Side by Side
At a Glance
12 dimensions of comparison between Naftiko and StackOne — same row, different layer of the stack. Scan top-to-bottom to see where each product makes a different choice on the same axis.
Dimension
Naftiko
StackOne
Category
Naftiko
Spec-driven integration platform
StackOne
AI agent integration platform — connector catalog + tool discovery + managed data plane
Primary primitive
Naftiko
Capability — declared in YAML; consumes + exposes
StackOne
Action — a provider-faithful operation, retrieved at request time
Where shaping happens
Naftiko
In a spec file, at authoring time, by a human who can be named
StackOne
In a retrieval index, at request time, by the Tool Discovery engine
Catalog breadth
Naftiko
Bring-your-own — author any integration, including internal and homegrown systems with no vendor connector
StackOne
490+ connectors, 30,000+ actions out of the box — substantially broader for public SaaS
Selection accuracy
Naftiko
Not applicable by design — the surface is the selection, made once at authoring time
StackOne
91.6% first-try on its self-published S1 Search Bench; Falcon engine claims 78–80% token savings
Data model philosophy
Naftiko
Task-shaped — the capability returns the fields the task declared
StackOne
Provider-faithful — deliberately preserves each provider's real schema, field names and capabilities
Open source posture
Naftiko
Apache 2.0 engine and linter; free community Fleet; paid Standard / Enterprise
StackOne
Proprietary. Starter free (1,000 credits/seat/mo), Team $600/mo, Enterprise custom
Deployment ownership
Naftiko
Self-hosted by default — one container per capability, every tier
StackOne
Hosted by default; self-hosting / on-premise VPC is an Enterprise add-on. US/EU standard regions; other regions a further add-on
Metering
Naftiko
No per-call meter — you run the engine
StackOne
Credit-based per call. Background and failed calls are not billed
Multi-protocol exposure
Naftiko
REST + MCP + Skills + A2A (roadmap) from one declaration
StackOne
Tool API + MCP + AI Agent Webhooks
Audit / observability
Naftiko
OpenTelemetry traces and per-capability metrics. Not a per-tool audit log — see our security posture
StackOne
Audit logs, SIEM export, project-level RBAC
Pre-execution reviewability
Naftiko
The complete surface is readable before a credential is attached
StackOne
The retrieved tool set is assembled per request; there is no artifact to approve beforehand
Common Ground
Where They Overlap
Both Naftiko and StackOne bet on the layer above per-vendor MCPs. Here are the 5 concrete places where those bets actually meet — same problem, sometimes the same shape, increasingly the same conversation.
1
Both agree the raw catalog is the wrong size for an agent
StackOne built Tool Discovery, the Falcon token-optimization engine, field-level customization and a Managed Data Sync layer on top of its own catalog. Naftiko authors a task-shaped surface instead. Two different answers, but the same starting diagnosis: handing an agent everything an API can do does not work.
2
Both reject the flattened unified API
StackOne argues that flattening every SaaS API into one generic schema is “good for developers writing deterministic code, bad for agents,” and preserves each provider's real data model. Naftiko agrees the generic schema is the wrong abstraction — and goes to the task shape rather than back to provider fidelity.
3
Both treat token cost as a real engineering problem
StackOne publishes token-savings figures for Falcon. Naftiko treats catalog cost as recurring per turn rather than paid once at setup. Neither of us thinks “use a bigger context window” is an answer.
4
Both now answer the ownership question
StackOne offers self-hosting and on-premise VPC as an Enterprise add-on, with audit logs and SIEM export. That neutralizes “run it in your own cloud” as a Naftiko differentiator, and we should stop implying otherwise.
5
Both take injection seriously
StackOne ships Defender Core as a free open-source injection scanner across every tier. Naftiko lints the spec at design time and in CI. Both refuse to treat prompt injection as somebody else's problem.
Where We Diverge
How Naftiko Is Different
The clearest single-sentence difference: StackOne makes a very large catalog navigable at request time; Naftiko makes a small surface reviewable before request time. Their benchmark answers ‘did the agent find the right tool among many?’ Ours is a different question — ‘was the set of tools correct, and who approved it?’
1
The benchmark measures a different question
Naftiko
There is no first-try accuracy figure because there is no retrieval step. The surface is the selection, and a reviewer approved it before the credential was attached.
StackOne
91.6% first-try on S1 Search Bench — a real number, honestly published, measuring selection accuracy among 30,000+ actions.
We are not disputing the number. We are pointing out that it measures finding the right tool among many, not whether the set was correct — which is what a reviewer signs.
2
Shaping as artifact vs shaping as retrieval layer
Naftiko
The tools, the fields, the upstream operations and the credential scope are in a file. Changes arrive as a diff someone reviews.
StackOne
The tool set is assembled by the discovery engine per request. It is auditable afterwards; it is not approvable beforehand.
This is the whole disagreement. Everything else on this page is table stakes one of us happens to satisfy.
3
Breadth vs internal systems of record
Naftiko
No catalog, and that is the point — a source capability is authored against your API by the team that owns it. Internal and homegrown systems are first-class.
StackOne
490+ connectors and 30,000+ actions. Substantially broader than anything Naftiko ships for public SaaS.
Buy StackOne for breadth across public SaaS. The systems of record that stall agent programs are frequently internal, where no vendor connector exists.
4
Fidelity vs task shape
Naftiko
A capability returns the handful of fields the task declared. An undeclared field is not shortened — it is absent.
StackOne
Provider-faithful actions preserve the upstream schema, then Falcon and field customization reduce what reaches the model.
Their own argument is that fidelity beats flattening. Ours is that fidelity is the wrong size for a task, which is why it needs a discovery and optimization stack on top.
5
The meter rewards a large surface
Naftiko
No per-call meter. Three upstream calls collapsed into one capability invocation costs the same as one — you run the engine.
StackOne
Credit-based per call. Background and failed calls are not billed, so the meter does not punish errors.
Be precise here: StackOne does not charge you for retries. The point is that three faithful calls cost three credits where one shaped invocation costs one.
Partnership Thesis
Where each one wins
StackOne and Naftiko are substitutes for part of the problem and complements for none of it, so the honest framing is a buying guide rather than a partnership pitch.
“If your problem is reaching many public SaaS systems quickly, StackOne is broader and will stay broader. If your problem is that a security reviewer will not let an agent touch a system of record until someone can say exactly what it may reach, that is the question a declared capability answers and a retrieval index cannot.”
Two First-Meeting Questions
Q1. When StackOne is the better buy
Breadth across public SaaS, a team that wants connectors rather than specs, and no control requiring prior approval of the agent's surface. Their catalog is real, their benchmark is honestly published, and self-hosting is available at the Enterprise tier.
Q2. When Naftiko is the better buy
The systems that matter are internal or homegrown; or a control requires the reachable surface to be approved before a credential is attached. That is a review-gate question, and it is the one place a runtime retrieval layer has no answer.
