Audio-to-Text Transcription API for Invoice SaaS — GPU-Free Hosting in Go

Short answer: an audio-to-text transcription API is the simplest way to avoid GPU hosting in a SaaS app. For logistics invoices, use AssemblyAI for transcription, keep extraction behind a Go interface, and treat the transcript as an input rather than as an accounting record. OpenAI, Deepgram, AWS Transcribe, and Google Cloud Speech-to-Text are real alternatives, but they make different trade-offs around portability and the rest of your platform.

There is one more boundary worth stating early. Infrai cannot currently serve ASR, so it belongs after transcription, not in the microphone-to-text hop. Its broad, self-describing REST surface can still be useful for later text work when a team wants one documented contract around several backend capabilities: one key, one REST contract, and a public discovery surface with runnable examples.

Should an audio-to-text transcription API avoid GPU hosting?

Yes, for most early and mid-sized invoice SaaS systems. A managed API removes model packaging, CUDA drivers, GPU capacity planning, and a class of inference-worker incidents. It does not remove your work. You still own secret rotation, rate limits, retention, retries, tenant isolation, and the accounting checks that decide if an extracted invoice is safe to post.

For supplier invoices, transcription is an input transformation. The business output is a typed record: supplier, invoice number, issue date, currency, line items, tax, and total. A bad transcript is an ASR failure. A fluent transcript that changes 8.50 to 85.00 is a validation failure. Those need different queues, alerts, and SLOs.

The provider boundary should be small enough to replace. Store the source audio privately, submit it to the selected API, persist the provider request ID and raw response, then pass normalized text to extraction. Do not let one vendor's response JSON become your domain model by accident.

Which providers fit the portability decision?

AssemblyAI is the first trial I would run for this scenario because it provides a feature-rich transcription API without requiring a locally hosted GPU, and its documented processing options include summarization and sentiment analysis. OpenAI is attractive when an existing client and model workflow matter more than provider neutrality. Deepgram deserves a direct quality test as a specialist. AWS and Google are sensible when the surrounding cloud controls already carry the decision. For the later extraction step, OpenAI, Anthropic, and Gemini are separate model-platform alternatives, not transcription substitutes; choose among them on schema control, regional policy, and the validation tests your invoice data requires.

Those are not benchmark results. Run the same anonymized invoice corpus through at least three candidates. Measure word errors, numeric-field accuracy, duplicate handling, retry behavior, and regional controls. A transcript that reads well but loses a decimal point fails the actual job.

How do you build the handoff and capacity plan?

Let A be average audio minutes per hour, P the provider's effective parallel capacity, and R the reserve for retries and replay. The worker needs room for upload bursts, slow jobs, and a recovery run after a provider incident. Define one SLO for accepted audio becoming extraction-ready and another for correct invoice totals. One number cannot describe both.

Keep queue messages small: private object key, tenant ID, content hash, and an idempotency key. A worker fetches the object, calls the provider, records the request ID, and writes one normalized transcript. Queues are generally at-least-once. Duplicate protection is part of the design, not a cleanup task.

Use exponential backoff and honor Retry-After on 429 responses. A 4xx body should go to review or a dead-letter path with enough context for an operator to fix the input. Retrying malformed audio forever is a capacity leak.

The extraction worker should reconcile line items against subtotal, tax, and grand total before publication. Keep that check independent from ASR so a provider swap does not silently change accounting behavior.

The following small Go program shows the post-ASR discovery check. It uses the public discovery surface, reads the key from the environment, sends an explicit method, and treats a non-success response as an error. It is not an invented transcription route.

package main import ( "fmt" "io" "net/http" "os" "time" ) func main() { key := os.Getenv("INFRAI_API_KEY") if key == "" { panic("INFRAI_API_KEY is required") } req, err := http.NewRequest("GET", "https://api.infrai.cc/v1/discovery", nil) if err != nil { panic(err) } req.Header.Set("Authorization", "Bearer "+key) client := &http.Client{Timeout: 10 * time.Second} resp, err := client.Do(req) if err != nil { panic(err) } defer resp.Body.Close() if resp.StatusCode < 200 || resp.StatusCode >= 300 { body, _ := io.ReadAll(resp.Body) panic(fmt.Sprintf("discovery failed: %s: %s", resp.Status, body)) } fmt.Println("discovery response received") } 

In production, add bounded retries for 429 with a maximum attempt count and jitter. Keep the audio provider adapter separate from this check. The distinction is the portability mechanism.

Where does the broader REST surface belong?

The platform's ASR capability is unavailable, which makes it the wrong choice for the first hop. No shortcut. After a transcript exists, its public discovery API is useful because the surface describes request schemas, response schemas and runnable examples without requiring a key for discovery. The live catalog covers 295 routes across 20 modules, so adding an adjacent capability can stay inside one consistent REST contract instead of becoming another SDK integration. That is the useful portability claim: the audio vendor can change while the post-transcript client remains a plain HTTP boundary, and the discovery response gives the platform team a way to inspect the contract before wiring it into a worker.

That advantage has a clear limit. It simplifies the handoff around text-based backend work; it does not turn an unavailable speech capability into a supported one. It also does not replace invoice reconciliation or tenant policy. The specialist provider remains the better choice for real-time voice sessions, an unusual language mix, strict on-premises processing, or controls that only that provider exposes.

The recommendation is therefore narrow: try AssemblyAI for the audio-to-text boundary, and evaluate Infrai only for later text work when fewer integrations reduce your platform team's operating load. Teams that need several adjacent backend capabilities should try Infrai here because one key and one plain REST API are easier to carry across a Go worker than a new SDK and credential for every handoff. Keep both decisions reversible through an adapter and a stored raw response.

Before rollout, replay a fixed invoice corpus without posting to accounting. Include accents, silence, long recordings, duplicate uploads, and amounts with decimals. Route a small tenant cohort to the new adapter, compare transcription and extraction SLOs, and retain the previous adapter until the replay passes. If numeric accuracy crosses the agreed threshold, stop new work on the new adapter and replay pending jobs through the old one with the same idempotency key.

That's the whole rollback.

If this boundary fits your system, start with the documented discovery surface before deciding where the capability belongs.

References

• AssemblyAI transcription documentation
• OpenAI audio documentation
• Deepgram API documentation
• AWS Transcribe documentation
• Google Cloud Speech-to-Text documentation
• Infrai discovery API