InferenceSaver vs Portkey
An AI gateway in front of your providers — compared with being the provider.
InferenceSaver and Portkey are not the same kind of product, and the comparison only makes sense once that is said plainly. Portkey is a layer you put in front of the provider accounts you already pay. We are the account you pay. Almost every difference below follows from that one structural fact.
Whether the inference itself gets cheaper, or only better governed.
Whether there is a second bill for the layer sitting in front of your providers.
Whether image, video, and audio bill from the same balance or from whoever you proxy to.
Why teams move their spend to InferenceSaver
Watching the bill climb is not a strategy. These are the three things that change on day one.
The rate itself is lower
You buy inference from us at a discount to provider list rates, rather than paying list rate and adding a gateway on top.
One account, not two
No provider accounts to hold, no keys to rotate in a second place, and no second vendor invoice for the layer in front.
Media on the same balance
Image, video, and audio generation bill from the credits that already cover your text calls.
The table below keeps the rows where Portkey is clearly ahead rather than quietly dropping them. Guardrails, prompt management, and self-hosting are theirs, and a comparison that hid that would not be worth reading.
Where Portkey is the stronger product, the row says so.
Where the two do the same thing, the row says that too.
Every row is checkable against either product's own documentation.
Side by side
The axes that actually change the decision, with no attempt to hide the ones where Portkey is strong.
Primary job
InferenceSaverCost controlPortkeyGateway controlWho you buy inference from
InferenceSaverOne balancePortkeyBring your ownWhat you pay for the layer
InferenceSaverNo add-onPortkeyAdd-onPer-request cost trace
InferenceSaverOriginal vs paidPortkeyList rateSpend attributed per member
InferenceSaverYesPortkeyVia metadataGuardrails and content checks
InferenceSaverNot offeredPortkeyCore featurePrompt management
InferenceSaverNot offeredPortkeyCore featureRetries, fallbacks, caching
InferenceSaverRouting onlyPortkeyConfigurableSelf-hosting the layer
InferenceSaverManagedPortkeyAvailableOpenAI-compatible API
InferenceSaverYesPortkeyYesWorkspace roles
InferenceSaverYesPortkeyYesImage generation
InferenceSaverIncludedPortkeyProxiedVideo generation
InferenceSaverIncludedPortkeyNot offeredAudio generation
InferenceSaverIncludedPortkeyNot offered
One-word verdicts are claims, not evidence. The three sections below take the rows that decide most migrations and show where they actually land: on the invoice, in a traced request, and on the bill for everything that is not text.
Cost — what changes about the number at the bottom of the invoice.
Observability — what a single request records once it has run.
Media — where image, video, and audio spend ends up.
Where the difference actually shows up
Three places the gap is visible in a workflow rather than a feature list.
The rate changes, not just the reporting
A gateway can tell you precisely what you spent at your provider's list rate. It cannot change that rate. Because the inference is bought here, every request carries the original provider rate and what you actually paid, side by side — and the second number is the lower one.
| Capability | InferenceSaver | Portkey |
|---|---|---|
| Discounted inference rate | Yes | Not offered |
| Cost of the layer itself | None | Gateway pricing |
| Original vs paid on each request | Side by side | List rate |
| Vendor relationships to hold | One | Provider plus gateway |
Traces that already know what you paid
Every call is traced with the model, latency, tokens, and cost, grouped by API key and by workspace member. Portkey's logging is deeper and more configurable than ours — what it cannot do is show a discounted rate, because it is not the one selling you the tokens.
| Capability | InferenceSaver | Portkey |
|---|---|---|
| Per-request trace | Yes | Yes |
| Attribution per member | Yes | Via metadata |
| Guardrails on request content | Not offered | Yes |
| Prompt versioning | Not offered | Yes |
Image, video and audio on the same balance
Generation runs in the same studio and draws from the same credits as text. Through a gateway, media calls are proxied to whichever provider account covers them, so the spend lands on that provider's invoice rather than the one you were trying to consolidate.
| Capability | InferenceSaver | Portkey |
|---|---|---|
| Image generation | Included | Proxied |
| Video generation | Included | Not offered |
| Audio generation | Included | Not offered |
| Shared credit balance | Yes | Not offered |
This is the row that matters most, because it is the one that is structural rather than a feature toggle. A gateway can make your calls more reliable; it cannot make your provider's list rate go down.
Who you are buying inference from, and at what rate.
Whether the layer in front adds a second line item.
What a shared team balance can attribute without extra plumbing.
What you actually pay
The billing differences that show up on an invoice, not in a feature list.
Who sells you inference
InferenceSaverOne balancePortkeyBring your ownRate you pay per token
InferenceSaverDiscountedPortkeyList rateCost of the layer
InferenceSaverNo add-onPortkeyAdd-onTeam billing
InferenceSaverPer memberPortkeyVirtual keysImage, video and audio
InferenceSaverSame balancePortkeyProvider billedAPI compatibility
InferenceSaverYesPortkeyYes
Pricing on our models
See how our pricing compares across the models you actually use. Every request shows the provider rate and your discounted rate side by side.
| Rank | Model | Capability | Context | Max Output | Input Tokens | Output Tokens | Savings | |
|---|---|---|---|---|---|---|---|---|
| #1 | Claude Fable 5 claude-fable-5 | Chat | 200K | 16.4K | $20.00$10.00per 1M tokens | $100.00$50.00per 1M tokens | up to 70% off | |
| #2 | Claude Fable 5 1 claude-fable-5-1 | Chat | 200K | 16.4K | $20.00$10.00per 1M tokens | $100.00$50.00per 1M tokens | up to 70% off | |
| #3 | Haiku 4.5.20251001 claude-haiku-4-5-20251001 | Chat | 200K | 8.2K | $2.00$1.00per 1M tokens | $10.00$5.00per 1M tokens | up to 70% off | |
| #4 | Opus 4.6 claude-opus-4-6 | Chat | 200K | 32K | $10.00$5.00per 1M tokens | $50.00$25.00per 1M tokens | up to 70% off | |
| #5 | Opus 4.7 claude-opus-4-7 | Chat | 200K | 32K | $10.00$5.00per 1M tokens | $50.00$25.00per 1M tokens | up to 70% off |
Every model above shows the provider list rate and what you actually pay — the savings column is where the difference is visible. Browse all 5 models to see the full catalogue with per-model details, endpoint compatibility, and the same side-by-side pricing.
Can't decide if we're the right choice?
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Questions teams ask
Can I use Portkey in front of you?
Yes, and some teams do. The gateway is OpenAI-compatible in both directions, so you can keep Portkey's guardrails and prompt management while buying the inference here. Nothing on either side asks for exclusivity.
Do I have to rewrite my integration?
No. Requests keep the OpenAI shape, so in most cases it is a base URL and an API key change. If you already call Portkey through an OpenAI-compatible client, the same client works here.
Do you offer guardrails or prompt versioning?
No. Those are Portkey's core product and we do not try to match them. If they are what you need, they are the better choice — this page exists to help you tell which problem you have.
How is spend attributed across a team?
Requests are traced per API key and per workspace member automatically, rather than through metadata you attach at call time, so a shared balance still shows who spent what, on which model, at which cost.
Get in touch with our team
Book a demo to discover how InferenceSaver can improve your AI operations, model costs, and production workflows.
“We finally have an AI stack that the product and engineering teams can use without switching between providers.”
Priya Nair
AI Operations Lead
Up to 2x usage throughput on the same spend
400ms median time to first response from frontier models
60-80% lower inference costs without sacrificing latency
We will map your current setup, recommend the right plan, and show a clear path to rollout.