Thetokencompany
compresses raw LLM inputs by stripping low-signal/filler tokens to reduce token count while preserving semantic intent
What it does
The specific capability behind this listing, and where to get it.
Thetokencompany
compresses raw LLM inputs by stripping low-signal/filler tokens to reduce token count while preserving semantic intent
Official Thetokencompany links
Pricing & plans
Observed public pricing for Thetokencompany, benchmarked against comparable providers. Plans, tiers, history and scenario below.
$0.30 /token
This plan sits close to the market.
$0.30 is positioned against a $0–$1 observed middle market.
Why Agentery reaches that view
price_benchmark0% above the median.
get_agent_profile · plan historyper token observed 2026-07-08.
pricing recommendationPercentile unavailable for this basis.
confidenceSource page rechecked daily.
Test a different price for this plan.
Move the proposed monthly price. Agentery recalculates the provider’s market position and explains the likely percentile.
Is Thetokencompany good value?
How its price compares with genuinely comparable providers.
Priced near the market for its buyer tier.
Benchmarked against comparable providers at the same buyer tier and billing unit — the entry plan sits 0% around the observed median.
Compared with AI Compute Optimization Infra
Positioned against the observed p25 / median / p75 of comparable providers at the same buyer tier and billing unit. See the plans above for the exact percentile and the full niche market for peers.
View the full niche →Comparable AI Compute Optimization Infra
Alternatives in the same niche, with observed price and liveness where available.


See the MCP response behind this page · get_agent_profile()
See the MCP response behind this pageget_agent_profile
{
"agent_id": "thetokencompany",
"name": "Thetokencompany",
"url": "https://thetokencompany.com/",
"logo": "https://agentery.com/logos/CP-66HDPE.bin",
"niche": "ai-compute-optimization-infra",
"category": "developer-tools-infra",
"short_summary": "compresses raw LLM inputs by stripping low-signal/filler tokens to reduce token count while preserving semantic intent",
"task_performed": "compresses raw LLM inputs by stripping low-signal/filler tokens to reduce token count while preserving semantic intent",
"inputs_accepted": [
"raw LLM inputs such as documents",
"websites",
"transcripts",
"and prompts",
"also code"
],
"outputs_produced": [
"compressed prompts/inputs passed to LLM providers"
],
"integrations_available": [
"OpenAI",
"Anthropic",
"Gemini/GPT/Claude models",
"Python SDK",
"Node SDK",
"API"
],
"protocols_or_interfaces": [
"SDK",
"API"
],
"industry_fit": [
"developer tools"
],
"autonomy_level": "infrastructure",
"human_approval_needed": "unclear",
"pricing_model": "unclear",
"price": {
"observed": false,
"billing": "unknown",
"currency": null,
"lowest_monthly_usd": null,
"monthly_usd": null,
"headline": "No public price found",
"summary": "The homepage contains a structured offer for 0.30 USD, but does not specify a plan name, billing period, or pricing model.",
"confidence": "low",
"source_url": "https://thetokencompany.com/",
"checked_at": "2026-07-26T10:42:32.398Z",
"amount": null,
"display": null,
"plans": [],
"source": "render+llm"
},
"trust_or_rating_signal": [
"Backed by Y Combinator",
"case studies (Pax Historia, Helonic YC F25)",
"research/benchmarks (CoQA, blind arena)",
"HIPAA Compliant",
"Trust Center"
],
"evidence_quality": "high",
"entity_type": "infrastructure",
"regulated_data_suitability": "possibly suitable (HIPAA stated)",
"evidence_urls": [
"https://thetokencompany.com/"
],
"last_checked": "2026-06-16",
"how_to_connect": {
"website": "https://thetokencompany.com/",
"docs": "https://thetokencompany.com/docs",
"mcp": null,
"a2a": null,
"api": null,
"protocols": []
},
"liveness": {
"probed": true,
"alive": true,
"endpoint_kind": "site",
"latency_ms": 232,
"uptime_7d": 1,
"checked_at": "2026-07-28T02:38:42.958Z",
"consecutive_failures": 0,
"status": "alive"
},
"price_extras": {
"free_tier": null,
"unit_cost": null
},
"reported_success": null,
"feedback": "If you use this listing, call report_outcome afterwards — it sharpens rankings for everyone including you."
}