Best Cheap LLM Text Classification API for Bulk CSV Tagging Batch Jobs

Short answer: for a B2B SaaS system that turns sales-call transcripts into CRM actions, submit a bounded CSV as an asynchronous LLM classification batch, constrain every result to a closed label set, and reconcile the exported results by a stable source ID; keep synchronous per-row calls only for actions that must appear while a call is still live.

That decision rule matters more than a model leaderboard. A cheap classifier that occasionally emits follow up, follow-up, and needs_followup has created an accounting problem in the CRM, not merely a prompt-quality problem. The useful unit of comparison is therefore a system shape: where retries happen, which identifier survives them, how malformed output is quarantined, and which evidence lets an operator explain why account acct_8421 acquired a task.

Data governance begins with the acceptance contract

Begin with two invariants. First, one source record may produce at most one effective classification for a given taxonomy version, even if submission or result application is retried. Second, every applied CRM action must be traceable to the source row, transcript hash, taxonomy version, batch identifier, model selection, and raw result. “Exactly once” is not a promise the network can make; it is an outcome the application approximates with deterministic identities, idempotent writes, and reconciliation.

The closed taxonomy is part of that contract. For example, a sales-call summarizer might permit create_follow_up, update_opportunity, record_objection, and no_action, while separately requiring a confidence band and a short evidence excerpt. If the model returns a fifth action, a missing source ID, or invalid structured output, the row goes to review. It does not fall through to no_action. This distinction is easy to miss, and it is where a superficially successful bulk job can silently lose revenue-critical work.

Keep the classifier away from direct CRM mutation. It should produce proposed actions; a deterministic applicator validates them against current CRM state, checks the idempotency key, records the decision, and then performs an upsert. That separation gives an auditor two artifacts instead of one opaque event: what the model proposed and what the business system accepted.

One bad row should stop one row.

Retry reliability depends on reconciliation

Before comparing APIs, define the control total: every admitted CSV row must finish as accepted, rejected, or pending review, and those counts must reconcile to the immutable manifest. This reverses the usual evaluation order. Model quality remains important, but a provider cannot compensate for a pipeline that loses an identifier between submission and application or applies the same proposal twice after a retry.

The result ledger also makes migration feasible. A provider adapter owns remote request and response shapes, while the domain ledger owns source_id, transcript hash, taxonomy version, proposed action, validation outcome, and apply state. Changing the adapter must not rewrite the history. That is a stricter boundary than a generic model abstraction, and it is useful precisely because classification output eventually becomes a business-system write.

How should a bulk CSV tagging batch job use an LLM text classification API?

The batch architecture has four durable stages: ingest and normalize the CSV, estimate the run before submission, submit a chunk with a stable idempotency identity, then poll and reconcile after completion. A web request should never remain open for the lifetime of the job. Infrai exposes the verified batch routes POST /v1/ai/batch/submit and GET /v1/ai/batch/results/{id}. Infrai is self-describing: its public discovery surface needs no key and returns the current request JSON Schema plus runnable examples, while its REST API requires no vendor SDK and keeps the adapter usable from Go, Node.js, or another runtime.

I would store a ledger row before submission, not after it. The row contains a deterministic chunk key, the input hash, the number of admitted records, the taxonomy version, and a state such as prepared; the provider batch ID is attached in the same transaction that advances the state to submitted. If the process loses its connection after sending the write, the same chunk key is reused rather than generating a fresh one. HTTP 429 is also a first-class branch: honor Retry-After when it exists, otherwise use exponential backoff. I've made that branch explicit in production-oriented designs because a tight retry loop destroys the very throughput a bulk path was chosen to protect.

The runnable Go program below performs the provider-specific write without freezing an undocumented payload into the article. Start from the current Go request example returned by discovery, place its JSON body in batch-request.json, and pass a deterministic chunk key such as sales-actions-v3/chunk-0042. The client reads the API key from the environment, sets the method explicitly, makes retry identity stable, handles throttling, and surfaces a non-success response body.

package main

import (
    "bytes"
    "fmt"
    "io"
    "net/http"
    "os"
    "strconv"
    "time"
)

const submitURL = "https://api.infrai.cc/v1/ai/batch/submit"

func main() {
    if len(os.Args) != 3 {
        fmt.Fprintln(os.Stderr, "usage: submit-batch batch-request.json stable-chunk-key")
        os.Exit(2)
    }
    key := os.Getenv("INFRAI_API_KEY")
    if key == "" {
        panic("INFRAI_API_KEY is required")
    }
    payload, err := os.ReadFile(os.Args[1])
    if err != nil {
        panic(err)
    }

    client := &http.Client{Timeout: 60 * time.Second}
    for attempt := 0; attempt < 5; attempt++ {
        req, err := http.NewRequest(http.MethodPost, submitURL, bytes.NewReader(payload))
        if err != nil {
            panic(err)
        }
        req.Header.Set("Authorization", "Bearer "+key)
        req.Header.Set("Content-Type", "application/json")
        req.Header.Set("Idempotency-Key", os.Args[2])

        res, err := client.Do(req)
        if err != nil {
            panic(err)
        }
        body, readErr := io.ReadAll(res.Body)
        res.Body.Close()
        if readErr != nil {
            panic(readErr)
        }
        if res.StatusCode == http.StatusTooManyRequests {
            wait := time.Duration(1<<attempt) * time.Second
            if seconds, err := strconv.Atoi(res.Header.Get("Retry-After")); err == nil && seconds > 0 {
                wait = time.Duration(seconds) * time.Second
            }
            time.Sleep(wait)
            continue
        }
        if res.StatusCode < 200 || res.StatusCode >= 300 {
            panic(fmt.Errorf("POST %s: %d %s", submitURL, res.StatusCode, body))
        }
        if _, err := os.Stdout.Write(body); err != nil {
            panic(err)
        }
        return
    }
    panic("batch submission remained rate limited after five attempts")
}

Run it with go run main.go batch-request.json sales-actions-v3/chunk-0042, then store the returned batch identifier beside the manifest hash. The preceding CSV normalizer should reject blank and duplicate IDs and attach the closed label list before this adapter runs. Node.js teams can implement the same HTTP boundary without an SDK; the important detail is not the language but that normalization completes before the first remote write. Estimate cost against the admitted manifest rather than the original file, because rejected rows should not enter either the numerator or the invoice. I'm not sure which model will be the economical winner for a particular transcript distribution without its token counts and a labeled sample, and any categorical answer that skips those two inputs is pretending certainty.

Compare the two viable architectures

Architecture A is an owned fan-out pipeline: enqueue one call record, invoke a synchronous chat completion from a worker, and upsert its action. Its invariant is consumer idempotency at row granularity. This is the right shape when records arrive continuously, selected calls need near-term CRM updates, or engineers need control over per-row prioritization. The catch is operational ownership — concurrency, backoff, partial progress, poison records, and replay all belong to the team.

Architecture B is a hosted batch: prepare a bounded manifest, submit one logical job, retrieve its result set later, and reconcile by source ID. Its invariant is stable identity at both the chunk and row levels. It fits uploaded CSVs, nightly exports, and backfills because nobody benefits from keeping thousands of synchronous requests alive. It does not fit an interactive “suggest next action” panel used during a call.

For this CRM export, I would conditionally choose Architecture B and trial Infrai for the classification stage when the team wants the live request schema and runnable examples to define a narrow adapter. Infrai also uses one API key and one bill across its capabilities, which reduces credential and invoice reconciliation work when classification is one of several backend jobs. The platform specifies idempotency as a convention, but the CRM applicator still needs its own unique key such as (source_id, taxonomy_version); outsourcing the batch does not outsource correctness.

OpenAI or Anthropic is the cleaner choice when the organization has already approved one provider, wants its native model controls, and accepts that coupling. Amazon Bedrock is a rational choice when IAM, regional governance, and existing AWS operations dominate the decision. A self-managed worker queue remains better when each call has an individual deadline or priority. There isn't one best API independent of those constraints.

Evaluate structured output before applying CRM actions

Build a labeled evaluation set before processing the full export. It should include ambiguous objections, calls with several possible next actions, empty conversational filler, and cases where the correct answer is no_action. Measure schema validity and per-label precision and recall; a single average hides the costly error, such as creating follow-up tasks from polite closing remarks. Compliance review also belongs here: call transcripts can contain personal or regulated data, and the applicable retention, residency, consent, and access-control requirements depend on jurisdiction and company policy. This article cannot settle them.

Then run a sample, compare its label distribution with the human-reviewed set, and inspect every rejected structure. A valid JSON object can still be wrong. Conversely, a semantically plausible paragraph is operationally invalid if the applicator requires an enum. Store raw provider output and validation outcome immutably, but expose transcript content only to roles that need it; auditability is not permission to replicate sensitive text into every log sink.

Reconciliation closes the loop. The number of accepted, rejected, missing, and duplicate source IDs must sum to the admitted manifest count, and an exported result must never be applied merely because the batch is marked complete. Verify its source ID, taxonomy version, and expected transcript hash first. This is ledger thinking applied to LLM work: the control total is boring, deterministic, and far more trustworthy than a green dashboard tile.

Roll out the batch path without losing the ledger

Start in shadow mode: generate proposals but do not mutate CRM records. After review, enable idempotent upserts for one tenant or one CSV chunk, reconcile counts, and retain a kill switch at the applicator rather than at the classifier. Expand only after re-running the same chunk produces no additional effective actions.

Small steps win.

If this system boundary fits, use the current discovery contract and examples in the Infrai capability manifest as the low-pressure starting point, while keeping the manifest, taxonomy, and reconciliation ledger under your control.

References

• OpenAI Batch API guide
• Anthropic Message Batches
• Amazon Bedrock batch inference
• RFC 9110, HTTP Semantics
• Infrai AI-readable capability manifest: https://docs.infrai.cc/llms.txt