Kashikoi is live · custom pricing · last verified 24 Oct 2026 · viability monitor
Kashikoi
Kashikoi is a cloud simulation engine that benchmarks your AI agent from a single prompt — no code, no manual grading.
Kashikoi attacks a real bottleneck: eval authoring. Its one-prompt setup, synthetic data generation, and per-scenario scoring across accuracy, latency and cost per call are the metrics teams actually argue about before launch, and the GPT-4 vs GPT-5 vs Perplexity comparison view makes model selection a data question rather than a hunch. The catch is coverage: four example agent types is a narrow surface, and the site says custom integrations for your AI stack are something the team builds for you, so onboarding depends on their side of the desk. If you're drowning in eval sprawl and your agent fits support, data, code or security analysis, ask for a demo. If your priority is a prebuilt
Verdict generatedVerified 6d ago · liveness 56/100 · cite: rightaichoice.com/tools/kashikoi
- AI product teams shipping agentic applications
- ML engineers choosing between model versions
- Product managers who need benchmark evidence before launch
- QA teams automating AI regression tests
- Non-technical users unfamiliar with agent traces
- Teams whose testing is static assertions rather than simulated conversation
- Companies that require an on-premise deployment
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Skip Kashikoi if your agent doesn't fit one of the documented shapes — support bot, data agent, code assistant or SOC analyst — and you need a self-service connector library rather than connectors the vendor builds for you.
Because connectors are bespoke, budget engineering time on your side to describe and validate the integration before simulations can run at full coverage.
Kashikoi's pricing fits teams whose volume aligns with the published tiers. Compare against the alternatives listed below for stage-specific value.
In short
Kashikoi — Kashikoi is a cloud simulation engine that benchmarks your AI agent from a single prompt — no code, no manual grading. Best for AI product teams shipping agentic applications, ML engineers choosing between model versions, Product managers who need benchmark evidence before launch. Pricing is on request (custom quotes).
What is Kashikoi?
Kashikoi — Kashikoi is a cloud simulation engine that benchmarks your AI agent from a single prompt — no code, no manual grading. Best for AI product teams shipping agentic applications, ML engineers choosing between model versions, Product managers who need benchmark evidence before launch. Pricing is on request (custom quotes).
Kashikoi is a Y Combinator-backed simulation engine for evaluating AI agents. You write one prompt; Kashikoi generates synthetic multi-turn interactions, runs them against your agent, and scores the outcomes. The examples on its homepage cover four agent types: a SOC AI analyst scored on threat detection (85.7%), actionable recommendations (85.7%), false positives (14.3%) and severity accuracy (71.4%); an AI data analyst scored on accuracy, context understanding and pivot match rate, with a metric-by-metric breakdown showing where GPT-4 beat GPT-5 and which runs both failed; a code explorer scoring architecture decisions, change proposals, solution evaluation, impact assessment, code analysis and refactor planning; and a head-to-head model comparison of OpenAI, Anthropic/Claude and Perplexity responses aggregated across conversations. Runs are tracked cumulatively — one dashboard example shows 3.7K total runs, 89% average success, per-category latency and cost per call in dollars, then reports prompt-level deltas such as accuracy 73% to 94%, response time 3.2s to 1.1s, and cost per call $0.08 to $0.04 alongside 847 synthetic data points and 12 optimized prompts. Metrics, comparisons and visualizations are configurable to your requirements, and the vendor states it will build connectors for your AI stack to ensure full coverage during testing. It is built for teams shipping agentic products who are still writing and babysitting evaluations by hand.
Key features
- One-prompt, no-code test setup
- Autonomous synthetic conversation generation
- Multi-scenario simulation runs with edge-case detection
- Per-scenario pass/fail scoring
- Accuracy, latency and cost-per-call metrics
- Per-task-type performance breakdown
- Model comparison mode (GPT-4 vs GPT-5 vs Perplexity)
- Live dashboards with cumulative run history
- Customizable metrics, comparisons and visualizations
- Synthetic data point generation for prompt tuning
- Prompt optimization insights from run results
- Continuous simulation runs for regression tracking
- Prebuilt agent examples: SOC AI analyst, data analyst, code explorer
- Custom-built connectors for your AI stack
- Web-based platform
Our long-form take written Jul 17, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
Models under the hood
as of 2026-10-25
Limitations
- The publicly documented agent shapes are narrow: Kashikoi's homepage walks through a SOC AI analyst, an AI data analyst, a code explorer and a model comparison, with the seed material describing additional agent types as planned rather than shipped — confirm yours is covered.
- Connectors to your AI stack are described as custom work the team builds for you ("We will build the connectors you need"), so onboarding depends on their schedule rather than a self-service library.
- The evidence shows only a web-based experience; there is no indication of mobile, desktop or offline runtime.
- Because the platform runs simulations rather than live traffic, results depend on how well the generated scenarios reflect your real users.
as of 2026-10-05
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Is Kashikoi still active?
Kashikoi is live · custom pricing · last verified 24 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is Kashikoi? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What do users say about Kashikoi?
What people actually say about Kashikoi — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
ResearchedDiscussed by users across 1 source (YouTube) · researched Aug 28, 2026.
Average across the 1 source that answered — each source counts once, not each post.
- +No-code setup: start with a single prompt, no engineering.
- +Autonomous simulation of realistic multi-scenario interactions.
- +Automated scoring on accuracy, latency, and cost per call.
- +Edge-case detection surfaces failures before production.
- +Comparison mode: benchmark GPT-4 vs GPT-5 vs Perplexity.
- −Zero verified user feedback on the actual tool.
- −Name collision with an unrelated language school confuses search.
- −YouTube results show no genuine product reviews.
- −Pricing is hidden—no self-serve tiers or free trial.
- −Integrations are built on demand, not out-of-the-box.
- • No transparent pricing—likely high enterprise cost
- • Integrations built on demand may carry additional fees
How we verified this
Every listed fact about Kashikoi— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 24 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Kashikoi 12 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed pricing tiers
- — changed who should skip it, switching out, switching in, description, features, use cases, pricing fit, hidden costs, setup time, who it suits, limitations, summary, our verdict
- — changed SSL certificate
- — changed limitations
- — changed link health
- — changed use cases, who it suits, description, our verdict, features, hidden costs, limitations, switching in, switching out, models, pricing fit, setup time, who should skip it, summary
Showing the 6 most recent of 12 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Kashikoi cost in 2026?
Kashikoi: Pricing is on request (custom quotes). Pricing last verified 24 Oct 2026.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where Kashikoi's pricing actually pencils out — and where peers do it cheaper.
Kashikoi's pricing fits teams whose volume aligns with the published tiers. Compare against the alternatives listed below for stage-specific value.
Has Kashikoi's pricing changed?
No plan price for Kashikoi has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Kashikoi?
Pick Kashikoi if you are AI product teams shipping agentic applications, ML engineers choosing between model versions, Product managers who need benchmark evidence before launch, QA teams automating AI regression tests.
Look elsewhere if you are Non-technical users unfamiliar with agent traces, Teams whose testing is static assertions rather than simulated conversation, Companies that require an on-premise deployment, Teams wanting a prebuilt connector library they can wire up themselves today.
Skip it if Skip Kashikoi if your agent doesn't fit one of the documented shapes — support bot, data agent, code assistant or SOC analyst — and you need a self-service connector library rather than connectors the vendor builds for you.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Kashikoi actually fits — and what changes day-one when you adopt it.
You've got a GPT-4 based support agent and want to know if GPT-5 is worth the switch. You point Kashikoi at the agent, write one prompt describing the support scenarios, and let it generate and run the conversation set.
Outcome: You get a metric-by-metric comparison (like the 8-metric data-analyst breakdown showing where each model wins and where both fail) instead of one aggregate accuracy number, so the model decision is defensible in review.
Every prompt change risks silently breaking behavior. You set up continuous simulation runs for your code assistant, reusing the same scenario set across architecture decision, change proposal and refactor planning tasks.
Outcome: A dashboard like the code explorer view — overall score, pass counts, average response time, tokens, and per-task-type pass rates — tells you within a run whether the change helped or hurt.
Before letting an AI analyst triage alerts, you simulate threat scenarios across 7 cases to see how it classifies and how noisy it is.
Outcome: You learn that threat detection is 85.7% but false positives are 14.3% and severity accuracy only 71.4%, which is enough evidence to hold the deployment and fix alert logic and prioritization first.
Use cases
- Benchmark a customer support bot on 1,200+ simulated conversations and see success rate, latency and cost per call
- Score a SOC analyst agent on threat detection, actionable recommendations, false positives and severity accuracy
- See which of two models wins metric-by-metric on your own scenarios instead of an aggregate score
- Find edge cases and failure modes in an AI feature before it reaches real users
- Track accuracy, response time and cost regressions across continuous simulation runs
- Test a code assistant on architecture decisions, refactor planning and change impact assessment
- Compare OpenAI, Anthropic and Perplexity responses on the same interview or analysis prompt set
Setup time & first value
How long it actually takes to get something useful out of Kashikoi — broken out by persona, not the marketing-page minute.
For a team whose agent matches one of the documented shapes (support bot, data agent, code assistant, SOC analyst), first simulated results come quickly — the pitch is literally one prompt — but plan on extra time for the custom connector work Kashikoi says it builds for your stack. Most of the calendar time is integration, not simulation.
Switching to or from Kashikoi
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From hand-written eval scripts: replace the assertion suite with one prompt plus generated scenarios, then compare pass/fail per scenario in the dashboard.
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “Kashikoi”, and we withheld 6: 6 could not be judged, because “Kashikoi” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Kashikoi.
Official links
What are the best Kashikoi alternatives?
The closest alternatives to Kashikoi we list are CodeClash, Goodfire and Sharbo. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| CodeClash | Free | Free | — |
| Goodfire | Paid | Pricing not published | — |
| Sharbo | Contact Sales | Custom pricing | — |
Alternatives to Kashikoi
View all
Tools that pair well with Kashikoi
Common stack mates teams adopt alongside Kashikoi, with the specific reason each pairing earns its keep.
CodeClash
CodeClash benchmarks goal-oriented software engineering by making AI models build and evolve codebases to win 15-round arena tournaments.
Goodfire
Goodfire's Silico is an AI interpretability agent for reverse-engineering, debugging, and steering model internals.
Sharbo
Sharbo builds a skill network for AI agents — encode expert workflows once and let every agent activate them autonomously.
Kashikoi vs its closest rival
Kashikoi's closest head-to-head is with Presto Voice — Kashikoi and Presto Voice serve entirely different markets. Kashikoi is a simulation engine for AI product teams to benchmark agent performance before launch, while Presto Voice is a vertical-specific voice AI for QSR drive-thrus. Choose Kashikoi if you need to evaluate AI agents autonomously and optimize prompts; choose Presto Voice if you operate a drive-thru chain and want to automate orders and increase revenue via upselling. They are not direct competitors. Verdict generated
Head-to-head comparisons
Kashikoi vs Presto Voice
Kashikoi and Presto Voice serve entirely different markets. Kashikoi is a simulation engine for AI product teams to benchmark agent performance before launch, while Presto Voice is a vertical-specific voice AI for QSR drive-thrus. Choose Kashikoi if you need to evaluate AI agents autonomously and optimize prompts; choose Presto Voice if you operate a drive-thru chain and want to automate orders and increase revenue via upselling. They are not direct competitors.
GeneratedKashikoi vs Screenplayiq
Choose Kashikoi if you build and deploy AI agents and need autonomous, continuous simulation to catch edge cases before launch. Choose ScreenplayIQ if you're in the film industry and want data-driven predictions of a script's box office potential. They serve completely different buyers—no overlap.
GeneratedKashikoi vs Truleo
Truleo and Kashikoi serve entirely different markets. Truleo is purpose-built for law enforcement to surface leads from siloed data using features like jail call analysis and report writing automation. Kashikoi is a simulation engine for AI teams to benchmark agents autonomously. Choose Truleo if you're in policing; choose Kashikoi if you ship AI applications and need realistic pre-deployment testing.
GeneratedFrequently asked questions
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