Manifest vs OpenLIT: Which is Better in 2026?
A comprehensive comparison of Manifest and OpenLIT covering features, pricing, use cases, and which tool is the right choice for your needs.
⚡ Quick Verdict
Choose Manifest if:
- →You want more affordable paid plans (from $19/mo)
- →You need autofix retries, reroutes and repairs broken provider calls before the agent sees them or routes to subscription providers (claude, chatgpt, gemini) as well as metered apis
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
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Manifest vs OpenLIT: At a Glance
Pricing Comparison: Manifest vs OpenLIT
Understanding the pricing differences between Manifest and OpenLIT is crucial for making the right choice. Here's how their plans compare side by side.
OpenLIT Pricing
💡 Pricing takeaway: Both Manifest and OpenLIT 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 Manifest and OpenLIT stacks up.
What Makes Each Tool Unique
🔵 Unique to Manifest
Features available in Manifest but not in OpenLIT:
- ✓Autofix retries, reroutes and repairs broken provider calls before the agent sees them
- ✓Routes to subscription providers (Claude, ChatGPT, Gemini) as well as metered APIs
- ✓MIT-licensed and self-hostable with Docker at no request cap
- ✓Local and custom provider support
- ✓Usage, reliability and cost observability in early access
🟣 Unique to OpenLIT
Features available in OpenLIT but not in Manifest:
- ✓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
Use Case Recommendations
Best for: Manifest
Manifest is an open-source LLM router that sits between your agents and whichever providers you use, under an MIT licence with roughly 7.4k GitHub stars. Its distinguishing feature is Autofix: rather than only routing and logging, it retries, reroutes and repairs broken provider calls before the failure ever reaches your agent. That targets a specific and under-served failure mode — agent harnesses tend to handle a 500 or a rate-limit badly, aborting a multi-step run because one upstream call blipped, and the resulting partial work is often unrecoverable. Manifest's second unusual capability is that it routes to subscription providers, not just pay-per-token APIs: Claude, ChatGPT and Gemini consumer or team subscriptions can be used as inference backends alongside metered API keys, local models and custom providers. For anyone already paying for a coding-agent subscription, that removes a duplicate inference bill. Observability — usage, reliability and cost tracking — is in early access. The deployment story is the reason the free tier is genuinely useful rather than a funnel: self-hosting with Docker under the MIT licence has no request cap at all, and the free hosted Cloud tier allows 10,000 routed requests a month with unlimited agents, all providers and no restrictions, seven-day dashboard retention, Autofix, budget alerts and Discord support.
Ideal use cases:
- •Teams or individuals who need autofix retries, reroutes and repairs broken provider calls before the agent sees them
- •Teams or individuals who need routes to subscription providers (claude, chatgpt, gemini) as well as metered apis
- •Teams or individuals who need mit-licensed and self-hostable with docker at no request cap
- •Teams or individuals who need local and custom provider support
- •Anyone focused on llm-gateway workflows
- •Anyone focused on open-source workflows
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
🤖 Other AI Agent Infrastructure Tools to Consider
Manifest and OpenLIT 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 Manifest better than OpenLIT?
It depends on your needs. Manifest offers 5 key features including Autofix retries, reroutes and repairs broken provider calls before the agent sees them and Routes to subscription providers (Claude, ChatGPT, Gemini) as well as metered APIs, while OpenLIT provides 5 features including OpenTelemetry-native tracing — spans go to your existing collector and backend and Instruments GPUs, LLMs, MCP servers, vector DBs and coding agents. Manifest uses a freemium model with a free tier, while OpenLIT is free with free access available. Choose based on which features and pricing model align with your requirements.
Is Manifest cheaper than OpenLIT?
OpenLIT doesn't have standard paid plans, while Manifest starts at $19/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 Manifest and OpenLIT together?
Yes, many users combine Manifest and OpenLIT in their workflow. Manifest excels at autofix retries, reroutes and repairs broken provider calls before the agent sees them, while OpenLIT shines with opentelemetry-native tracing — spans go to your existing collector and backend. 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 Manifest and OpenLIT?
While both are ai agent infrastructure tools, Manifest emphasizes autofix retries, reroutes and repairs broken provider calls before the agent sees them, whereas OpenLIT is known for opentelemetry-native tracing — spans go to your existing collector and backend. The best choice depends on your specific workflow and feature priorities.
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