenLIT vs Preloop: At a Glance
Pricing Comparison: OpenLIT vs Preloop
Understanding the pricing differences between OpenLIT and Preloop is crucial for making the right choice. Here's how their plans compare side by side.
OpenLIT Pricing
Preloop Pricing
💡 Pricing takeaway: Both OpenLIT and Preloop offer free tiers, making it easy to try before you buy. Compare the specific plans to find the best value for your use case.
Feature-by-Feature Comparison
Here's how every feature from OpenLIT and Preloop stacks up.
What Makes Each Tool Unique
🔵 Unique to OpenLIT
Features available in OpenLIT but not in Preloop:
- ✓OpenTelemetry-native tracing — spans go to your existing collector and backend
- ✓Instruments GPUs, LLMs, MCP servers, vector DBs and coding agents
- ✓Built-in LLM evaluations and model comparison
- ✓Prompt management and an API key vault
- ✓Apache 2.0, self-hosted via Helm chart or Docker with OAuth sign-in
🟣 Unique to Preloop
Features available in Preloop but not in OpenLIT:
- ✓MCP firewall with allow, deny and require-approval rules on tool access
- ✓AI model gateway with per-agent budgets and cost attribution
- ✓Policy-as-code in YAML with CEL expressions
- ✓Human approvals on mobile, watch, Slack, Mattermost or webhook
- ✓One-command discovery and transparent rewrite of existing agent configs
- ✓Runtime session observability and an audit trail for AI Act evidence
Use Case Recommendations
Best for: OpenLIT
OpenLIT is an Apache 2.0 open-source observability and engineering platform for LLM and agent workloads, built on OpenTelemetry rather than on a proprietary tracing format. That choice is the substance of the product: because instrumentation emits standard OTel spans, traces can go to your existing collector and backend instead of being locked into a vendor's storage, and the same pipeline that carries your service traces carries your agent traces. Coverage runs wider than the usual LLM-call span — it instruments GPUs, LLMs, MCP servers, vector databases and coding agents, which means an agent's slow step can be attributed to the retrieval layer or the GPU rather than assumed to be the model. Around tracing it adds the adjacent pieces teams otherwise assemble separately: running LLM evaluations, managing prompts, comparing models against one another, and storing API keys in a built-in vault rather than in environment variables scattered across services. The lifecycle framing on the site runs instrument, develop, manage, observe, improve, covering both development and production stages. Deployment is self-hosted via Helm chart or Docker with OAuth sign-in, documented for both. A fully hosted OpenLIT Cloud is announced for teams that would rather not operate it, but is not yet available.
Ideal use cases:
- •Teams or individuals who need opentelemetry-native tracing — spans go to your existing collector and backend
- •Teams or individuals who need instruments gpus, llms, mcp servers, vector dbs and coding agents
- •Teams or individuals who need built-in llm evaluations and model comparison
- •Teams or individuals who need prompt management and an api key vault
- •Anyone focused on opentelemetry workflows
- •Anyone focused on observability workflows
Best for: Preloop
Preloop is an Apache-2.0 licensed control plane for AI agents, aimed at the governance problem that appears the moment more than one agent is running in an organisation: nobody can see what tools they can reach, what they are spending, or who approved anything. It bundles six capabilities into one self-hostable platform — an MCP firewall that defines allow, deny and require-approval rules on tool access; an AI model gateway that attributes cost and enforces per-agent budgets; policy-as-code written in YAML with CEL expressions; human-in-the-loop approvals delivered to mobile, watch, Slack, Mattermost or a webhook; runtime session observability; and an audit trail. Onboarding is deliberately frictionless: `preloop agents discover` finds compatible agent configurations already on the machine and transparently rewrites them so tool calls route through the MCP firewall and model traffic through the gateway, with no SDK changes and no agent code changes. The named runtimes it rewrites include Claude Code, Codex CLI, Cursor, Gemini CLI, Hermes, OpenClaw, OpenCode and Windsurf. The vendor positions Preloop explicitly as an open-source alternative to AWS Bedrock AgentCore and as a way to build EU AI Act readiness evidence, with dedicated guidance published for that use case. Because the full control plane is Apache 2.0 and self-hostable, the paid tiers sell hosting, team governance and conditional-approval sophistication rather than the core capability.
Ideal use cases:
- •Teams or individuals who need mcp firewall with allow, deny and require-approval rules on tool access
- •Teams or individuals who need ai model gateway with per-agent budgets and cost attribution
- •Teams or individuals who need policy-as-code in yaml with cel expressions
- •Teams or individuals who need human approvals on mobile, watch, slack, mattermost or webhook
- •Anyone focused on mcp workflows
- •Anyone focused on agent-governance workflows
🤖 Other AI Agent Infrastructure Tools to Consider
OpenLIT and Preloop aren't the only options. Here are other popular tools in the same space:
SuperAGI
Open-source autonomous AI agent framework with visual dashboard — 14K GitHub stars
MetaGPT
Multi-agent AI framework simulating software teams — 45K GitHub stars, builds full apps from prompts
Cerebras
Fastest LLM inference powered by the Wafer Scale Engine.
Scale AI
AI data platform for training data and model evaluation.
Roboflow
End-to-end computer vision platform for developers.
Labelbox
Enterprise data labeling platform for ML training datasets.
Is one of these your tool?
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Frequently Asked Questions
Is OpenLIT better than Preloop?
It depends on your needs. OpenLIT offers 5 key features including OpenTelemetry-native tracing — spans go to your existing collector and backend and Instruments GPUs, LLMs, MCP servers, vector DBs and coding agents, while Preloop provides 6 features including MCP firewall with allow, deny and require-approval rules on tool access and AI model gateway with per-agent budgets and cost attribution. OpenLIT uses a free model with a free tier, while Preloop is open-source with free access available. Choose based on which features and pricing model align with your requirements.
Is OpenLIT cheaper than Preloop?
OpenLIT doesn't have standard paid plans, while Preloop starts at $29/month. Both tools offer free tiers, so you can try each before committing. Always check the official websites for the most current pricing.
Can I use OpenLIT and Preloop together?
Yes, many users combine OpenLIT and Preloop in their workflow. OpenLIT excels at opentelemetry-native tracing — spans go to your existing collector and backend, while Preloop shines with mcp firewall with allow, deny and require-approval rules on tool access. Using both allows you to leverage the strengths of each tool, though this means managing two subscriptions — though free tiers can help manage costs.
What's the main difference between OpenLIT and Preloop?
While both are ai agent infrastructure tools, OpenLIT emphasizes opentelemetry-native tracing — spans go to your existing collector and backend, whereas Preloop is known for mcp firewall with allow, deny and require-approval rules on tool access. The best choice depends on your specific workflow and feature priorities.
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