Experiential Labs vs Kastra: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Experiential Labs and Kastra — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Experiential Labs
Experiential Labs
Open-source AI gateway that routes every model through one endpoint at provider cost, then improves that traffic with caching, routing and fine-tuning.
Key features
- Unified Model Endpoint: One OpenAI-compatible POST endpoint fronts every hosted provider, your own bring-your-own keys and your own GPUs, so switching models is a parameter change rather than an integration.
- Zero-Markup Routed Tokens: Routed traffic bills at the provider's list price with 0% added on top, with the company earning on hosted inference and the Pro plan instead of on your tokens.
- Model Recommendation from Traffic: The intelligence layer watches real request patterns and tells you when switching models would win, including newly released models on the day they ship, with optional per-prompt optimization.
- Caching Opportunity Detection: Identifies where cache hit rate could improve and shows the projected savings, with repeated tokens returning at 90% off once enabled.
- Traffic-Trained Custom Models: Fine-tunes a model on your own traffic and proves it in closed-loop simulation before it ever serves, then exposes it through the same endpoint you already call.
- Spend Attribution Console: Breaks requests and dollars down by agent, person, model, provider and day across the whole organization, alongside catalog, usage and limits.
- Live Request Logs and Metrics: Streams per-request time-to-first-token, token counts, provider, status and cost, with dashboard rollups for requests, spend, p50 TTFT and cache hit rate.
- Governance Controls: Budgets, provider allowlists and attribution are available from the free tier upward for controlling who can spend what on which models.
Best for
- Consolidating Multi-Provider Access: Replace separate SDKs and keys for OpenAI, Anthropic, Google and others with a single endpoint and key across every application.
- Cutting Inference Spend: Use caching recommendations and model-switch suggestions to lower the cost of an existing production workload without changing application code.
- Replacing a Frontier Model with a Small One: Distill or fine-tune a small model on your own traffic for a narrow repetitive task and serve it at a fraction of frontier-model cost and latency.
- Chargeback and Budgeting: Attribute AI spend to individual agents, teams or people for internal cost allocation and to enforce per-key budget caps.
- Evaluating New Model Releases: Compare a newly shipped model against your current one on your own traffic before committing to a migration.
- Hybrid Local and Hosted Serving: Route some workloads to self-hosted GPUs at zero marginal cost while sending the rest to hosted providers through the same interface.
- Self-Hosting the Gateway: Run the open-source gateway inside your own infrastructure when hosted routing is not an option.
Kastra
Kastra Labs Inc
Kastra is the runtime authorization layer for AI: it decides in sub-milliseconds what agents, models, and tools are allowed to do before they act.
Key features
- Sub-Millisecond Policy Decisions: Every prompt, tool call, and API request is evaluated in under a millisecond so enforcement never becomes the bottleneck.
- Deterministic Policy Engine: Rules are written and evaluated deterministically, not by an LLM judge, so the same input always produces the same allow/deny.
- Kastra Edge (Local Enforcement): A local enforcement component that runs next to the agent so decisions happen even without a network round-trip.
- Cryptographic Audit Trails: Signed logs of every decision give security and compliance teams tamper-evident evidence of agent behavior.
- Coding Agent Integrations: First-class hooks for Claude Code, Cursor, Codex, and OpenClaw let policies wrap the tool calls those agents already make.
- Kastra Recon: Discovers what actions an agent actually attempts in a codebase or environment, so policies can be authored from observed behavior instead of guesses.
- Zero Implicit Trust Model: Nothing an agent asks to do runs until it is explicitly allowed by policy, aligning agent access with zero-trust principles.
Best for
- Guardrails for Coding Agents: Prevent an autonomous coding agent from dropping tables, force-pushing to main, or leaking secrets during long runs.
- Enterprise Rollout Approvals: Give security teams a control plane before letting employee-facing AI agents access internal APIs.
- Regulated-Industry Agent Deployments: Provide the auditable authorization trail required in finance, healthcare, or government agent pilots.
- Multi-Agent Systems: Enforce per-agent scopes so a research agent can read data but only a deploy agent can trigger production changes.
- Incident Forensics: Reconstruct exactly what an AI agent was allowed or blocked from doing after a suspicious action.