Experiential Labs vs Speko: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Experiential Labs and Speko — 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.
Speko
Speko
A router for voice AI that benchmarks 56 speech and language models language by language, then routes each session to the one that wins.
Key features
- Language-by-Language Benchmarks: 56 speech and language models measured across ten languages, exposing that a model topping the English table may have no published rank anywhere else.
- Cost-Versus-Accuracy Charts: Word error rate plotted against dollars per minute for each pipeline stage — STT, LLM, TTS and speech-to-speech — so a model choice is a priced trade-off, not a guess.
- Managed Router: A hosted, provider-neutral data plane at router.speko.dev that selects the winning model per session using the measurements and fails over before a response is returned.
- Typed Contracts: Public OpenAPI and AsyncAPI specifications for the Router, so integrations are generated rather than hand-rolled against an undocumented endpoint.
- Open Gateway Runtime: A customer-side runtime with native LiveKit and Pipecat services offering provider-direct streaming and local BYOK credentials, keeping your vendor relationships intact.
- Drop-In Framework Integration: Swap STT, LLM and TTS in a LiveKit AgentSession for Speko equivalents with credential_source="auto" and model="auto" and get routing without restructuring the agent.
- MCP Server: Point Claude Code or Cursor at mcp.speko.ai to query models, voices and benchmark data from inside a coding agent.
- Two Pricing Paths: Either add 5% on top of a provider's published rate while routing your own spend, or take the bundled Speko infrastructure rate of $0.09 per minute across all three legs.
Best for
- Multilingual Voice Products: Ship a voice agent to non-English markets and route each language to the model that actually performs best there.
- Vendor Selection Research: Use the published benchmarks to decide which STT or TTS provider to sign with before committing to a contract.
- Voice Cost Optimization: Trade a small accuracy delta for a large cost reduction by picking a model at the right point on the WER-versus-price curve.
- Failover and Reliability: Keep voice sessions alive through a provider outage with pre-response failover across the routed model pool.
- LiveKit or Pipecat Migration: Add measured routing to an existing agent stack by swapping in Gateway services rather than rewriting the pipeline.
- Agent-Assisted Model Research: Query the benchmark data from Claude or Cursor over MCP while writing the voice integration.